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# Review of Hydrogen Sensors in Aerobic and Anaerobic Environments Coupled with Artificial Intelligence Tools

Jordan Herbeck-Tazibt, Mohand A Djeziri, Tomas Fiorido, Jean-Luc Seguin

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Jordan Herbeck-Tazibt, Mohand A Djeziri, Tomas Fiorido, Jean-Luc Seguin. Review of Hydrogen Sensors in Aerobic and Anaerobic Environments Coupled with Artificial Intelligence Tools. Sensors, 2025, 25, 10.3390/s25226936. [hal-05374741](https://hal.science/hal-05374741v1)

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Review

# Review of Hydrogen Sensors in Aerobic and Anaerobic Environments Coupled with Artificial Intelligence Tools

Jordan Herbeck-Tazibt * , Mohand A. Djeziri , Tomas Fiorido and Jean-Luc Seguin

Centre National de la Recherche Scientifique (CNRS) and Institut Matériaux Microélectronique Nanosciences de Provence (IM2NP), Aix-Marseille Université, Université de Toulon, 13397 Marseille, France; mohand.djeziri@im2np.fr (M.A.D.); tomas.fiorido@im2np.fr (T.F.); jean-luc.seguin@im2np.fr (J.-L.S.)

* Correspondence: jordan.herbeck-tazibt@im2np.fr

## Highlights

This review highlights the potential applications of hydrogen gas, with a focus on specific sensor types for aerobic and anaerobic applications.

### What are the main findings?

* This article provides a comprehensive and recent survey of intelligent and resistive hydrogen sensors according to type of application, in comparison with other sensor technologies, and identifies those coupled with artificial intelligence, detailing the methods and algorithms used, and identifying those in place within sensors in the literature.

* In the literature, very few studies deal jointly with aerobic and anaerobic hydrogen applications.

* Existing hydrogen sensor technologies in the literature are listed and compared according to potential hydrogen applications.

* This review shows the types of hydrogen sensors coupled with a software layer.

### What are the implications of the main findings?

* Each field of application is matched to the type of sensor used, with explicit detection ranges based on hydrogen-specific standards and regulations.

* In this paper, the use of Artificial Intelligence within aerobic and anaerobic hydrogen sensors is necessary for performance improvement.

* The main methods and algorithms used in hydrogen sensors, from the simplest to the most complex, are discussed and compared with sensors coupled with AI tools. Their performance is also assessed.

<figure><img src="image_3.png" /></figure>

### check for updates

Academic Editor: Jesus Lozano

Received: 23 September 2025

Revised: 31 October 2025

Accepted: 3 November 2025

Published: 13 November 2025

**Citation:** Herbeck-Tazibt, J.; Djeziri, M.A.; Fiorido, T.; Seguin, J.-L. Review of Hydrogen Sensors in Aerobic and Anaerobic Environments Coupled with Artificial Intelligence Tools. *Sensors* **2025**, *25*, 6936. https://doi.org/10.3390/s25226936

**Copyright:** © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Abstract

Hydrogen-based technologies are progressing in several areas, such as transportation and energy, especially regarding their use as a replacement for greenhouse gas-emitting fuels. However, hydrogen is known for its explosiveness and large-scale flammability; hence, there is a need to ensure it can be detected and measured without risk. Several types of hydrogen sensors are available on the market. Each sensor is suited to a specific environment and operating conditions. In recent years, Artificial Intelligence tools have been increasingly used to improve the design and performance of these sensors in terms of safety, reliability, sensitivity, speed, and selectivity. This paper provides a review of available hydrogen sensors, their fields of application, and the main directions explored by the scientific community for integrating Artificial Intelligence tools to improve their performance. A comparative analysis is presented based on criteria related to sensor
technologies, data processing tools, and target performance. This review highlights the
results achieved and the challenges that remain to be addressed in various application fields.

**Keywords:** hydrogen sensors; artificial intelligence; modeling; physics; materials; mathematical algorithm

# 1. Introduction

Hydrogen is a promising multi-purpose energy carrier with a wide range of applications. The International Energy Agency (IEA) consider hydrogen as a major technological, economic, and environmental challenge [1], and admits that its energy properties are environmentally friendly [2]. It is increasingly used in many fields, such as clean energy and sustainable mobility [3]; chemical and metallurgical industry; transportation systems (trains, airplanes, buses, cars) [1,4]; medicine, for its antioxidant properties; and environmental protection for water treatment and pollution control [5].

However, hydrogen is a colorless, odorless, and highly volatile gas, with a great capacity to leak from its containers. It is also highly flammable, with a flammability and explosivity range in air ranging from 4% to 75%, which requires reliable tools for hydrogen detection in real-time measurement, accurately and at an early stage [6].

To meet the demands of hydrogen gas control and monitoring, numerous sensors have been designed and tested according to the requirements and standards relating to hydrogen use and exposure [6]. They are based on operating principles specific to each application field [7]. In addition, Artificial Intelligence is used to improve sensor design [8] and performance, such as sensitivity, stability, selectivity, detection kinetics, concentration ranges, and robustness to interferents [9].

A significant amount of research is devoted to intelligent hydrogen sensors, such as the design of porous silicon hydrogen sensors [10], based on SnO₂ ([11–13]), Pd or alloys Pd-M (M = Ni, Ag, Au) [14], other semiconductor materials such as ZnO, WO₃, and In₂O₃ [15], or nanostructured composites [16]. These sensors are designed for specific infrastructures [17], stand-alone devices [18,19], or for a wide variety of applications [20]. However, to the best of our knowledge, there is no comprehensive review of hydrogen sensors in aerobic and anaerobic environments, including an analysis of AI's contribution to improving their performance. This review paper details the state-of-the-art of hydrogen sensors in aerobic and anaerobic environments, as well as the contributions of Artificial Intelligence in improving their performance in different application domains. This fills the gap by offering a structured mapping of existing technologies and recent advances.

This paper is organized as follows. Section 2 covers standards and applications using hydrogen sensors in anaerobic and aerobic environments. In Section 3, a study of hydrogen sensors coupled with artificial intelligence tools is carried out, with the measurements made, the performance obtained, and the methods and algorithms aimed at achieving target performance in sensor improvement. An analysis and discussion are provided in Section 3. A conclusion is presented in Section 4.

# 2. Standards and Applications Using Hydrogen Sensors

A wide variety of sensors based on different principles and materials are available, including optical, electrochemical, catalytic, electrical, thermal conductivity, resistive, metal film, and semiconductor sensors. There are two main areas of application for hydrogen sensors: the anaerobic environment (without oxygen), and the aerobic environment (presence
of oxygen and/or humidity). Each environment presents specific standards that impact the design and performance of the sensors.

## 2.1. Standards

Maximum exposure values and detection limits for hydrogen must be known for every application and for every use of a hydrogen sensor [21]. These values are based on industry standards and recommendations for the management of explosion risks and human health. According to the Occupational Safety and Health Administration (OSHA) [22], hydrogen parameters are essential for correctly monitoring systems and ensuring safety in installations. These values are presented in Table 1. They are supported by the French National Institute for Research and Safety (INRS) [23,24], and meet the following requirements:

* ISO/TR 15916 [25]: Safety in hydrogen systems.

* ATEX (EXPlosive ATmospheres) [26]: Standards for high-risk environments.

* European Directive SEVESO III [27]: Industrial risk management.

* OSHA and NIOSH [28]: Work environment regulations.

<table><caption>**Table 1.** Exposure limits and detection values recommended by OSHA [22] and INRS [23,24], justified by the Safety Data Sheets (SDS) [26,29].</caption><tr><td>Oxygen level in case of leak</td><td>&lt;19.5%</td></tr><tr><td>Lower Explosive Limit (LEL)</td><td>4%</td></tr><tr><td>Upper Explosive Limit (UEL)</td><td>75%</td></tr><tr><td>Average Exposure Limit (AEL)</td><td>8 h (1000 ppm)</td></tr><tr><td>Short Term Exposure Limit (STEL)</td><td>15 min (1000 ppm)</td></tr><tr><td>Sensor Detection Limit (SDL)</td><td>0.1 ppm–100% in volume (environment)<br>0.1 ppm–1% is often sufficient for</td></tr><tr><td>Recommended sensitivity range</td><td><ul><li>Detect leaks early.</li><li>Prevent the concentration from reaching the LEL (4% vol).</li></ul></td></tr></table>

## 2.2. Hydrogen Sensors

Numerous hydrogen sensors based on different materials are being studied [30]. They are grouped into families by type of technology, and each targets a specific application.

Catalytic sensors [31–33] are used for hydrogen detection. They work by oxidizing hydrogen on a catalytic surface (often platinum), which generates heat (an exothermic reaction). This heat increases the temperature of a sensitive element (such as a heating wire), which modifies its electrical resistance, hence the signal measurement. They are simple and robust, but require oxygen for operation and are highly sensitive to interferents. An example of a catalytic sensor is shown in Figure 1. It consists of a substrate, a heating element and a sensitive track, and the catalytic surface is based on Pt/TiO₂ [32].

The response of this sensor to hydrogen and a comparison with a reference sensor are illustrated in Figure 2.

Figure 2 shows that, under 3% H₂ exposure, the catalytic sensor has responses in excess of 30%, while the reference sensor has responses of the order of 1.7%. The Pt/TiO₂-based catalytic sensor is therefore more sensitive to hydrogen than a reference sensor on polyimide film [32].
<figure><img src="image_4.png" /><figcaption>**Figure 1.** Physical principle of a catalytic sensor. The geometric dimensions of the channels are 14 µm deep and 20–30 µm wide, which are aligned with the microheater ** and sensing electrode *.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Figure 2.** Response of a catalytic sensor as a function of H₂ concentration and comparison with a reference sensor on polyimide film.</figcaption></figure>

Electrochemical sensors [33–35] consist of a sensing electrode and a counter-electrode, separated by a thin layer of electrolyte. Their detection principle is based on an electrochemical reaction between hydrogen gas and the electrode, resulting in a measurable variation in current (amperometric sensors) or electrical potential (potentiometric sensors). Amperometric sensors generate an electric current proportional to the hydrogen concentration, while potentiometric sensors measure the voltage variation associated with the chemical reaction [36]. These sensors have good accuracy but use an electrolyte with a limited service life [7]. The operating principle of an amperometric electrochemical sensor is illustrated in Figure 3.

<figure><img src="image_6.png" /><figcaption>**Figure 3.** Schematic of two-electrode (**left**) and three-electrode (**right**) electrochemical sensors [34].</figcaption></figure>

An example of the response of this type of sensor at 4% H₂ is shown in Figure 4. Good measurement repeatability and baseline stability are observed.

<figure><img src="image_7.png" /><figcaption>**Figure 4.** Cyclic behavior of a Pt-based electrochemical sensor exposed to 4% H₂ [34].</figcaption></figure>
**Semiconductor metal oxide sensors (MOX)** are manufactured from materials such as tin dioxide (SnO₂), zinc oxide (ZnO), or titanium dioxide (TiO₂), usually integrated as thin layers or beads in a porous ceramic matrix around a heating coil [37–41]. The operation of these sensors requires a high temperature, typically between 200 °C and 400 °C, in order to activate chemical reactions on the oxide surface and achieve stable, measurable conductivity. The detection principle is based on the variation in the electrical conductivity of the semiconductor material in the presence of hydrogen. At high temperatures, oxygen adsorbed on the surface captures electrons, forming a depleted layer characterized by high resistance. When a hydrogen-containing atmosphere is present, it reacts with the adsorbed oxygen, releasing electrons into the material and causing a drop in electrical resistance. This variation is directly correlated to hydrogen concentration [16,37,40]. They can be influenced by temperature and humidity [12]. They are small, inexpensive to manufacture, and sensitive to hydrogen, and have a fast response time [37]. An example of a MOX sensor is shown in Figure 5 [40]. This is a ternary Si-Pd-Ni alloy sensor with a chemical treatment (BTESM) on a Al₂O₃ substrate. This sensor is sensitive to hydrogen and detects low concentrations from 1 ppm to 400 ppm, as shown in Figure 6 [40]. However, the sensor's stability is poor at baseline and with exposure. These performances can be improved by using a software layer, as proposed in [37].

<figure><img src="image_8.png" /><figcaption>**Figure 5.** Diagram of a Si-Pd-Ni MOX sensor.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Figure 6.** Exposure of MOX sensor to low concentrations of H₂.</figcaption></figure>

**Thermal conductivity sensors** [37] can detect large hydrogen concentrations of up to 100%, with a response and return time of just a few seconds. These sensors exploit the difference in thermal conductivity between hydrogen and other gases to measure its concentration. They generally use a heating probe whose temperature or resistance varies according to the heat transfer in the surrounding gas. Hydrogen has a different thermal conductivity to other gases. They work well at low humidity and are not poisoned by other gases, but they are less accurate at low hydrogen concentrations [38].

Metal film sensors [42,43] have a detection principle based on the measurement of variations in their physical properties through the reversible interaction of the layer with hydrogen. Palladium absorbs hydrogen to form a metal hydride, resulting in a change in physical properties and a variation in resistance. They are highly selective to hydrogen and sensitive, especially in Pd and Pt, but have long response times and significant aging [42–44]. A macro-sensor on a Si/SiO₂ substrate was produced, and a PdAu alloy layer was deposited by RF sputtering. This sensor is exposed to hydrogen
concentrations ranging from 0.3% to 3% and a comparison is made with a commercial
thermal conductivity sensor in Figure 7.

<figure><img src="image_10.png" /><figcaption>**Figure 7.** PdAu macrosensor and BlueVary analyzer (**left**); concentration response profile of a PdAu sensor and a commercial thermal conductivity sensor for stepwise exposure (**right**) [42,45].</figcaption></figure>

By comparative standards, both sensors offer optimum stability, reliability and sensi-
tivity to hydrogen. Nevertheless, the PdAu macro-sensor exhibits higher responses than
a commercial analyzer-type sensor, particularly for high hydrogen concentrations. Per-
formance is better with a macro-sensor. Furthermore, this device enables fast, real-time
measurements, and is relatively less expensive than an analyzer, where measurements are
spotty and sometimes time-consuming.

Spectroscopic [46,47] and fiber-optic sensors [48,49] where hydrogen-sensitive layers
change their optical properties in the presence of hydrogen, are promising for its detection.
They are highly sensitive, unaffected by electrical or magnetic interference, and suitable
for complex environments. Fibers are generally made of silica, but PdNi and PdAg alloys
are sometimes used [50]. However, they are sensitive to temperature variations and
interferents such as H<sub>2</sub>S and NH<sub>3</sub>, and are much more expensive. They are often useful for
monitoring hydrogen in environments where electrical safety is critical [50]. An example of
a transmission fiber-optic hydrogen sensor with multilayer Pd-Y alloy films is presented in
Figure 8. Its probe consists of a cell, a pair of collimators, and several films with palladium
alloy nanofilms. On either side of the gas cell, two collimators are fixed and aligned. A
series of Pd/Y alloy film substrates of equal thickness are parallel and fixed in the cell.
Light from a laser diode penetrates all the aligned films through the collimators. When
hydrogen gas is injected into the cell, all the thin films can interact simultaneously with the
light and the hydrogen [47].

In Figure 9, we can see that the noise is greater than the signal at a concentration of 0.05% H<sub>2</sub>. This shows that the sensor cannot recognize a concentration below 0.05% H<sub>2</sub>. However, it does provide responses at 0.5% H<sub>2</sub>. Spectral analysis and signal processing can contribute to sensor stability and drift [47].

<figure><img src="image_11.png" /><figcaption>**Figure 8.** Fiber optic hydrogen sensor.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>Figure 9. Sensor response at 0%, 0.05%, and 0.5% H₂.</figcaption></figure>

## 2.3. Anaerobic Applications

Since the famous hydrogen disaster linked to the Hindenburg Fire on 6 May 1937, most recent scientific work has focused on measuring hydrogen to monitor and prevent leaks of hydrogen or combustible gas into the air. Operating in an anaerobic environment was given very little attention. With recent technological advances and the desire to anchor the energy transition in the collective memory and in the world, the anaerobic environment has become increasingly important, particularly in the transport sector, with hydrogen-powered vehicles, trains, buses, aircraft, and even rockets and streetcars [7].

### 2.3.1. Power to Gas (P2G)

Power-to-Gas establishes a bridge between electricity and gas networks. The energy surplus is used to perform water electrolysis via electrolyzers (Alkaline Electrolyzer (AEL) & Proton Exchange Membrane (PEM*)) to produce green hydrogen. This hydrogen is then either directly injected into the natural gas network or used in a methanation reaction (Sabatier reaction) with CO₂ captured from industrial sites or recovered from biogas. The resulting synthetic gas can be stored for extended periods and used to offset electricity production deficits through fuel cells or gas turbines. An illustration of the Power-to-Gas principle and its applications is presented in Figure 10. Power-to-Gas technology thus helps reduce the need to balance the electricity grid [51].

When injecting hydrogen into the natural gas transmission network, it is essential to monitor and control its concentration in real-time; hence it is important to use a sensor that can measure the different levels of hydrogen.

This technology is based on a concentration range from 1 to 20% dihydrogen, with a pressure range of around 30 bars. The interferents for this type of application are carbon monoxide (CO) and dihydrogen sulfide (H₂S) [51].

The sensors used for this application must guarantee optimum hydrogen detection in anaerobic conditions, in a dry environment, below 100 °C, in a wide concentration range from 0 to 100%, and must be selective to hydrogen, with a response and return time of less than one minute [42,45,51,52].
<figure><img src="image_13.png" /><figcaption>**Figure 10.** Illustration of Power-to-Gas [52].</figcaption></figure>

**Catalytic sensors** are not suitable for measuring hydrogen in anaerobic environments and high-concentration zones, as they require the presence of oxygen for an oxidation reaction that leads to hydrogen detection [52].

**Electrochemical sensors** are used in anaerobic environments, but they only work in humid environments, as hydrogen detection relies on ambient humidity of between 20% and 60% (absent from gas lines), which will lead to a loss of resistance and subsequent sensor degradation [53].

**Thermal conductivity sensors** are highly sensitive to variations in several components with different thermal conductivities, resulting in baseline drift. They are therefore not selective to hydrogen [54].

**MOX sensors**, where the presence of oxygen is mandatory to enable load variation, are not suitable. This sensor technology cannot operate in an anaerobic environment, but can operate in an aerobic environment [55].

**Metal-film sensors** appear to be the most suitable for this type of application [42,52].

As far as Power to Gas is concerned, European discussions on maximum injection rates and consumer requirements in terms of sensitivity and detection speed are still ongoing [51], but the specifications shown in Table 2 are an illustration of the basic requirements for this type of application:

<table><caption>**Table 2.** Specification requirements [52].</caption><thead><tr><th>Specifications Requirements</th><th>Catalytic</th><th>Electrochemical</th><th>MOX</th><th>Thermal Conductivity</th><th>Metal Films</th></tr></thead><tbody><tr><td>Anaerobic</td><td>✗</td><td>✓</td><td>✗</td><td>✓</td><td>✓</td></tr><tr><td>Dry environment</td><td>✓</td><td>✗</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Temperature < 100 °C</td><td>✗</td><td>✓</td><td>✗</td><td>✓</td><td>✓</td></tr><tr><td>Measurement 0–100% H<sub>2</sub></td><td>✗</td><td>✓</td><td>✗</td><td>✓</td><td>✓</td></tr><tr><td>Selective to H<sub>2</sub></td><td>✗</td><td>✓</td><td>✗</td><td>✗</td><td>✓</td></tr><tr><td>T<sub>90</sub>/T<sub>10</sub> < 1 min</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

A Pd film-based sensor can repeatedly detect different hydrogen concentrations in aerobic or anaerobic environments at room temperature [42]. However, the repeated exposure of pure Pd to hydrogen causes problems: high measurement hysteresis, long response and feedback times, loss of detection properties, etc. [56]. Optimization of the film's physico-chemical properties is possible to overcome these problems. [42].

Other sources [48,57,58] assert the use of optical hydrogen sensors in which hydrogen interacts with an optical fiber coated with a sensitive material, modifying light transmission or reflection. Optical hydrogen sensors with reflexivity and transmission [48,58] and surface plasmon resonance [57] are reliable in terms of selectivity and sensitivity to hydrogen.
### 2.3.2. Monitoring Tanks on Future Vehicles

Monitoring the fuel tanks of future vehicles is one of the most important applications for hydrogen. To monitor hydrogen tanks in cars, planes, trains, buses, streetcars, and rockets, sensors must meet stringent requirements in terms of safety, accuracy, durability, and environmental compatibility [59,60]. Criteria such as sensitivity and accuracy must be respected in order to detect minute concentrations before the gas reaches the Lower Explosive Limit (LEL). Other criteria, such as resistance to repeated charging and discharging cycles, variations in temperature, pressure and humidity, and low energy consumption, are also essential [59]. Similarly, robustness against vibration and electromagnetic interference must be guaranteed [59]. Depending on the type of vehicle, specific technologies will be chosen to meet the specific constraints of the operating environment (high pressure for trains, low weight for aircraft, and low cost for cars) [60].

The recent review article by Lee, Seoung-Ki Lee [61] and their colleagues discusses the use of hydrogen sensors for mobility and transport infrastructure. For each type of sensor, response and return times, hydrogen concentration ranges, operating temperatures, sensitivity values, and detection materials were studied and listed for tank monitoring in future vehicles.

According to the Occupational Safety and Health Administration (OSHA) [22,28] and the National Institute for Research and Safety (INRS) [23,26,29], detection limits are different for each vehicle, and it is necessary to adapt the type of sensor to the range, from 10 ppm to 4% for aircraft, 100 ppm to 4% for trains, buses and cars, and 10 ppm to 100% for high-pressure tanks [62,63]. Table 3 uses recent data [61] to summarize these studies.

<table><caption>**Table 3.** Ranges according to vehicle type and benefits [61].</caption><thead><tr><td>Vehicle Types</td><td>Sensitivity</td><td>Advantages</td></tr></thead><tbody><tr><td>Planes</td><td>10 ppm to 4%</td><td>Priority given to rapid detection to avoid any risk in flight</td></tr><tr><td>Trains and buses</td><td>100 ppm to 4%</td><td>Robustness against vibrations and interference</td></tr><tr><td>Vehicles</td><td>100 ppm to 4%</td><td>Compact and low cost</td></tr><tr><td>High pressure tanks</td><td>10 ppm to 100%</td><td>Low leakage than hydrogen-saturated environments</td></tr></tbody></table>

Hydrogen sensors are available and adapted to the targeted concentrations depending on the type of vehicle. Detection ranges are specific and known for these sensors, as shown in Table 4.

<table><caption>**Table 4.** Detection ranges by sensor technology [60,61].</caption><thead><tr><td>Sensors</td><td>Measurement Ranges</td><td>Advantages</td></tr></thead><tbody><tr><td>Semiconductors (SnO<sub>2</sub>, ZnO)</td><td>1 ppm to 1%</td><td>Sensitive to low concentrations</td></tr><tr><td>Electrochemical (Pt, Pd, Au, Ni or Co)</td><td>10 ppm to 10%</td><td>Accurate and reliable for critical applications</td></tr><tr><td>A palladium</td><td>1 ppm to 100%</td><td>Very sensitive and specific to hydrogen</td></tr><tr><td>Optics (Pd)</td><td>10 ppm to 100%</td><td>Demanding environments (airplanes, trains)</td></tr></tbody></table>

Based on the works of [60,61,64], we can see the following.

**Semiconductor sensors** based on materials such as SnO₂ and ZnO, with a measurement range from 1 ppm to 1%, are sensitive to low concentrations. They are relatively inexpensive, compact, and lightweight, and can detect leaks close to vehicle fuel tanks. However, they are likely to interfere with other gases, such as CO and CH₄, and are sensitive to the influence of temperature (below 200 °C) and humidity (20–100% RH) [60,61].
**Electrochemical sensors** based on platinum, palladium, aluminum, nickel, or cobalt, operate over a wide range from 10 ppm to 10% and are precise and reliable for critical applications. They are used in detection systems and are sensitive and selective to hydrogen, guaranteeing low energy consumption, but have a limited lifetime and operate only under humidity ranging from 20 to 60%, making them suitable only for areas close to pipes and tank valves [60,61].

**Catalytic sensors** can also be used, but only in areas where air-hydrogen mixtures are likely to occur, such as during tank ventilation [64].

**Palladium sensors** are still the most sensitive and specific to hydrogen gas compared with other types of sensor [64].

**Optical sensors** are the preferred choice for aircraft and trains, while **palladium-based optical sensors** offer promising results in complex and critical environments [60,61,64].

According to the research carried out, the use of these sensors in anaerobic environments remains very poorly documented, with few concrete applications and few articles published on the subject, which shows that this is an area that still needs to be developed.

## 2.4. Aerobic Applications

Hydrogen gas is present in many potential applications in an aerobic environment, i.e., in the presence of oxygen and humidity, and taking into account the environment and interferents. Aerobic applications are more numerous and more in tune with current events and the energy transition, since hydrogen will be in contact with different environments and with ambient air containing 20% oxygen. In the hydrogen context, an aerobic environment could be used for leak detection and fuel cells, as well as electrolyzers and bioreactors [5,21].

### 2.4.1. Leak Detection

Hydrogen leaks can occur at various stages of the hydrogen value chain, including production, storage, transport, and use. Compliance with hydrogen-related standards and regulations is essential when using sensors to detect hydrogen leaks, be they primary, secondary, classified by level of danger, or tertiary [33]. Table 5 shows the nature of these leaks and their hydrogen concentration ranges.

<table><caption>**Table 5.** Type of leak and associated concentration ranges [33].</caption><thead><tr><td>Types of Leaks</td><td>Nature</td><td>Concentration Range H<sub>2</sub></td></tr></thead><tbody><tr><td>Primary</td><td>Trace-Background Leak</td><td>&lt;10 ppm</td></tr><tr><td>Early</td><td>Beginning of leak</td><td>10-100 ppm</td></tr><tr><td>Secondary</td><td>High risk</td><td>100 ppm-1%</td></tr><tr><td>Tertiary</td><td>Immediate danger</td><td>1-100%</td></tr></tbody></table>

The recent paper of Mohammed W. and Zekai Hong presents hydrogen sensors that can be used for leak detection, assessing performance by sensor type, including selectivity, sensitivity, response time, detection ranges, detection limits, market position, and lifetime [65].

To be effective, hydrogen sensors must at least guarantee detection in the range of 0% to 0.1% H₂, in order to detect early leaks and prevent the concentration from reaching the lower explosive limit, i.e., 4% [65]. The oxygen level in the event of a hydrogen leak must always be less than 19.5%, according to the Occupational Safety and Health Administration (OHSA) [22,28]. Values corresponding to current regulations can be found in Table 6. Environmental temperatures range from -30 °C to 80 °C, and relative humidity from 10% to 98%. Response times must be less than one second, and a service life of at least 10 years
must be guaranteed. Each sensor must ensure sensitivity and selectivity to hydrogen, notably by resisting potential interferents such as hydrocarbons [65]. It should be noted that the sensors, whatever the context, must withstand 1 bar of pressure [65].

<table><caption>**Table 6.** The values of OSHA/INRS regulations [22-24,26,28,29].</caption><thead><tr><td>Settings</td><td>Concentration (in % by Volume)</td></tr></thead><tbody><tr><td>Short-term exposure threshold</td><td>1% for 15 min</td></tr><tr><td>Average exposure threshold</td><td>1% over an 8 h period</td></tr><tr><td>Minimum flammability limit</td><td>4%</td></tr><tr><td>Alarm threshold in natural cavities</td><td>1% to 2%</td></tr><tr><td>Average concentration monitored for safety in the natural environment</td><td>0.1% to 2%</td></tr></tbody></table>

**Electrochemical sensors** (0.01% H₂) are the most widely used because they achieve better leak detection performance, with high sensitivity (low/high concentrations) and a fast response time (less than one minute), despite regular maintenance and sensitivity to interferents [66].

**Both catalytic and resistance-based sensors** have lower environmental sensitivity and a service life that can be less than ten years [33,65].

**Working function sensors** (sensors with a detection function and an integrated signal processing function (filtering, amplification, self-calibration, etc.)) and **optical sensors** are expensive, limiting their use in industry, despite their optimum performance [67].

**Thermal conductivity sensors**, despite their simplicity, robustness, and low cost, have limited sensitivity to low concentrations [33,67].

For **leak detection in natural cavity storage**, which is also an important issue in the energy transition and a worldwide concern, five types of sensors have produced satisfactory performance results: electrochemical sensors, thermal sensors, palladium-effect sensors, semiconductor sensors, and optical spectroscopy sensors [68,69].

All known detection limit values for this type of application, arranged by sensor technology, are listed in Table 7.

<table><caption>**Table 7.** Detection limit values for different technologies [68,69].</caption><thead><tr><td>Technology</td><td>Detection Limit Value (in % by Volume) of H<sub>2</sub></td></tr></thead><tbody><tr><td>Electrochemical</td><td>0.01%</td></tr><tr><td>Semiconductors</td><td>0.1%</td></tr><tr><td>Thermals</td><td>0.1% to 0.5%</td></tr><tr><td>Palladium effect</td><td>0.001% to 0.01%</td></tr><tr><td>Spectroscopiques</td><td>0.001% to 0.01%</td></tr></tbody></table>

In the field of leak detection, a number of hypotheses can be made regarding the use of sensors, depending on the type of leak, the associated ranges, and detection limits. Table 8 shows the sensors recommended for a specific type of leak.

<table><caption>**Table 8.** Recommended sensors depending on the nature of the leak.</caption><thead><tr><td>Nature of the Leak</td><td>Recommended Sensors</td><td>Advantages</td></tr></thead><tbody><tr><td>Slow leak at very low H<sub>2</sub> concentration</td><td>Electrochemical or MOX sensor</td><td>High sensitivity (ppm); low cost; use for environmental monitoring</td></tr></tbody></table>
<table><caption>**Table 8. Cont.**</caption><thead><tr><td>Nature of the Leak</td><td>Recommended Sensors</td><td>Advantages</td></tr></thead><tbody><tr><td>Rapid leak at moderate H₂ concentration</td><td>Catalytic sensor or thermal sensor</td><td>Detection around 4% H₂-Fast response</td></tr><tr><td>Continuous leak in controlled atmosphere</td><td>Optical sensor or Pd-based metal film sensor</td><td>Stable; reliable; little sensitive to external interference</td></tr><tr><td>Significant leak with high risk of explosion (ATEX zones)</td><td>Catalytic sensor</td><td>Robust and suitable for complex environments</td></tr><tr><td>Leaks in environments with multiple gases</td><td>Fiber-optic sensor or infrared spectroscopy sensor</td><td>High selectivity to H₂; little cross-interference</td></tr></tbody></table>

### 2.4.2. Fuel Cells

The fuel cell uses a redox reaction to convert the chemical energy contained in hydrogen into electrical energy. The end products of this reaction are electricity, water, and heat, with no emissions of carbon dioxide or polluting particles [39]. According to the application, sensors must be able to detect hydrogen at atmospheric pressure up to an average of 30 bar, at high humidity ranging from 80 to 100%, and at a wide range of hydrogen concentrations up to 100% [70].

Electrochemical sensors (0.01% H₂) are highly sensitive in monitoring hydrogen, as are palladium sensors (0.001% to 0.01% H₂), which are particularly sensitive to low levels of hydrogen [70,71]. Solid-state sensors (0.1% H₂) are affordable and useful for monitoring large areas, while thermal sensors are used for the continuous monitoring of reactant levels in the cell and can detect an average of 0.1% to 0.5% hydrogen.

Spectroscopic sensors are often used in critical environments as they present with high accuracy (0.001% to 0.01% H₂) [70–72]. Electrochemical, palladium-based metal film, and MEMS sensors can be used to detect micro-leaks and for early detection, particularly with semiconductor sensors [70–72]. To ensure optimum monitoring in line with safety standards, infrared or catalytic sensors will be used, or even laser diode sensors for the emergency monitoring of high concentrations [70–72].

### 2.4.3. Bioreactors–Electrolyzers–White Hydrogen Research

**Hydrogen bioreactors** operate according to specific biological processes in which microorganisms generate hydrogen in controlled environments, such as dark fermentation, hydrogen photoproduction and bioelectrolysis [73–75]. The benefits are production from renewable resources (organic waste, biomass, water), waste reduction and CO₂-free hydrogen production as part of the circular economy and energy transition. The development of these large-scale systems could transform waste management and the production of green hydrogen. Sensors measure hydrogen concentration in the bioreactor in real time, guaranteeing precise monitoring of the biological process, system safety, and production control [75].

**Hydrogen electrolyzers** [76] are devices that produce hydrogen by breaking down water (H₂O) into hydrogen (H₂) and oxygen (O₂) using an electric current. This method is considered one of the cleanest for producing hydrogen, especially when powered generally by renewable energy. Sensors measure the concentration of hydrogen produced by the electrolysis of water, detect any leaks, and ensure that the plant is operating correctly [77].

Hydrogen sensors for bioreactors and electrolyzers [73–77] generally operate with detection thresholds from 0% to 100%, and alarm thresholds set at around 1–2% to prevent risks. Automated safety and control systems are essential to ensure their safe and efficient operation. Hydrogen concentration ranges from 10% to 60%, depending on the type of microorganism and substrates used [76,78]. However, the sensors must detect hydrogen concentrations of less than 1% for these applications, ensuring full resistance at a minimum of 30 bar pressure, under high humidity of around 80%, and also taking into account the
presence of interfering gases. Sensors operating solely under atmospheric pressure are not suited for these applications [74]. In bioreactors [73] and electrolyzers [76,78], the types of sensors used are chosen according to the target application and the environment.

The search for white dihydrogen (H₂ white) [59] is an approach aimed at recovering hydrogen naturally present in the ground. It is produced geologically by reactions such as serpentinization (ultrabasic water-rock interaction), radiolysis of water, or deep degassing. Its extraction without carbon-based chemical transformation and purification can have a reduced or non-zero carbon footprint, guaranteeing a low environmental impact. To detect and quantify natural H₂ leaks into the ground and air, several types of hydrogen sensors are used. These sensors can identify areas where hydrogen naturally escapes from the subsurface and detect low concentrations of H₂, in the ppm range. They provide data for choosing the best drilling locations, reducing exploration risks and costs, or prioritizing high-potential sites. The main objective remains to manage the risks associated with leakage. The sensors used for this type of application are similar in every respect to those used for the electrolyser application, since the search for white dihydrogen is based on electrolysis with renewable energies [59].

A summary of the detection limits in these areas is presented in Table 9 [59].

<table><caption>**Table 9.** Detection limit value for each technologies of sensors [59].</caption><thead><tr><td>Sensors Technologies</td><td>Detection Limit Value (in % by Volume)</td></tr></thead><tbody><tr><td>Electrochemical</td><td>0.01%</td></tr><tr><td>Semiconductors</td><td>0.1% to 0.5%</td></tr><tr><td>Thermals</td><td>0.1%</td></tr><tr><td>Palladium effect</td><td>0.001% to 0.01%</td></tr><tr><td>Spectroscopiques</td><td>0.001% to 0.01%</td></tr></tbody></table>

**Electrochemical sensors** with platinum electrodes are preferred because of their high sensitivity and selectivity to hydrogen, despite their limited service life and need for regular maintenance. They monitor hydrogen levels to adjust operating conditions, and operate under high humidity [43,79].

**Semiconductor sensors** are useful for monitoring the accumulation of hydrogen in the environment thanks to their sensitivity to low concentrations and their affordability. However, they are sensitive to interferents, humidity, and temperature influences, which can be detrimental, leading to loss of control in this type of application [40,44].

**Thermo-catalytic sensors** ensure reliability and robustness, but require a controlled environment where oxygen is not excessive and energy consumption is not too high [40,43,44,79].

**Palladium-based sensors** are still in the test phase [40,43,44,79]. They are compact, robust in delicate environments, specific, and selective of hydrogen, but are sensitive to interferents and surface contamination by impurities including sulfides and hydrocarbons.

Fiber-optic sensors are sensitive to temperature variations and interferents such as H₂S and NH₃. They are expensive, unaffected by electrical or magnetic interference, and suitable for complex environments [50]. They are often useful for monitoring hydrogen in environments where electrical safety is critical [43,79].

**Laser spectroscopy sensors** are recommended for electrolyzers only, and are used to monitor hydrogen leakage with precision or to adjust electrolysis yields. This technology provides high sensitivity, optimum accuracy, and long-range detection capabilities, but remains expensive [80].
#### 2.4.4. Chemical and Metallurgical Process Controls

Chemical processes transform reagents into useful molecules, while metallurgical processes transform ores into metals or alloys. They cover synthesis, smelting, extraction, purification, alloying, formation, and heat treatment, with multiple applications in industry, energy, and the environment [81]. Chemical and metallurgical process controls often involve monitoring hydrogen ($H_2$) for reasons of safety, quality, or understanding chemical mechanisms [81].

The sensors used for this kind of application depend on the process; the ones that can be used are as follows [81–83]:

**Electrochemical sensors** provide general monitoring for safety and optimization (0.1% to 0.5% $H_2$) [82].

**Semiconductor sensors** are effective for fast, continuous detection (0.5% to 1% $H_2$), while **thermal sensors** are required for critical monitoring and dynamic environments (0.5% to 2% $H_2$) [83].

**Spectroscopic sensors** are currently being developed, targeting an accuracy of (0.001% to 0.01% $H_2$) over long distances, which is useful for monitoring low concentrations [46].

**Palladium-effect and spectroscopic sensors** [81–83] are often used for low-level detection in specific processes. Hydrogen concentration ranges vary according to the process, but sensors must guarantee functional detection under pressure (approx. 30 bar) and relative humidity ranging from 20 to 80%.

#### 2.4.5. Wastewater Treatment

Hydrogen wastewater treatment is an innovative technology for purifying and recovering hydrogen from wastewater. The process uses chemical transformation technologies to extract, treat, and use the hydrogen generated as an energy resource or as a carrier in various industrial systems. The principle of wastewater treatment with hydrogen is based on the decomposition or conversion of organic compounds contained in wastewater to generate hydrogen [72].

In this field of application, the sensors must detect low concentration ranges from 0 to 0.01% dihydrogen, under atmospheric pressure and a relative humidity of up to 80% [72]. Digestion requires sensors to detect ranges from 10 to 100 ppm, and standards for biological treatment systems require a minimum of 0.5 ppm to 5 ppm. For effluents after treatment, the range is between 0.1 and 10 ppm [72].

**Electrochemical sensors** are used to accurately measure hydrogen concentration in wastewater in real time (approx. 0.01 to 0.5 ppm) [75].

**Thermal sensors** are useful for monitoring hydrogen production by bacteria in biological treatment systems (approx. 0.1 ppm) [75].

**Semiconductor sensors** are used to monitor hydrogen generated in digestion or sludge treatment processes (approx. 0.1 to 0.5 ppm) [84,85].

**Metal film sensors** are used for the lowest concentration values, as they are sensitive and well suited to monitoring bacterial or chemical hydrogen production in wastewater (approx. 0.01 ppm to 0.1 ppm) [84,85].

**Spectroscopic sensors**, which are also highly sensitive, can detect low levels of hydrogen in wastewater (around 0.01 ppm) [75,84,85].

#### 2.4.6. Biology-Bacteriology

Hydrogen biology and bacteriology focus on the use of micro-organisms, particularly bacteria, in the production and processing of hydrogen. Its use is based on the fact that certain bacteria and micro-organisms can generate hydrogen through their metabolic reactions [86]. These reactions exploit organic substrates or light energy (photosynthesis) to
convert water or organic molecules into dihydrogen. Sensors are crucial for this type of application in order to monitor hydrogen production, use bacteria as biocatalysts for hydrogen, and optimize biological processes for the creation of hydrogen with low environmental impact. Bacteria produce hydrogen through anaerobic fermentation, bacterial photosynthesis, the degradation of organic compounds, and the use of natural enzymes [86].

For this application, dihydrogen concentration ranges are very low, from 0 to 0.01% H₂. Sensors must be able to operate at atmospheric pressure and over a wide humidity range, up to 100%. Potential interferents are mainly CO and H₂S, but oxygen and carbon dioxide are interfering gases that can be used in several sub-applications [86]. The average values for the detection of biological fluids and bacterial production in aerobic environments are low, ranging from 0.1 to 1 ppm, while the average values for bacterial production in anaerobic environments aimed at bacterial metabolism range from 10 to 100 ppm [86].

**Electrochemical sensors** (approx. 0.01 to 1 ppm) are used to measure hydrogen concentrations in biological fluids such as plasma, serum, or exhaled air [6,87].

**Thermal sensors** are used because they are sensitive to low levels of hydrogen in biological environments (around 0.1 ppm) [6,87].

**Semiconductor sensors** can be used to monitor bacterial hydrogen production or its presence in various biological environments, with detection limits ranging from 0.1 to 0.5 ppm [6,87].

**Palladium sensors** are widely used in bacteriology to study hydrogen production by bacteria, as they detect around 0.01 ppm to 0.1 ppm [6,87].

**Spectroscopic sensors** are still being developed to detect concentrations below 0.01 ppm [6,87].

### 2.4.7. Healthcare–Pharmaceutical Industry

The use of hydrogen in healthcare is growing thanks to its antioxidant and anti-inflammatory capacities, and its potential therapeutic effects. Hydrogen is increasingly being studied for applications in the treatment of various pathologies, as well as in the monitoring of biomarkers in medical environments using specific sensors [63,88]. Its antioxidant and anti-inflammatory properties provide protection against cell damage, leading to preventive or curative therapies. Its many applications include the prevention of heart attacks and hypertension and neurological diseases (stroke, Alzheimer's), diabetes therapies, chemotherapy, and radiotherapy [63,88].

Sensors used for this type of application must detect low dihydrogen concentration ranges, ranging from 0 to 0.01%, and must ensure optimum detection under atmospheric pressure, taking into account a wide humidity range of up to 100% [63,88].

For hydrogen detection in blood, for example, concentrations range from 0.5 ppm to 1 ppm. These sensors' use in exhaled air and detection in biological fluids ranges from 0.1 ppm to 0.5 ppm [89].

**Electrochemical and metal film sensors** are used because they can detect around 0.01 ppm to 0.1 ppm of hydrogen in biological environments [88,90].

**Thermal sensors** sensitive to small variations in dihydrogen concentrations in biological environments detect about 0.1 ppm, while **semiconductor sensors** detect about 0.1 ppm to 0.5 ppm [91].

**Spectroscopic sensors** enable low detection rates of less than 0.01 ppm. They are also widely used in medical diagnostics, as they enable the real-time detection of hydrogen and detect low concentrations of dihydrogen [88,90,91].
## 2.5. Influence of Oxygen on Hydrogen Sensors

In an aerobic environment, two physical phenomena occur. The oxygen present in the atmosphere is adsorbed onto the surface of the sensor. It can occupy the active sites of the sensor, preventing hydrogen from binding properly, which reduces the sensor's sensitivity. Oxygen can also react with hydrogen to form water on the sensor surface [71,83]. This leads to a change in the electrical response and a disruption of hydrogen detection.

Platinum-based catalytic sensors, for example, detect hydrogen through a catalytic reaction. However, in the presence of oxygen, the reaction on the platinum surface produces water, which alters the sensor's response. This can lead to reduced sensitivity and a nonlinear response, as water formation becomes the primary detection mechanism [31,92]. Semiconductor-based sensors, such as $WO_3$, exhibit sensitivity to humidity. Water vapor can interact with the active sites on the surface, altering the conduction properties and affecting the sensor's response. The addition of complementary materials capable of interacting with water molecules can improve stability under humid conditions [93].

The studies by Y. Peng and J. Ye provide an example of the influence of oxygen and humidity on a reduced graphene oxide-based Pt-Pd sensor [94]. In Figure 11, a reduction in the response in the presence of oxygen, as well as the impact of humidity, can be observed. Figure 11a shows that the response amplitude under anaerobic conditions is 4000, whereas in an aerobic environment it does not exceed 1000. Figure 11b illustrates that for a concentration of 1000 ppm in air, the sensor's response changes depending on the humidity level.

<figure><img src="image_14.png" /><figcaption>**Figure 11.** Sensor responses to 1000 ppm of hydrogen for various types of gas mixtures (a). I-V characteristics of the sensor exposed to 1000 ppm hydrogen in air under two different relative humidity levels (b) [94]. The color curves red, blue and green, then black, respectively represent the time dependence of the response when exposed to 1000 ppm $H_2$ with different carrier gases including $N_2$, air and $N_2$ switched back to air as time proceeds for 35 min.</figcaption></figure>

The influence of humidity on hydrogen sensors is also illustrated in other research studies, such as resistive thin-film sensors combining palladium nanoparticles with metal oxides like NiO [95] or other metals such as Au [43], as well as in optical sensors using palladium alloys with metals like cobalt, which enhance the sensor's sensitivity and stability while reducing interference from water vapor and oxygen [96]. Pd/$SnO_2$-based sensors have already been developed in the literature, as they exhibit excellent selectivity for hydrogen, even in the presence of humidity, ensuring a rapid response and mechanical stability in high-humidity environments [97]. Another sensor based on epitaxial graphene provides increased sensitivity to humidity along with a certain degree of robustness [98].

## 2.6. Synthesis

Application fields, performance requirements, measurement ranges, and associated publications are summarized in Tables 10–12 show the range of sensors available for aerobic and anaerobic applications, and the detection ranges of each technology. It can be seen that
their applications in aerobic environments are more numerous than in anaerobic ones, that there are many different sensor technologies, and that their use depends on the context of the application.

<table><caption>**Table 10.** Aerobic and anaerobic applications: state of the art.</caption><thead><tr><th>Application</th><th>Range H<sub>2</sub> (%)</th><th>Pressure (Bar)</th><th>Humidity (%)</th><th>Interfering Gases</th><th>References</th></tr></thead><tbody><tr><td colspan="6" style="text-align: center; font-weight: bold;">Anaerobic</td></tr><tr><td>Power to Gas (P2G)</td><td>1–20</td><td>30</td><td></td><td>CO-H<sub>2</sub>S</td><td>[42,45,48,51–58]</td></tr><tr><td>Monitoring of tanks of future vehicles (Train/Bus/Cars)</td><td>0.01–4</td><td>300–700</td><td></td><td>CO-CO<sub>2</sub>-O<sub>2</sub>-S</td><td></td></tr><tr><td>Monitoring of H<sub>2</sub> aircraft tanks</td><td>0.001–4</td><td>350–700</td><td></td><td>CO-CO<sub>2</sub>-O<sub>2</sub>-S</td><td>[59–64]</td></tr><tr><td>High pressure tank monitoring</td><td>0.001–100</td><td>200–700</td><td></td><td>CO-CO<sub>2</sub>-O<sub>2</sub>-S</td><td></td></tr><tr><td colspan="6" style="text-align: center; font-weight: bold;">Aerobic</td></tr><tr><td>Leak detection (safety)</td><td>0–0.1</td><td>1</td><td>20–80</td><td>CO-H<sub>2</sub>S-H<sub>2</sub>O-O<sub>2</sub></td><td>[33,63,65,66,68,69]</td></tr><tr><td>Fuel cells</td><td>90–100</td><td>30</td><td>80–100</td><td>CO-H<sub>2</sub>S</td><td>[39,70–72]</td></tr><tr><td>Bioreactors-Electrolyzers</td><td>&lt;1</td><td>30</td><td>80</td><td>CO-H<sub>2</sub>S-S</td><td>[40,43,44,59,73–77,79,80]</td></tr><tr><td>White H<sub>2</sub> research or leak detection from storage in natural cavities</td><td>0–1</td><td>1</td><td>20–80</td><td>CO-H<sub>2</sub>S-H<sub>2</sub>O-O<sub>2</sub></td><td>[59,68,69]</td></tr><tr><td>Chemical and metallurgical process controls</td><td>Variable depending on the process</td><td>30</td><td>Possible</td><td>CO-H<sub>2</sub>S</td><td>[46,81–83]</td></tr><tr><td>Pharmaceuticals</td><td>0–0.1</td><td>1</td><td>20–80</td><td>CO-H<sub>2</sub>S-H<sub>2</sub>O-O<sub>2</sub></td><td>[88,90,91]</td></tr><tr><td>Wastewater treatment-Biology-Bacteriology</td><td>0–0.01</td><td>1</td><td>20–100</td><td>CO-H<sub>2</sub>S-H<sub>2</sub>O-O<sub>2</sub></td><td>[6,72,75,84–87]</td></tr><tr><td>Health</td><td>0–0.01</td><td>1</td><td>20–100</td><td>CO-H<sub>2</sub>S-H<sub>2</sub>O-O<sub>2</sub></td><td>[63,88]</td></tr></tbody></table>

Most studies, therefore, focus on aerobic environments, where the presence of oxygen promotes the absorption and redox mechanisms required for detection [99], and the economic and industrial needs are well established (hydrogen leaks, hydrogen stations, batteries, and laboratory research). Studies and applications in anaerobic environments are more limited, mainly due to the low surface reactivity in the absence of oxygen, the experimental complexity of measurements under controlled atmospheres, and the risks associated with handling hydrogen at high concentrations. These constraints reduce the reproducibility and sensitivity of conventional sensors based on metal oxides or catalytic processes [100]. Anaerobic environments are more specific (biogas, biological reactors, sealed fuel cells), generating few commercial needs and few scientific publications. Future research should focus on the development of oxygen-independent sensitive materials, such as PdAu alloys or graphene, the optimization of physical detection mechanisms (conductivity changes, diffusion) suitable for inert environments, and the design of microelectromechanical platforms ensuring safety in enclosed hydrogen-rich settings. These approaches would allow the application of hydrogen sensors to be extended to anaerobic environments. Future hydrogen applications in anaerobic settings include aerospace [101], ships [102], airplanes [103], and high-pressure tanks [104].

**Performance data for different hydrogen sensors:**

The sensors' performances are often interrelated, encompassing reliable detection at both low and high hydrogen concentrations, response and recovery times of less than one minute, and long operational lifetimes [14,37,52]. Research studies in which these performances are quantified are summarized in Table 13, along with the corresponding performance metrics and references.
<table><caption>**Table 11.** Sensor technology detection ranges for anaerobic applications.</caption><thead><tr><th rowspan="2">Applications</th><th colspan="12">Detection Ranges According to Sensor Types</th></tr><tr><th>Catalytic</th><th>Electrochemical</th><th>MOX semiconductor</th><th>Thermal conductivity</th><th>Metallic films</th><th>Transistor</th><th>Optical fibers</th><th>Ultrasound</th><th>Laser Spectroscopy</th><th>Diode Laser</th><th>Infrared</th><th>Hall effect</th><th>Thermals</th><th>Spectroscopiques</th><th>Other Optics (reflectivity, transmission)</th><th>Medical diagnosis</th></tr></thead><tbody><tr><td><strong>Anaerobic</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Power to Gas</td><td>0.1–100%</td><td>0.5 ppm–1%</td><td>1 ppm–4%</td><td>0.5–100%</td><td>1 ppm–4%</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>0.01–100%</td><td>NI</td><td>NI</td><td>NI</td><td>10 ppb–100%</td><td>10 ppb–100%</td><td>NI</td></tr><tr><td>Monitoring the tanks of future vehicles</td><td>NI</td><td>0.5 ppm–1%</td><td>1 ppm–4%</td><td>NI</td><td>10 ppb–10%</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>K-NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td></tr><tr><td>H<sub>2</sub> Aircraft Tank Monitoring</td><td>NI</td><td>0.5 ppm–1%</td><td>1 ppm–4%</td><td>NI</td><td>10 ppb–10%</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>0.01–100%</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td></tr><tr><td>Monitoring of H<sub>2</sub> train tanks</td><td>0.1–100%</td><td>0.5 ppm–1%</td><td>1 ppm–4%</td><td>0.5–100%</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td></tr><tr><td>High pressure tank monitoring</td><td>0.1–100%</td><td>0.5 ppm–1%</td><td>1 ppm–4%</td><td>0.5–100%</td><td>10 ppb–10%</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>0.01–100%</td><td>NI</td><td>NI</td><td>NI</td><td>10 ppb–100%</td><td>10 ppb–100%</td><td>NI</td></tr></tbody></table>

NI: Non-Identified: Values are not known or have not been specified; K-NI: Known but Non-Identified: These sensors are used but the values are not specified.

<table><caption>**Table 12.** Sensor technology detection ranges for aerobic applications.</caption><thead><tr><th rowspan="2">Applications</th><th colspan="12">Detection ranges according to sensor types</th></tr><tr><th>Catalytic</th><th>Electrochemical</th><th>MOX semiconductor</th><th>Thermal conductivity</th><th>Metallic films</th><th>Transistor</th><th>Spectroscopiques</th><th>Ultrasound</th><th>Laser Spectroscopy</th><th>Diode Laser</th><th>Infrared</th><th>Hall effect</th><th>Thermals</th><th>MEMS</th><th>Other sensors</th><th>Medical diagnosis</th></tr></thead><tbody><tr><td><strong>Aerobic</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Leak detection</td><td>0.1–100%</td><td>0.5 ppm–1%</td><td>1 ppm–4%</td><td>0.5–100%</td><td>10 ppb–10%</td><td>NI</td><td>10 ppb–10%</td><td>0.1–100%</td><td>0.01–100%</td><td>NI</td><td>100 ppm–100%</td><td>1 ppm–10%</td><td>0.5–100%</td><td>NI</td><td>NI</td><td>NI</td></tr><tr><td>Fuel cells</td><td>0.1–100%</td><td>0.5 ppm–1%</td><td>1 ppm–4%</td><td>NI</td><td>10 ppb–10%</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>0.01–100%</td><td>100 ppm–100%</td><td>NI</td><td>NI</td><td>10 ppb–100%</td><td>NI</td><td>NI</td></tr><tr><td>Bioreactors-Electrolyzers</td><td>0.1–100%</td><td>0.01%</td><td>0.1%</td><td>NI</td><td>10 ppm–0.01%</td><td>NI</td><td>10 ppm–0.01%</td><td>NI</td><td>0.01–100%</td><td>NI</td><td>NI</td><td>NI</td><td>0.1–0.5%</td><td>NI</td><td>NI</td><td>NI</td></tr></tbody></table>
<table><caption>**Table 12. Cont.**</caption><tr><td>White H<sub>2</sub> research-<br>Detection of leaks<br>from storage in<br>natural cavities</td><td>0.1-<br>100%</td><td>10 ppm-<br>0.01%</td><td>0.01-<br>0.1%</td><td>NI</td><td>1 ppm-<br>0.01%</td><td>NI</td><td>0.1 ppm-<br>0.01%</td><td>NI</td><td>0.01-<br>100%</td><td>NI</td><td>NI</td><td>NI</td><td>0.01-<br>0.1%</td><td>NI</td><td>NI</td><td>NI</td></tr><tr><td>Chemical and<br>metallurgical<br>process controls</td><td>NI</td><td>0.01%</td><td>0.1%</td><td>NI</td><td>10 ppm-<br>0.01%</td><td>NI</td><td>10 ppm-<br>0.01%</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>0.1-0.5%</td><td>NI</td><td>NI</td><td>NI</td></tr><tr><td>Pharmaceuticals</td><td>NI</td><td>0.01-<br>0.1 ppm</td><td>0.1 ppm</td><td>NI</td><td>0.01-<br>0.1 ppm</td><td>NI</td><td>0.01 ppm</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>0.05 ppm-<br>0.1 ppm</td><td>NI</td><td>NI</td><td>NI</td></tr><tr><td>Wastewater<br>treatment</td><td>NI</td><td>0.01-<br>0.5 ppm</td><td>0.1-<br>0.5 ppm</td><td>NI</td><td>0.01-<br>0.1 ppm</td><td>NI</td><td>0.01 ppm</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>Roughly<br>0.1 ppm</td><td>NI</td><td>NI</td><td>NI</td></tr><tr><td>Biology-<br>Bacteriology</td><td>NI</td><td>0.01-<br>0.1 ppm</td><td>0.1-<br>0.5 ppm</td><td>NI</td><td>0.01-<br>0.1 ppm</td><td>NI</td><td>&lt;0.01 ppm</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>Roughly<br>0.1 ppm</td><td>NI</td><td>NI</td><td>NI</td></tr><tr><td>Health</td><td>NI</td><td>0.01-<br>0.1 ppm</td><td>0.1-<br>0.5 ppm</td><td>NI</td><td>0.01-<br>0.1 ppm</td><td>NI</td><td>&lt;0.01 ppm</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>Roughly<br>0.1 ppm</td><td>NI</td><td>NI</td><td>&lt;0.1 ppm</td></tr></table>

NI: Non-Identified: Values are not known or have not been specified; K-NI: Known but Non-Identified: These sensors are used but the values are not specified.
<table><caption>**Table 13.** Key performances for different hydrogen sensor technologies.</caption><thead><tr><td>Sensor Type</td><td>Detection Limit</td><td>Response Time</td><td>Stability-Lifetime</td><td>References</td></tr></thead><tbody><tr><td>Pd-based metallic film</td><td>0.1–100 ppm</td><td>1–10 s</td><td>Maximum 1 year</td><td>[99]</td></tr><tr><td>Semiconductor</td><td>1–100 ppm</td><td>1 min</td><td>More than 1 year</td><td>[100]</td></tr><tr><td>Optical fiber</td><td>10 ppb–1 ppm</td><td>1–30 s</td><td>Maximum 2 years</td><td>[105,106]</td></tr><tr><td>Electrochemical</td><td>0.5–10 ppm</td><td>1 min</td><td>1–3 years</td><td>[107]</td></tr><tr><td>MEMS</td><td>100 ppb–1 ppm</td><td>Less than 1 s</td><td>Maximum 6 months</td><td>[108]</td></tr><tr><td>Graphene-based</td><td>1 ppb–10 ppm</td><td>Less than 5 s</td><td>Maximum 1 year</td><td>[109]</td></tr><tr><td>PdAu-based plasmonic</td><td>0.1–10 ppm</td><td>Less than 2 s</td><td>Maximum 1 year</td><td>[110]</td></tr></tbody></table>

Table 13 shows that the lowest detection limits are achieved with optical and graphene-based nanostructured sensors. The fastest response times are observed for MEMS and plasmonic sensors. Lifetimes vary greatly depending on the sensor technology and the environment (temperature and humidity conditions). Semiconductor sensors remain the most robust and cost-effective but require high operating temperatures. Palladium-based sensors suffer from limited lifetimes and degradation due to hydrogen absorption hysteresis. Optical fiber and nanostructured technologies, such as MEMS and graphene-based sensors, enable low detection limits (below ppm and even ppb), with response times of few seconds.

Response and recovery times are key performance indicators for hydrogen sensors, mainly for applications including security, leak detection, and real-time monitoring. Xing's work focuses on a sensor based on a homogeneous $SnO_2$ composite enriched with oxygen vacancies, designed to achieve rapid response and recovery. A well-engineered material reduces response times to approximately 1.1 s and recovery times to approximately 1.9 s for $In_2O_3-SnO_2$, representing reductions of 11% and 9.5%, respectively, compared to pure $SnO_2$ [111]. Chen's work shows that using an MXene/$SnO_2$ heterojunction sensor enabled a rapid response (value unspecified). A shorter response time is obtained compared with $SnO_2$ alone [112]. A PdO-modified heterojunction hydrogen sensor developed by Xing presents response and recovery times of approximately 0.8 s at 50 ppm hydrogen [113]. Yang developed an ultra-fast $H_2$ detection system comprising a vertical thermal conduction structure and a neural network algorithm [114]. The response time obtained is around 0.4 s.

### 3. Hydrogen Sensors Coupled with Artificial Intelligence

Artificial intelligence (AI) is used in hydrogen sensors to **improve performance** (accuracy, sensitivity, selectivity, response time, repeatability, and drift phenomena [44]) and **sensors design**. It also involves the fusion of data from several sensors, combining several data sources to assess the presence of hydrogen [115]. In the predictive maintenance domain, algorithms are used to analyze trends in sensor measurements [116] and to optimize energy consumption [117]. In the literature, a small number of papers deal with smart hydrogen sensors, and most of them concern resistive sensors. Nevertheless, this is a recent and fast-growing field with promising results.

#### 3.1. *Hydrogen Sensor Design Improvement*

To the best of our knowledge, there are few works that use a software layer to improve the design of hydrogen sensors from a materials point of view. This improvement can be performed using data-driven methods according to the schema shown in Figure 12. An example of improving the design of $SnO_2$-based hydrogen sensors is presented below [11].
<figure><img src="image_15.png" /><figcaption>**Figure 12.** Improving sensor design from a physical point of view through a software layer.</figcaption></figure>

Cheng Shi and al. [11] propose a technique for predicting SnO₂-based hydrogen gas detection performance as a function of the environment and a set of sensor structure parameters using artificial intelligence.

This technique can simulate the effects of temperature, hydrogen concentration, dopant material type, doping concentration, and other factors on material sensing performance. The idea is to use machine learning tools to build a model that describes the inherent relationship between the dihydrogen sensing response (HDR) of SnO₂-based sensors and its corresponding influential characteristics (nano-composite chemistry and operating conditions) as follows [118]:

$$ \text{HDR} = \text{ML}(\text{Dopant molecular mass and dosage, temperature, H}_2 \text{ concentration}) \quad (1) $$

The model is used as a simulator to analyze the influence of each parameter on the response. This avoids the need for experimentation, which requires considerable resources. The data used to train the model are summarized in Table 14.

<table><caption>**Table 14.** Study of the chemical properties of dopants for hydrogen detection.</caption><thead><tr><th rowspan="2">Sensor</th><th colspan="4">Inputs</th><th colspan="3">Outputs</th></tr><tr><th>Molecular Weight of the Dopant (g/mol)</th><th>Doping Dosage (mol%)</th><th>Temperature (°C)</th><th>Concentration H₂ (ppm)</th><th>HDR (-)</th><th>Number of Data</th><th>References</th></tr></thead><tbody><tr><td>SnO₂-Ag</td><td>107.87</td><td>0-5</td><td>150-480</td><td>1.07-2000</td><td>0.6-80.1</td><td>137</td><td>[119]</td></tr><tr><td>SnO₂-Co</td><td>58.93</td><td>0.196-1.195</td><td>260-400</td><td>100-35,000</td><td>1.1-291.4</td><td>51</td><td>[120]</td></tr><tr><td>SnO₂-Pd</td><td>106.42</td><td>0.5</td><td>175-225</td><td>50-1000</td><td>105.7-265.5</td><td>4</td><td>[56]</td></tr><tr><td>SnO₂-Pt</td><td>195.08</td><td>0.155-1.552</td><td>150-350</td><td>500-10,000</td><td>1.0-148.8</td><td>46</td><td>[121]</td></tr><tr><td>SnO₂-Ru</td><td>101.7</td><td>0.298-4.408</td><td>200-350</td><td>500-10,000</td><td>1.2-26.7</td><td>45</td><td>[118]</td></tr></tbody></table>

Several models have been implemented: Gene Expression Programming (GEP) [122]; Support Vector Least Squares Regression (LS-SVR) [123]; Multilayer Perceptron Neural Networks (MLPNN) [124]; and cascade (CNN) [125]. A comparative study of these models, illustrated by the spider graph in Figure 13, shows that MLPNN performs best (regression coefficient = 0.9882; mean absolute deviation = 2.74; and root mean square error = 8.05).

Trend analyses were carried out by studying the influence of dopant type on sensor HDR. The experimentally measured HDRs of the five SnO₂-based sensors studied and their corresponding MLPNN predictions under the same operating conditions (T = 300 °C; H₂ concentration = 1000 ppm) are plotted in Figure 13.

Firstly, acceptable agreement between the actual and predicted HDR values can be observed. Furthermore, the figure confirms that SnO₂-based nanocomposites decorated
with Pd and Ru metals exhibit the maximum and minimum sensitivity to hydrogen gas detection in air, respectively. This is an important finding for applications that require a suitable agent to decorate SnO₂ nanoparticles to improve hydrogen detection performance.

<figure><img src="image_16.png" /><figcaption>**Figure 13.** Spider plot (**left**). Evaluation of the hydrogen sensing response of different SnO₂-based composites from experimental and modeling perspectives (**right**).</figcaption></figure>

## 3.2. Improving the Performance of Hydrogen Sensors

Artificial intelligence is increasingly used to improve the performance of hydrogen sensors. Performance targets include accuracy, sensitivity, selectivity, response time, repeatability, and the phenomenon of drift caused by the aging of hydrogen sensors. Most of the research work found in the literature concerns resistive sensors that integrate the software part downstream of the raw sensor measurement, as illustrated in Figure 14. Depending on the target, data-driven methods are used to build classification and/or regression models.

<figure><img src="image_17.png" /><figcaption>**Figure 14.** Software layer to enhance sensor performance.</figcaption></figure>

The inputs/outputs of the algorithm depend on those of the sensor, and therefore on its technology. For all resistive sensors, the inputs are variations in resistance (ΔR), voltage (T), current (I), operating temperature (Tf), and heating temperature (Tc), as well as other environmental variables such as relative humidity (RH), flow rate (D), and pressure (P) when measured. The outputs of the system are mainly the presence or absence of hydrogen and the evolution of its concentration. The inputs and outputs of the software layer according to the type of sensor are presented in Table 15.

The software part is composed of two parts, as illustrated in Figure 15. The pre-processing step is used to extract relevant characteristics of the target, followed by two types of models: classification models to detect hydrogen in a mixture, and regression models to estimate and predict its concentration.
<table><caption>Table 15. Existing research on hydrogen sensors coupled with a software layer.</caption><thead><tr><th>Type of Hydrogen Sensors</th><th>Inputs</th><th>Outputs</th><th>Objectives</th><th>Performance Before</th><th>Methods and Algorithms Used</th><th>Performance After</th><th>References</th></tr></thead><tbody><tr><td>Pd-doped ZnO nanorods</td><td>Series of resistors with derivatives and peaks</td><td>H<sub>2</sub> concentration (5 ppm-500 ppm)-150-200 °C Binary classification marked by the presence or absence of H<sub>2</sub></td><td>Evaluating hydrogen selectivity</td><td>75% precision</td><td>Support Vector Machines (SVM)</td><td>96% precision</td><td>M. Kumar [126], J. Zhu [127]</td></tr><tr><td>ZnO decorated with noble metals</td><td>Resistors Current Voltage</td><td>H<sub>2</sub> concentration</td><td>Improving selectivity and sensitivity</td><td>Reliable detection of hydrogen; low selectivity among CO and H<sub>2</sub>S</td><td>Principal Component Analysis (PCA) + Random Forest (RF) Using a series of classifiers to evaluate results</td><td>Reduces classification errors by 40%</td><td>Yeong Min Kwon [128]</td></tr><tr><td>MoO<sub>3</sub></td><td>Resistance time series over 60 sliding seconds</td><td>H<sub>2</sub> concentration</td><td>Predicting H<sub>2</sub> concentration levels over time</td><td>RMSE around 15 ppm</td><td>LSTM</td><td>RMSE reduced to 3 ppm</td><td>[66,129,130] Other studies suggest an improvement from a materials point of view [131]</td></tr><tr><td>Pd-doped SnO<sub>2</sub></td><td>5 × 5 images where each pixel corresponds to the normalized resistance</td><td>H<sub>2</sub> concentration</td><td>Evaluate the selectivity of H<sub>2</sub> among CO, NH<sub>3</sub> and CH<sub>4</sub></td><td>55-65% correct identification of H<sub>2</sub></td><td>Classification CNN</td><td>97% identification of H<sub>2</sub></td><td>Y. Shubin [14] S. Diao [132]</td></tr><tr><td>Pd-doped SnO<sub>2</sub></td><td>2D raster images</td><td>Binary classification marked by the presence or absence of hydrogen</td><td>Diagnosis of sensor faults</td><td>66-75% in normal and noisy environments</td><td>Method CNN with RF</td><td>100% in normal and noisy environments</td><td>Y.Sun [133]</td></tr><tr><td>Au-doped SnO<sub>2</sub></td><td>Voltage Resistance Heating temperature</td><td>Low H<sub>2</sub> concentration (1-100 ppm)</td><td>Identify low hydrogen concentrations at high noise levels</td><td>65% detection</td><td>PCA + KNN</td><td>92% detection</td><td>[134,135] This method can be complemented by the work of Zeng [136]</td></tr><tr><td>WO<sub>3</sub> doped Cu, Fe and Pt</td><td>Voltage Current Resistance variations with different gases Slope Area</td><td>Gas classification and concentration</td><td>Evaluate the selectivity of hydrogen among CO or NH<sub>3</sub></td><td>60% precision</td><td>RF</td><td>93% precision</td><td>Yu [137] Wang [138]</td></tr><tr><td>Graphene/SnO<sub>2</sub></td><td>Resistance values versus time Voltage Current</td><td>H<sub>2</sub> concentration Reconstruction errors in order to identify the presence of an anomaly by assessing threshold exceedance</td><td>Detect potential leaks or drifts in normal sensor behavior</td><td>58% anomaly detection</td><td>Autoencoder method</td><td>98% fault detection</td><td>H. Mirzaei [139] J. Seo [140]</td></tr><tr><td>Catalytic</td><td>Temperature Resistance and response Voltage</td><td>H<sub>2</sub> concentration</td><td>Improving hydrogen selectivity and detection</td><td>NI</td><td>Linear regression Neural network</td><td>Constant error level for all gases</td><td>D. Spirjakin [141] Ivanov [31,142]</td></tr><tr><td>Catalytic based PdO/SnO<sub>2</sub></td><td>Relative humidity Resistance Voltage Current Heating temperature</td><td>H<sub>2</sub> concentration</td><td>Identify hydrogen concentrations in a very humid environment (80% RH) Evaluate hydrogen selectivity</td><td>65-70% identification but many errors</td><td>Classification: PCA + RF (feature extraction and selection) MLP to differentiate hydrogen concentrations</td><td>91% recognition of H<sub>2</sub> concentrations</td><td>Gao et al. [143]</td></tr></tbody></table>
<table><caption>Table 15. *Cont.*</caption><thead><tr><th>Type of Hydrogen Sensors</th><th>Inputs</th><th>Outputs</th><th>Objectives</th><th>Performance Before</th><th>Methods and Algorithms Used</th><th>Performance After</th><th>References</th></tr></thead><tbody><tr><td>Electrochemical-Thermal</td><td>Current Self-biasing voltage Operating temperature Relative humidity</td><td>H<sub>2</sub> concentration</td><td>Improve performance in terms of selectivity and drift</td><td>10-100 ppm Not very selective Time less than 30 s Service life 1 year</td><td>MLP to recognize hydrogen among potential interferents + SVM-RF for multi-gas classification</td><td>10 ppm 5-20 s H<sub>2</sub> detection with CO and CH<sub>4</sub> Service life up to 2 years</td><td>N. Franic, I. Pivac, F. Barbir [34,53,144,145]</td></tr><tr><td>Thermal conductivity (MEMS)</td><td>Resistor Heating temperature Pressure</td><td>H<sub>2</sub> concentration</td><td>Detection of hydrogen concentrations from 500 ppm to 3%</td><td>100 ppm for a 5 s response time</td><td>Polynomial regression calibration; MLP to compensate for temperature effect</td><td>100 ppm for a 5 s response time</td><td>H. Thanh [146] E. Gardner [147]</td></tr><tr><td>Fiber-optic hydrogen sensors with multilayer Pd-Y films</td><td>Wavelength of light source Absorption spectrum</td><td>H<sub>2</sub> concentration</td><td>Improve sensitivity and response speed to hydrogen</td><td>Wavelength drift introducing measurement errors and influencing detection</td><td>Fast Fourier transform (FFT) filtering methods; moving average algorithm for spectral data analysis</td><td>Optimal design enabling a 97% reduction in measurement error Lower wavelength drift error</td><td>Y. Liu [47]</td></tr><tr><td>Pd-based optical fibers-Spectroscopic</td><td>Reflected or transmitted wavelength (nm) Temperature Absorption or interference spectrum sometimes</td><td>H<sub>2</sub> concentration</td><td>Study drift and obtain a spatio-temporal profile when using a sensor network</td><td>NI</td><td>NI Sensors designed for industrial use by international manufacturers. Little information available on the internal software layer.</td><td>Measurements are repeatable and temperature effects are compensated from -10 to 80 °C for a detection threshold of 50 ppm</td><td>Zhang [148,149] [67,150]</td></tr><tr><td>Sensor for estimating and predicting hydrogen safety parameters</td><td>Heater voltage and current Heating temperature Inlet pressure and flow rate</td><td>Hydrogen outlet temperature Hydrogen outlet volumetric flow rate</td><td>Estimate hydrogen safety parameters such as pressure, temperature, and flow rates from the outlet of a Degussa sintering furnace.</td><td>NI</td><td>RNA-ANN Adaptative Neuro-Fuzzy Inference Systems (ANFIS) to solve a prediction problem</td><td>ANFIS >> ANN Average RMSE of 0.0387, 0.0283, 0.1301, and MAE of 0.0241, 0.0115, 0.0355 sequentially for temperature, pressure, and hydrogen flow rate</td><td>D. Sutarya [151]</td></tr><tr><td>Hydrogen detection using AI-based plasma discharge spectral analysis</td><td>Experimental bench with spectrometer Matrix 50 × 90 (images)</td><td>H<sub>2</sub> concentration and methane</td><td>Detecting hydrogen and methane in plasma and their respective concentrations</td><td>Quantitative detection but no estimation of hydrogen content in the presence of methane</td><td>Complex CNN model not appropriate Residual model with predictions made after training</td><td>Not very precise (improve in terms of materials or involve more in-depth image processing)</td><td>A. Salimian [19]</td></tr><tr><td>Analyzers-Chromatographs</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>Industry-standard software such as LabVIEW</td><td>NI</td><td>[150,152-154]</td></tr></tbody></table>

NI: Non-Identified: Values are not known or have not been specified.

<figure><img src="image_18.png" /><figcaption>Figure 15. Algorithmic part.</figcaption></figure>
To build models, datasets are required. Test benches, providing the possibility of imposing concentrations and recording measurements of other variables, are used to build labeled matrices. Figure 16 shows a schematic diagram of a test bench used to characterize resistive hydrogen sensors.

<figure><img src="image_19.png" /><figcaption>**Figure 16.** Hydrogen electrical characterization bench [42,45].</figcaption></figure>

This consists of gas sources for hydrogen, carrier gas, and interfering gases. Concentrations are regulated by a control system (mass flow) in a dilution chamber, then transferred to a gas enclosure. The sensor is connected to a Keithley 2450 type sourcemeter associated with a data acquisition and recording interface [45].

The existing research work on smart hydrogen sensors is summarized in Table 15, where sensor types, inputs, outputs, initial performance, methods and algorithms used, and final performance achieved are presented.

From Table 15, it can be seen that model inputs and outputs are defined according to the target and sensor type. Hydrogen concentration is common to all models, even when the target is to detect the presence of hydrogen.

Table 15 shows that metal oxide sensors based on various materials, such as ZnO [15], doped with Pd [155–158] or decorated with noble metals [128], the MoO₃ [129], the SnO₂ doped with Pd [14], the WO₃ [159] doped with Cu, Fe, and Pt [160–162], or graphene [16,163], are the most frequently used.

Model inputs are generally related: resistance, self-biasing voltage, current, heating temperature, operating temperature, relative humidity when measured [143], pressure [151], and wavelength [148,149]. They depend on the type of sensor. MOX sensors use current, resistance, voltage and heating temperature, and operating temperatures values as inputs in most applications [31,142]. The same applies to electrochemical, thermal, and catalytic sensors, with relative humidity added as one of the inputs to the models [53,144]. Fiber optic sensors have specific inputs such as the reflected or transmitted wavelength of the light source or absorption spectra [47]. Other [150,152–154] semiconductor sensors use raster images as inputs, with each pixel corresponding to the normalized resistance [14,132,133].

In the Model Learning section, most of the existing models were tested and implemented in order to assess which ones deliver the best performance. In light of this research, several recent models offer optimal performance after the addition of a software layer. A presentation of these models of different formalisms and a performance comparison is illustrated below:

* Support Vector Machines (SVM) [164] are based on separation plane calculations [165,166]. They can be used to classify hydrogen concentration levels or perform complex regressions [167]. SVMs are used for classification problems with a non-linear feature space in [168,169]. In [126,127], SVMs achieve 96% performance for hydrogen selectivity.
* Random Forests (RF) are also known as decision trees [170] or regression trees based on Bayes' theory. They are used in [137,138] to improve the selectivity and accuracy of hydrogen sensors, with an accuracy of over then 90%.

* SVMs and RFs are powerful tools for regression and classification. When classes overlap and are not physically separable (by hyperplanes), the RF algorithm is more appropriate as it is based on a probabilistic decision process [171,172]. If the classes are separable, even when the separating hyperplanes are strongly nonlinear, an SVM, thanks to the multitude of kernels and kernel combinations, manages to compute the separating hyperplanes [167,169,173].

* Linear and Logistic Regression [165,166,174] is used in [175] to model the relationship between measured resistance and hydrogen gas concentration and to predict hydrogen concentration levels down to around 10 ppm.

* Long Short Time Memory (LSTM) [175,176] is a memory neural network used to predict the evolution of hydrogen concentration levels over time. This is implemented in [66,130] for concentration predictions, with a sensitivity of around 3 ppm.

* Convolutional Neural Networks (CNNs) [165,166] combines feature extraction and model learning. Inputs are raster images where each pixel corresponds to the normalized resistance obtained by an optical sensor [14,132,133]; CNNs [174,177] are frequently used for classification [178] and lead to optimum performance (almost 100%) for the identification of hydrogen among CO, NH₃, or H₂S [14]. Although used for image analysis, CNNs can also be applied to time series and sensor signal data, since they can extract important features from resistance signals such as patterns or trends, and thus improve the accuracy of predictions [179].

* K-Nearest Neighbors (KNN) is a classification algorithm [174] based on a distance calculation [103] and is chosen to identify low hydrogen concentrations at high noise levels, with 91% accuracy [134–136].

* Multi-Layer Perceptron (MLP) [180,181] is also used to differentiate between low and high hydrogen concentrations in [143].

* Other types of artificial neural networks (RNN, RNA, and ANN) [182,183] are implemented to establish complex data relationships between resistance and gas concentrations [184]. In recent research work, they are used to estimate and predict physical parameters related to hydrogen safety [151].

* Polynomial regression is an identification model used in [185,186] for the calibration of hydrogen sensors.

* The autoencoder method, based on the statistical and variational properties of data [187], is used to detect hydrogen leaks in the presence of potential drifts in sensor response with 98% accuracy [139,140].

* Continuous Markov models are stochastic models that can be used to estimate hydrogen concentrations in the presence of uncertainties [188].

* Recent work in the IM2NP laboratory has enabled us to study the prediction of hydrogen content in natural gas pipelines supplying industrial machinery, in the context of power to gas technology, using geometric models associated with a PdAu alloy resistive hydrogen sensor, providing results for hydrogen detection, with promising performance in terms of selectivity, stability, and sensitivity [45].

All the models used guarantee good performance, with over 85% accuracy in the majority of these research projects.

However, for data preprocessing, the predominant method used is Principal Component Analysis (PCA) [189]. It was chosen because it is a highly efficient linear method for reducing data dimensionality and extracting useful features [143]. This is useful when the number of sensors or variables is high [189]. The works of Gao [143] show that PCA led to
a 91% improvement in the recognition of H₂ concentrations. PCA is used in [134,135] for hydrogen detection, with 92% accuracy.

Nevertheless, there are many other methods of feature extraction and selection in the literature [190,191], adapted to different types of sensor data, which can be used for improving the performances of hydrogen sensors.

Existing feature selection techniques can be divided into three main families: filter methods, Wrapper methods, and integrated methods [192]:

* Filtering methods: These methods are not based on the classification/regression model. They evaluate each feature independently, usually using statistical measures or correlation coefficients with the target variable. Consequently, model training and performance evaluation take place after the relevant features have been selected. They do not take into account the impact of each feature on the others, but simply evaluate them individually [193–195].

* Wrapper methods: These methods select features according to their impact on model performance. The specific machine learning algorithm is used to evaluate the effect of each feature or group of features on model performance. Features are retained or removed according to their ability to improve model performance. This technique is distinguished by its accuracy and its ability to take into account interactions between features [195,196].

* Integrated methods: These methods enable features to be selected directly during model training. Each model or algorithm selects the most important features according to the mechanism used during training (coefficient evaluation, data slicing, etc.). Unlike the Wrapper method, which involves training the model on selected features and then evaluating its performance, the Embedded technique stands out for its speed and high accuracy. However, it may not always achieve the same level of accuracy as the Wrapper method [195,197]. The CNN used in [14,19,132,133] is one of the methods that integrates the extraction/selection step through its convolutional layer.

For model learning, most of the data comes from labeled matrices, which are mostly built on experimental test benches. For example, the research by Ali Salimian [19] was carried out using an in-house laboratory test bench with an integrated spectrometer for hydrogen detection. The data was then recovered and used to train regression or classification models, depending on the type of problem. The same applies to most of the work in Table 15, such as that by Liu [47] or Sutarya [151]. Sometimes, for certain projects, data not integrated into the bench but retrieved from other in-house sensors were used for the software, as in the work of Diao [132] and Sun [133], with optical sensors being used to obtain images of resistance variations; from Gao [143] with a relative humidity sensor; and from Thanh [146] and Gardner [147], using a manometer-type sensor to measure pressure.

All this data provides the input for the algorithmic part [190,191], including data pre-processing [174,198,199] and a model learning section [165,166,200].

The performance of a model built with laboratory data may vary in a real environment, as real environmental conditions remain more complex than a controlled laboratory environment.

Other types of sensors have not been associated with a software layer. To our knowledge, there is no combined network of hydrogen sensors in different materials coupled with AI tools. Similarly, work on **thermal and electrochemical sensors** shows that there are currently no sensors of this type on the market that operate with a library of models [201,202]. For **optical and spectroscopic sensors**, coupling to AI is necessary to improve selectivity, precision, and spectral resolution, or to compensate for drift or environmental effects, such as temperature and humidity, and to detect micro-leaks [49,203,204]. **However, these types of sensors are aimed at industry and are designed by international manufacturers.** They
are equipped with a software layer, but little research has been carried out in this area for
fiber-optic and spectroscopic hydrogen sensors [67,149,205].

3.3. *Artificial Intelligence in Gas Detection*

Regarding the general case of gas sensors associated with AI, numerous studies have been reported. Zhang proposes a gas sensor network combining a carbon-based thin-film transistor and an LDA-LR (logistic regression) algorithm for the detection and identification of harmful indoor gases [206].

Claudia Gonzalez Viejo et al. employed two artificial neural network (ANN) to predict the peak areas of 17 different volatile aromatic compounds and the intensity of 10 sensory descriptors regarding the quality of beer [207]. Similarly, an electronic nose was designed to identify the baking level of cookies and classify them into specific categories using Convolutional Neural Networks (CNN) [208].

AI is also emerging in the field of healthcare; sensors contribute to the detection of key
breath gases such as acetone, ammonia, hydrogen sulfide, and nitric oxide for real-time
health monitoring. The need to improve the selectivity of respiratory sensors is addressed
through the integration of machine learning (ML) algorithms, including convolutional
neural networks (CNN) and support vector machines (SVM) [164].

Recent work within IM2NP-Lab [172,209] focuses on the detection and estimation of ethanol concentration in a disturbed environment using MOX sensors and the Random Forest (RF) method. The results demonstrate that the RF model achieves an accuracy of 94%.

4. Conclusions

Today, the development of hydrogen sensors is an attractive challenge in the context of energy transition. Indeed, a wide variety of applications require hydrogen detection or measurement systems due to safety, monitoring, and maintenance issues. The device must be low-cost, selective, and sensitive to hydrogen, reproducible over a wide humidity range, and able to operate at atmospheric pressure or under pressure under low- and high-hydrogen concentrations, and taking into account physical disturbances, the environment, the application, and the presence of interfering gases. For this reason, several types of sensors are being developed, which are increasingly associated with Artificial Intelligence.

This paper provides an overview of hydrogen applications in anaerobic and aerobic environments. It highlights the available sensor technologies, with detection ranges specific to each device. All the applications discussed in this review are associated with sensors of various types. This review assesses and discusses their compatibility with each application, while highlighting their measurement ranges.

The use of a software layer to improve the structure or performance of hydrogen sensors is a growing area of research. In this paper, an overview of the methods used and the performance achieved is presented.

Most existing models of classification and regression were studied, from the simplest,
such as polynomial models, to the most recent, such as convolutional neural networks and
autoencoders. However, the pre-processing part is limited to the use of PCA. In this paper,
a summary of existing feature selection methods is proposed to guide future research in
this field.

The software layer of the intelligent hydrogen sensors studied in this paper is often
built using labeled databases derived from test scenarios carried out in the laboratory in
a controlled environment. Few studies have looked at the evolution of the performance
obtained in the laboratory during tests in real environments.
**Supplementary Materials:** The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s25226936/s1, PRISMA 2020 Main Checklist. Reference [210] is cited in the Supplementary Materials.

**Author Contributions:** Conceptualization, J.H.-T., M.A.D., T.F. and J.-L.S.; methodology, J.H.-T., M.A.D., T.F. and J.-L.S.; software, J.H.-T.; validation, J.H.-T., M.A.D. and T.F.; formal analysis, J.H.-T., M.A.D. and T.F.; investigation, J.H.-T., M.A.D. and T.F.; resources, J.H.-T., M.A.D. and T.F.; data curation, J.H.-T., M.A.D. and T.F.; writing—original draft preparation, J.H.-T., M.A.D. and T.F.; writing—review and editing, J.H.-T., M.A.D., T.F. and J.-L.S.; visualization, J.H.-T., M.A.D. and T.F.; supervision, J.H.-T., M.A.D. and T.F.; project administration, J.H.-T., M.A.D. and T.F. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article. The PRISMA Checklist is provided in the Supplementary Materials.

**Acknowledgments:** This thesis was carried out at the IM2NP laboratory. This thesis is funded by Doctoral School 353 and Aix-Marseille University.

**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

**Abbreviations**

<table><tr><td>AI</td><td>Artificial Intelligence</td></tr><tr><td>IEA</td><td>International Energy Agency</td></tr><tr><td>OSHA</td><td>Occupational Safety and Health Administration</td></tr><tr><td>INRS</td><td>National Institute for Research and Security</td></tr><tr><td>NIOSH</td><td>National Institute for Occupational Safety and Health</td></tr><tr><td>LEL</td><td>Lower Explosive Limit</td></tr><tr><td>UEL</td><td>Upper Explosive Limit</td></tr><tr><td>AEL</td><td>Average Exposure Limit</td></tr><tr><td>STEL</td><td>Short Term Exposure Limit</td></tr><tr><td>SDL</td><td>Sensor Detection Limit</td></tr><tr><td>HDR</td><td>Hydrogen Detection Response</td></tr><tr><td>ML</td><td>Machine Learning</td></tr><tr><td>DL</td><td>Deep Learning</td></tr><tr><td>MM</td><td>Mathematical Modeling</td></tr><tr><td>GEP</td><td>Gene Expression Program</td></tr><tr><td>LS-SVR</td><td>Least Squares Support Vector Regression</td></tr><tr><td>MLPNN</td><td>Multilayer Perceptron Neural Networks</td></tr><tr><td>SVM</td><td>Support Vector Machine</td></tr><tr><td>PCA</td><td>Principal Component Analysis</td></tr><tr><td>CNN</td><td>Convolutional Neural Network</td></tr><tr><td>ANN</td><td>Artificial Neural Network</td></tr><tr><td>RNN</td><td>Recurrent Neural Network</td></tr><tr><td>MLP</td><td>Multi-Layer Perceptron</td></tr><tr><td>FFT</td><td>Fast Fourier Transform</td></tr><tr><td>RF</td><td>Random Forest</td></tr><tr><td>KNN</td><td>K-nearest neighbors</td></tr><tr><td>LSTM</td><td>Long Short-Term Memory</td></tr></table>

**References**

1. Hydrogen, IEA. Available online: https://www.iea.org/energy-system/low-emission-fuels/hydrogen (accessed on 10 April 2025).

2. International Energy Agency (IEA). World Energy Outlook 2023; International Energy Agency (IEA): Paris, France, 2023; 355p.
3. European Environment Agency's Home Page. Available online: https://www.eea.europa.eu/en (accessed on 10 April 2025).

4. Accueil | France Stratégie. Available online: https://www.strategie.gouv.fr/ (accessed on 10 April 2025).

5. Behrendt, F. The future of industrial hydrogen: Renewable sources and applications for the next 15 years. *Clean Energy* **2025**, *9*, 3–8. [CrossRef]

6. France Hydrogène. *Mémento de l'Hydrogène*; France Hydrogène: Paris, France, 2019.

7. Hübert, T.; Boon-Brett, L.; Black, G.; Banach, U. Hydrogen sensors—A review. *Sens. Actuators B Chem.* **2011**, *157*, 329–352. [CrossRef]

8. Patil, R.R.; Calay, R.K.; Mustafa, M.Y.; Thakur, S. Artificial Intelligence—Driven Innovations in Hydrogen Safety. *Hydrogen* **2024**, *5*, 312–326. [CrossRef]

9. Hu, W.; Wan, L.; Jian, Y.; Ren, C.; Jin, K.; Su, X.; Bai, X.; Haick, H.; Yao, M.; Wu, W. Electronic Noses: From Advanced Materials to Sensors Aided with Data Processing. *Adv. Mater. Technol.* **2019**, *4*, 1800488. [CrossRef]

10. Galstyan, V.E.; Martirosyan, K.S.; Aroutiounian, V.M.; Arakelyan, V.M.; Arakelyan, A.H.; Soukiassian, P.G. Investigations of hydrogen sensors made of porous silicon. *Thin Solid Film.* **2008**, *517*, 239–241. [CrossRef]

11. Shi, C.; Pei, W.; Jin, C.; Alizadeh, A.; Ghanbari, A. Prediction of the SnO₂-based sensor response for hydrogen detection by artificial intelligence techniques. *Int. J. Hydrog. Energy* **2023**, *48*, 19834–19845. [CrossRef]

12. Van, K.D.; Hieu, N.V.; Yang, T.C.-K.; Manh, T.L. Rapid Detection of Ultralow H₂S Concentration with on-chip Fabrication of SnO₂-based Gas Sensors by Direct Electrodeposition from Non-Aqueous Solvents. *J. Electrochem. Soc.* **2024**, *171*, 097506. [CrossRef]

13. Wang, C.; Li, J.; Luo, C.; Wang, X.; Yang, M.; Xiong, Z.; Gu, J.; Gong, Z.; Wei, Z.; Qian, F. SnO₂-based resistive hydrogen gas sensor: A comprehensive review from performance to function optimization. *Mater. Sci. Semicond. Process.* **2025**, *188*, 109209. [CrossRef]

14. Yan, S.; Cao, Y.; Su, Y.; Huang, B.; Chen, C.; Yu, X.; Xu, A.; Wu, T. Hydrogen Sensors Based on Pd-Based Materials: A Review. *Sensors* **2025**, *25*, 3402. [CrossRef]

15. Lee, J.-H.; Kim, J.-H.; Kim, J.-Y.; Mirzaei, A.; Kim, H.W.; Kim, S.S. ppb-Level Selective Hydrogen Gas Detection of Pd-Functionalized In₂O₃-Loaded ZnO Nanofiber Gas Sensors. *Sensors* **2019**, *19*, 4276. [CrossRef] [PubMed]

16. Ilnicka, A.; Lukaszewicz, J.P. Graphene-Based Hydrogen Gas Sensors: A Review. *Processes* **2020**, *8*, 633. [CrossRef]

17. Patel, P.; Garaniya, V.; Baalisampang, T.; Arzaghi, E.; Abbassi, R.; Salehi, F. A technical review on quantitative risk analysis for hydrogen infrastructure. *J. Loss Prev. Process Ind.* **2024**, *91*, 105403. [CrossRef]

18. Zhu, J.; Wen, H.; Fan, Y.; Yang, X.; Zhang, H.; Wu, W.; Zhou, Y.; Hu, H. Recent advances in gas and environmental sensing: From micro/nano to the era of self-powered and artificial intelligent (AI)-enabled device. *Microchem. J.* **2022**, *181*, 107833. [CrossRef]

19. Salimian, A. Quantitative hydrogen and methane gas sensing via implementing AI based spectral analysis of plasma discharge. *Int. J. Hydrogen Energy* **2024**, *50*, 1157–1173. [CrossRef]

20. Hu, D.; Gao, P.; Cheng, Z.; Shen, Y.; He, R.; Yi, F.; Lu, M.; Wang, J.; Liu, S. Comprehensive Review of Hydrogen Leakage in Relation to Fuel Cell Vehicles and Hydrogen Refueling Stations: Status, Challenges, and Future Prospects. *Energy Fuels* **2024**, *38*, 4803–4835. [CrossRef]

21. An, Y.; Loh, T.Y.; Bhaskara, P.S.; Sin, S.M.I. Review of safety regulations, codes, and standards (RCS) for hydrogen distribution and application. *Int. J. Green Energy* **2024**, *21*, 1757–1765. [CrossRef]

22. OSHA. Occupational Safety and Health Administration. Available online: https://www.osha.gov/ (accessed on 10 April 2025).

23. INRS. *Valeurs Limites D'exposition Pour la Prévention des Risques Chimique*; INRS: Paris, France, 2016.

24. INRS. *Détecteurs Portables de Gaz et de Vapeurs*; INRS: Paris, France, 2022.

25. *ISO/TR 15916:2015*; Basic Considerations for the Safety of Hydrogen Systems. ISO: Geneve, Switzerland, 2015.

26. INRS. *Hydrogène—Fiche Toxicologique*; INRS: Paris, France, 2021.

27. Ministères Aménagement du Territoire Transition Écologique. Risques Technologiques: La Directive SEVESO et la loi Risques. Available online: https://www.ecologie.gouv.fr/politiques-publiques/risques-technologiques-directive-seveso-loi-risques (accessed on 2 November 2025).

28. CDC. *National Institute for Occupational Safety and Health*; National Institute for Occupational Safety and Health (NIOSH): Washington, DC, USA, 2025. Available online: https://www.cdc.gov/niosh/index.html (accessed on 10 April 2025).

29. INRS. *Valeurs Limites D'exposition Professionnelle aux Agents Chimiques en France*; INRS: Paris, France, 2016.

30. Comment Fonctionnent les Capteurs des Détecteurs de gaz?—ANATECS. Available online: https://safetylife.fr/content/20-comment-fonctionnent-capteurs-detecteurs-gaz?srsltid=AfmBOopdv58L3uuFIawsRzbkqaNQYSDcodWa3EkpQ9KUSJe1sNeq-GL8 (accessed on 19 June 2025).

31. Ivanov, I.I.; Baranov, A.M.; Talipov, V.A.; Mironov, S.M.; Akbari, S.; Kolesnik, I.V.; Orlova, E.D.; Napolskii, K.S. Investigation of catalytic hydrogen sensors with platinum group catalysts. *Sens. Actuators B Chem.* **2021**, *346*, 130515. [CrossRef]

32. Panama, G.; Lee, H.-O.; Bae, J.; Lee, S.S. Hydrogen Sensor with a Thick Catalyst Layer Anchored on Polyimide Film. *Adv. Mater. Technol.* **2024**, *9*, 2400445. [CrossRef]
33. Menon, S.K.; Kumar, A.; Mondal, S. Advancements in hydrogen gas leakage detection sensor technologies and safety measures. *Clean Energy* **2025**, *9*, 263–277. [CrossRef]

34. Korotcenkov, G.; Han, S.D.; Stetter, J.R. Review of Electrochemical Hydrogen Sensors. *Chem. Rev.* **2009**, *109*, 1402–1433. [CrossRef]

35. Li, W.; Sokolovskij, R.; Jiang, Y.; Wen, K.; Hu, Q.; Deng, C.; Wang, Q.; Yu, H. Hydrogen Detection Performance of a Pt-AlGaN/GaN HEMT Sensor at High Temperatures in Air Ambient. *J. Electrochem. Soc.* **2024**, *171*, 127513. [CrossRef]

36. Bas, S.Z.; Cummins, C.; Borah, D.; Ozmen, M.; Morris, M.A. Electrochemical Sensing of Hydrogen Peroxide Using Block Copolymer Templated Iron Oxide Nanopatterns. *Anal. Chem.* **2018**, *90*, 1122–1128. [CrossRef]

37. Buttner, W.J.; Post, M.B.; Burgess, R.; Rivkin, C. An overview of hydrogen safety sensors and requirements. *Int. J. Hydrogen Energy* **2011**, *36*, 2462–2470. [CrossRef]

38. Djeziri, M.; Benmoussa, S.; Bendahan, M.; Seguin, J.L. Review on data-driven approaches for improving the selectivity of MOXSensors. *Microsyst. Technol.* **2024**, *7*, 791–807. [CrossRef]

39. Bendimerad, M. Memoire Online—Réalisation d'un capteur de gaz MOX. Available online: https://www.memoireonline.com/11/10/4074/mREALISATION-DUN-CAPTEUR-DE-GAZ-MOX5.html (accessed on 3 October 2024).

40. Kafil, V.; Sreenan, B.; Hadj-Nacer, M.; Wang, Y.; Yoon, J.; Greiner, M.; Chu, P.; Wang, X.; Fadali, M.S.; Zhu, X. Review of noble metal and metal-oxide-semiconductor based chemiresistive hydrogen sensors. *Sens. Actuators Phys.* **2024**, *373*, 115440. [CrossRef]

41. Tang, Y.; Zhoa, Y.; Liu, H. Room-Temperature Semiconductor Gas Sensors: Challenges and Opportunities. *ACS Sens.* **2022**, *7*, 3582–3597. [CrossRef]

42. Occelli, C.; Fiorido, T.; Perrin-Pellegrino, C.; Seguin, J.-L. Sensors for anaerobic hydrogen measurement: A comparative study between a resistive PdAu based sensor and a commercial thermal conductivity sensor. *Int. J. Hydrogen Energy* **2023**, *48*, 17729–17741. [CrossRef]

43. Kim, Y.J.; Lee, S.; Choi, S.; Eom, T.H.; Cho, S.H.; Park, S.; Park, S.H.; Kim, J.Y.; Kim, J.; Nam, G.B.; et al. Highly Durable Chemoresistive Micropatterned PdAu Hydrogen Sensors: Performance and Mechanism. *ACS Sens.* **2024**, *9*, 5363–5373. [CrossRef] [PubMed]

44. Darmadi, I.; Khairunnisa, S.Z.; Tomeček, D.; Langhammer, C. Optimization of the Composition of PdAuCu Ternary Alloy Nanoparticles for Plasmonic Hydrogen Sensing. *ACS Appl. Nano Mater.* **2021**, *4*, 8716–8722. [CrossRef]

45. Djeziri, M.; Benmoussa, S.; Occelli, C.; Fiorido, T.; Seguin, J.-L.; Bendahan, M. Hydrogen rate prediction in natural-gas pipes supplying industrial machines in the frame of power-to-gas technology. *IFAC-Paper* **2024**, *58*, 479–483. [CrossRef]

46. Sowmya, N.; Ponnusamy, V. Development of Spectroscopic Sensor System for an IoT Application of Adulteration Identification on Milk Using Machine Learning. *IEEE Access* **2021**, *9*, 53979–53995. [CrossRef]

47. Liu, Y.; Li, Y. Signal analysis and processing method of transmission optical fiber hydrogen sensors with multi-layer Pd-Y alloy films. *Int. J. Hydrogen Energy* **2019**, *44*, 27151–27158. [CrossRef]

48. Nugroho, F.A.A.; Darmadi, I.; Zhdanov, V.P.; Langhammer, C. Universal Scaling and Design Rules of Hydrogen-Induced Optical Properties in Pd and Pd-Alloy Nanoparticles. *ACS Nano* **2018**, *12*, 9903–9912. [CrossRef]

49. Dai, J.; Zhu, L.; Wang, G.; Xiang, F.; Qin, Y.; Wang, M.; Yang, M. Optical Fiber Grating Hydrogen Sensors: A Review. *Sensors* **2017**, *17*, 577. [CrossRef]

50. Shen, C.; Xie, Z.; Huang, Z.; Yan, S.; Sui, W.; Zhou, J.; Wang, Z.; Han, W.; Zeng, X. Review of the Status and Prospects of Fiber Optic Hydrogen Sensing Technology. *Chemosensors* **2023**, *11*, 473. [CrossRef]

51. Jupiter 1000—Power-to-Gas—Le Projet. Available online: https://www.jupiter1000.eu/projet (accessed on 10 April 2025).

52. Occelli, C. Micro-Capteurs Pour la Mesure de L'hydrogène Injecté dans les Réseaux de gaz Nature, dans le Contexte de la Technologie "Power to Gas". Ph.D. Thesis, Aix-Marseille University, Marseille, France, 2023.

53. Korotcenkov, G.; Liu, Y.; Stetter, J.R.; Tang, Y.; Zeng, X. Electrochemical gas sensors: Fundamentals, fabrication and parameters. In *Chemical Sensors: Comprehensive Sensor Technologies. Vol. 5. Electrochemical and Optical Sensors*; Momentum Press: New York, NY, USA, 2011; Volume 5, pp. 1–89. Available online: https://www.researchgate.net/publication/288761637_Electrochemical_gas_sensors_Fundamentals_fabrication_and_parameters (accessed on 13 June 2025).

54. Wang, J.; Liu, Y.; Zhou, H.; Wang, Y.; Wu, M.; Huang, G.; Li, T. Thermal Conductivity Gas Sensor with Enhanced Flow-Rate Independence. *Sensors* **2022**, *22*, 1308. [CrossRef] [PubMed]

55. Aroutiounian, V. Metal oxide hydrogen, oxygen, and carbon monoxide sensors for hydrogen setups and cells. *Int. J. Hydrogen Energy* **2007**, *32*, 1145–1158. [CrossRef]

56. Zhang, S.; Yin, C.; Yang, L.; Zhang, Z.; Han, Z. Étude des propriétés de détection de H 2 d' un film mince multicouche mésoporeux pur et dopé au Pd de SnO2. *Sens. Actuators B Chem.* **2019**, *283*, 399–406. [CrossRef]

57. Westerwaal, R.J.; Rooijmans, J.S.A.; Leclercq, L.; Gheorghe, D.G.; Radeva, T.; Mooij, L.; Mak, T.; Polak, L.; Slaman, M.; Dam, B.; et al. Nanostructured Pd–Au based fiber optic sensors for probing hydrogen concentrations in gas mixtures. *Int. J. Hydrogen Energy* **2013**, *38*, 4201–4212. [CrossRef]
58. Darmadi, I.; Nugroho, F.A.A.; Kadkhodazadeh, S.; Wagner, J.B.; Langhammer, C. Rationally Designed PdAuCu Ternary Alloy Nanoparticles for Intrinsically Deactivation-Resistant Ultrafast Plasmonic Hydrogen Sensing. *ACS Sens.* **2019**, *4*, 1424–1432. [CrossRef]

59. ResearchGate. (PDF) Hydrogen Sensing and Detection. Available online: https://www.researchgate.net/publication/265541692_Hydrogen_Sensing_and_Detection (accessed on 5 August 2025).

60. Y a-t-il une Place Pour L'hydrogène dans la Transition Énergétique. Available online: https://www actu-environnement.com/media/pdf/news-22533-hydrogene-france-strategie.pdf (accessed on 11 April 2025).

61. Lee, J.-S.; An, J.W.; Bae, S.; Lee, S.-K. Review of Hydrogen Gas Sensors for Future Hydrogen Mobility Infrastructure. *Appl. Sci. Converg. Technol.* **2022**, *31*, 79–84. [CrossRef]

62. Amid, A.; Mignard, D.; Wilkinson, M. Seasonal storage of hydrogen in a depleted natural gas reservoir. *Int. J. Hydrogen Energy* **2016**, *41*, 5549–5558. [CrossRef]

63. Chi, G.; Xu, S.; Yu, D.; Wang, Z.; He, Z.; Wang, K.; Zhou, Q. A brief review of structural health monitoring based on flexible sensing technology for hydrogen storage tank. *Int. J. Hydrogen Energy* **2024**, *80*, 980–998. [CrossRef]

64. Li, X. Status and development of hydrogen preparation, storage and transportation. *Chin. Sci. Bull.* **2022**, *67*, 425–436. [CrossRef]

65. Qanbar, M.W.; Hong, Z. A Review of Hydrogen Leak Detection Regulations and Technologies. *Energies* **2024**, *17*, 4059. [CrossRef]

66. A Leakage Detection Method for Hydrogen-Blended Natural Gas Pipelines in Utility Tunnels Based on Multi-Task LSTM and CFD Simulation-Web of Science Core Collection. Available online: https://www.webofscience.com/wos/woscc/full-record/WOS:001375529400001 (accessed on 11 June 2025).

67. Javahiraly, N.; Perrotton, C.; Meyrueis, P.; Dam, B. Study of a fiber optic sensor for hydrogen leak detection. In *Photonic Applications for Aerospace, Commercial, and Harsh Environments IV, Proceedings of the SPIE, Baltimore, MD, USA, 29 April–1 May 2013*; Kazemi, A.A., Kress, B.C., Thibault, S., Eds.; Spie-Int Soc Optical Engineering: Bellingham, WA, USA, 2013; Volume 8720, p. 872004.

68. A Robust Organic Hydrogen Sensor for Distributed Monitoring Applications-Web of Science Core Collection. Available online: https://www.webofscience.com/wos/woscc/full-record/WOS:001438316100001 (accessed on 11 April 2025).

69. Collina, G.; Bucelli, M.; Paltrinieri, N. Multi-stage monitoring of hydrogen systems for improved maintenance approaches: An extensive review. *Int. J. Hydrogen Energy* **2025**, *105*, 458–480. [CrossRef]

70. Preti, D.; Squarcialupi, S.; Fachinetti, G. Aerobic, Copper-Mediated Oxidation of Alkaline Formaldehyde to Fuel-Cell Grade Hydrogen and Formate: Mechanism and Applications. *Angew. Chem. Int. Ed.* **2009**, *48*, 4763–4766. [CrossRef] [PubMed]

71. Monsalve, K.; Roger, M.; Gutierrez-Sanchez, C.; Ilbert, M.; Nitsche, S.; Byrne-Kodjabachian, D.; Marchi, V.; Lojou, E. Hydrogen bioelectrooxidation on gold nanoparticle-based electrodes modified by Aquifex aeolicus hydrogenase: Application to hydrogen/oxygen enzymatic biofuel cells. *Bioelectrochemistry* **2015**, *106*, 47. [CrossRef]

72. Sonawane, J.M.; Ezugwu, C.I.; Ghosh, P.C. Microbial Fuel Cell-Based Biological Oxygen Demand Sensors for Monitoring Wastewater: State-of-the-Art and Practical Applications. *ACS Sens.* **2020**, *5*, 2297–2316. [CrossRef]

73. Markov, S.A. Bioreactors for hydrogen production. In *Biohydrogen*; Zaborsky, O.R., Ed.; Plenum Press Div Plenum Publishing Corp: New York, NY, USA, 1998; pp. 383–390.

74. Markov, S.A. Hydrogen production in bioreactors: Current trends. In *Proceedings of the WHEC 2012 Conference Proceedings*—19th World Hydrogen Energy Conference, Toronto, ON, Canada, 3–7 June 2012; Elsevier Science Bv: Amsterdam, The Netherlands, 2012; Volume 29, pp. 394–400.

75. Qyyum, M.A.; Ihsanullah, I.; Ahmad, R.; Ismail, S.; Khan, A.; Nizami, A.-S.; Tawfik, A. Biohydrogen production from real industrial wastewater: Potential bioreactors, challenges in commercialization and future directions. *Int. J. Hydrogen Energy* **2022**, *47*, 37154–37170. [CrossRef]

76. Shandarr, R.Y.; Trudewind, C.A.; Zapp, P. Life cycle assessment of hydrogen production via electrolysis—A review. *J. Clean. Prod.* **2014**, *85*, 151–163. [CrossRef]

77. Losiewicz, B. Technology for Green Hydrogen Production: Desk Analysis. *Energies* **2024**, *17*, 4514. [CrossRef]

78. Akyuz, S.; Telli, E.; Farsak, M. Hydrogen generation electrolyzers: Paving the way for sustainable energy. *Int. J. Hydrogen Energy* **2024**, *81*, 1338–1362. [CrossRef]

79. Li, Q.; Wang, L.; Xiao, A.; Zhu, L.; Yang, Z. Hydrogen sensing towards palladium-based nanocomposites: A review. *Int. J. Hydrogen Energy* **2024**, *136*, 1282–1305. [CrossRef]

80. Fetter, K.L.; Munera, L.; Watts, M.A.; Pineda, D.I. Interband cascade laser absorption sensor for sensitive measurement of hydrogen chloride in smoke-laden gases using wavelength modulation spectroscopy. *Appl. Opt.* **2024**, *63*, 8517–8525. [CrossRef]

81. Chuayboon, S.; Abanades, S. An overview of solar decarbonization processes, reacting oxide materials, and thermochemical reactors for hydrogen and syngas production. *Int. J. Hydrogen Energy* **2020**, *45*, 25783–25810. [CrossRef]

82. Qiu, Z.; Yue, Q.; Yan, T.; Wang, Q.; Sun, J.; Yuan, Y.; Che, Z.; Wang, Y.; Du, T. Gas utilization optimization and exergy analysis of hydrogen metallurgical shaft furnace. *Energy* **2023**, *263*, 125847. [CrossRef]

83. Protasova, L.; Snijkers, F. Recent developments in oxygen carrier materials for hydrogen production via chemical looping processes. *Fuel* **2016**, *181*, 75–93. [CrossRef]
84. Akbarzadeh, R.; Adeniran, J.A.; Lototskyy, M.; Asadi, A. Simultaneous brewery wastewater treatment and hydrogen generation via hydrolysis using Mg waste scraps. *J. Clean. Prod.* **2020**, *276*, 123198. [CrossRef]

85. Salem, R.M.M.; Saraya, M.S.; Ali-Eldin, A.M.T. An Industrial Cloud-Based IoT System for Real-Time Monitoring and Controlling of Wastewater. *IEEE Access* **2022**, *10*, 6528–6540. [CrossRef]

86. Szabo, C. A timeline of hydrogen sulfide (H₂S) research: From environmental toxin to biological mediator. *Biochem. Pharmacol.* **2018**, *149*, 5–19. [CrossRef]

87. Mccurry, M.D.; D'Agostino, G.D.; Walsh, J.T.; Bisanz, J.E.; Zalosnik, I.; Dong, X.; Morris, D.J.; Korzenik, J.R.; Edlow, A.G.; Balskus, E.P.; et al. Gut bacteria convert glucocorticoids into progestins in the presence of hydrogen gas. *Cell* **2024**, *187*, 2952–2968. [CrossRef] [PubMed]

88. Gullino, A.; Grassini, S.; Gugliandolo, G.; Moulaee, K.; Donato, N.; Parvis, M.; Lombardo, L. Hydrogen chemoresistive sensor for the analysis of gut health. In Proceedings of the 2021 IEEE International Symposium on Medical Measurements and Applications (IEEE MeMeA 2021), Lausanne, Switzerland, 23–25 June 2021; IEEE: New York, NY, USA, 2021.

89. Barbu, A.; Jansson, L.; Sandberg, M.; Quach, M.; Palm, F. The use of hydrogen gas clearance for blood flow measurements in single endogenous and transplanted pancreatic islets. *Microvasc. Res.* **2015**, *97*, 124–129. [CrossRef] [PubMed]

90. Logue, F.N.; Ramaswamy, K.; Hersh, J.H. Investigation of illness associated with exposure to hydrogen sulfide among Pennsylvania school students. *J. Environ. Health* **2001**, *63*, 9–13.

91. Hutchins, K.M. Functional materials based on molecules with hydrogen-bonding ability: Applications to drug co-crystals and polymer complexes. *R. Soc. Open Sci.* **2018**, *5*, 180564. [CrossRef]

92. Yamaguchi, T.; Kiwa, T.; Tsukada, K.; Yokosawa, K. Oxygen interference mechanism of platinum-FET hydrogen gas sensor. *Sens. Actuators Phys.* **2007**, *136*, 244–248. [CrossRef]

93. Deng, Z.; Wu, Z.; Liu, X.; Chen, Z.; Sun, Y.; Dai, N.; Ge, M. Humidity-tolerant and highly sensitive gas sensor for hydrogen sulfide based on WO 3 nanocubes modified with CeO₂. *RSC Adv.* **2024**, *14*, 15039–15047. [CrossRef] [PubMed]

94. Peng, Y.; Ye, J.; Zheng, L.; Zou, K. The hydrogen sensing properties of Pt–Pd/reduced graphene oxide based sensor under different operating conditions. *RSC Adv.* **2016**, *6*, 24880–24888. [CrossRef]

95. Antony, C.E.; Jayakumar, A.; Yadav, A.; Sivakumar, N.S.; Kamath, N.; Kamble, V.B.; Jaiswal-Nagar, D. Metal-polymer hybrid chemiresistive sensor for low concentration fast hydrogen detection. *arXiv* **2020**, arXiv:2011.07599. [CrossRef]

96. Luong, H.M.; Pham, M.T.; Guin, T.; Madhogaria, R.P.; Phan, M.-H.; Larsen, G.K.; Nguyen, T.D. Sub-second and ppm-level Optical Sensing of Hydrogen Using Templated Control of Nano-hydride Geometry and Composition. *Nat. Commun.* **2021**, *12*, 2414. [CrossRef]

97. Kumar, V.; Gautam, Y.K.; Gautam, D.; Kumar, A.; Adalati, R.; Singh, B.P. Highly Sensitive and Selective Hydrogen Gas Sensor with Humidity Tolerance Using Pd-Capped SnO₂ Thin Films of Various Thicknesses. *Fuels* **2023**, *4*, 279–294. [CrossRef]

98. Melios, C.; Winters, M.; Strupinski, W.; Panchal, V.; Giusca, C.E.; Jayawardena, K.D.G.I.; Rorsman, N.; Silva, S.R.P.; Kazakova, O. Tuning epitaxial graphene sensitivity to water by hydrogen intercalation. *Nanoscale* **2017**, *9*, 3440–3448. [CrossRef]

99. Darmadi, I.; Nugroho, F.A.A.; Langhammer, C. High-Performance Nanostructured Palladium-Based Hydrogen Sensors—Current Limitations and Strategies for Their Mitigation. *ACS Sens.* **2020**, *5*, 3306–3327. [CrossRef] [PubMed]

100. Pandey, G.; Lawaniya, S.D.; Kumar, S.; Dwivedi, P.K.; Awasthi, K. A highly selective, efficient hydrogen gas sensor based on bimetallic (Pd–Au) alloy nanoparticle (NP)-decorated SnO₂ nanorods. *J. Mater. Chem. A* **2023**, *11*, 26687–26697. [CrossRef]

101. Meillaud, L. La Guyane se Lance dans L'hydrogène Pour la Fusée Ariane 6. H2Today. Available online: https://hydrogentoday.info/guyane-hydrogene-fusee-ariane-6/ (accessed on 29 October 2025).

102. IMOCA OceansLab: Le Bateau de Course à Hydrogène sur le Point D'achever sa Construction, Bateau-Electrique.com. Available online: https://www.bateau-electrique.com/actualites/imoca-oceanslab-bateau-course-hydrogene-sur-point-achever-construction/ (accessed on 29 October 2025).

103. Cet Avion Hypersonique à Hydrogène Pourrait être le Plus Rapide au Monde. Available online: https://www.h2-mobile.fr/actus/avion-hypersonique-hydrogene-plus-rapide-monde/ (accessed on 29 October 2025).

104. Li, H.-W.; Onoue, K. Compressed Hydrogen: High-Pressure Hydrogen Tanks. In *Hydrogen Energy Engineering: A Japanese Perspective*; Sasaki, K., Li, H.-W., Hayashi, A., Yamabe, J., Ogura, T., Lyth, S.M., Eds.; Springer: Tokyo, Japan, 2016; pp. 273–278. ISBN 978-4-431-56042-5.

105. Dai, J.; Li, Y.; Ruan, H.; Ye, Z.; Chai, N.; Wang, X.; Qiu, S.; Bai, W.; Yang, M. Fiber Optical Hydrogen Sensor Based on WO3-Pd2Pt-Pt Nanocomposite Films. *Nanomaterials* **2021**, *11*, 128. [CrossRef]

106. Pathak, A.K.; Verma, S.; Sakda, N.; Viphavakit, C.; Chitaree, R.; Rahman, B.M.A. Recent Advances in Optical Hydrogen Sensor including Use of Metal and Metal Alloys: A Review. *Photonics* **2023**, *10*, 122. [CrossRef]

107. Wang, C.; Yang, J.; Li, J.; Luo, C.; Xu, X.; Qian, F. Solid-state electrochemical hydrogen sensors: A review. *Int. J. Hydrogen Energy* **2023**, *48*, 31377–31391. [CrossRef]

108. Hoffmann, M.; Wienecke, M.; Ciudin, R. MEMS-Based Hydrogen Sensors: A State of the Art Review | Request PDF. In Proceedings of the 2023 International Interdisciplinary PhD Workshop, Wismar, Germany, 3–5 May 2023. [CrossRef]
109. Lee, B.; Cho, S.; Jeong, B.J.; Lee, S.H.; Kim, D.; Kim, S.H.; Park, J.-H.; Yu, H.K.; Choi, J.-Y. Highly responsive hydrogen sensor based on Pd nanoparticle-decorated transfer-free 3D graphene. *Sens. Actuators B Chem.* **2024**, *401*, 134913. [CrossRef]

110. Li, C.; Zhu, H.; Guo, Y.; Ye, S.; Wang, T.; Fu, Y.; Zhang, X. Hydrogen-Induced Aggregation of Au@Pd Nanoparticles for Eye-Readable Plasmonic Hydrogen Sensors. *ACS Sens.* **2022**, *7*, 2778-2787. [CrossRef]

111. Xing, Q.; Chen, X.; Cai, Y.; Zhang, M. Composites homogènes à base de SnO₂ riches en défauts d'oxygène pour capteur d'hydrogène à réponse et récupération rapides. *Sens. Actuators B Chem.* **2024**, *419*, 136407. [CrossRef]

112. Chen, Q.; Zhang, Y.; Tang, M.; Wang, Z.; Zhang, D. Capteur d'hydrogène à réponse rapide basé sur l'hétérojonction de nanofeuilles de MXène et de SnO₂ pour la détection des défaillances des batteries lithium-ion. *Sens. Actuators B Chem.* **2024**, *405*, 135229. [CrossRef]

113. Xing, Q.; Cai, Y.; Zhang, M. A sub-second response/recovery hydrogen sensor based on multifunctional palladium oxide modified heterojunctions. *Sens. Actuators B Chem.* **2024**, *401*, 134956. [CrossRef]

114. Yang, R.; Yuan, Z.; Jiang, C.; Zhang, X.; Qiao, Z.; Zhang, J.; Liang, J.; Wang, S.; Duan, Z.; Wu, Y.; et al. Ultrafast Hydrogen Detection System Using Vertical Thermal Conduction Structure and Neural Network Prediction Algorithm Based on Sensor Response Process. *ACS Sens.* **2025**, *10*, 2181-2190. [CrossRef]

115. Morsi, I. A microcontroller based on multi sensors data fusion and artificial intelligent technique for gas identification. In *IECON 2007: 33rd Annual Conference of the IEEE Industrial Electronics Society, Vols 1-3, Conference Proceedings*; Institute of Electrical and Electronics Engineers, Industrial Electronics Society: Piscataway, NJ, USA, 2007; pp. 2203-2208.

116. Abiola, A.; Manzano, F.S.; Andujar, J.M. A Novel Deep Reinforcement Learning (DRL) Algorithm to Apply Artificial Intelligence-Based Maintenance in Electrolysers. *Algorithms* **2023**, *16*, 541. [CrossRef]

117. Shemyakina, A.A.; Levina, A.I.; Korablev, V.V.; Lepekhin, A.A. Architecture of the management system for hydrogen production at hydropplications. *Int. J. Hydrogen Energy* **2024**, *69*, 1227-1235. [CrossRef]

118. Liewhiran, C.; Tamaekong, N.; Wisitsoraat, A.; Phanichphant, S. H₂ Sensing Response of Flame-spray-made Ru/SnO₂ Thick Films Fabricated from Spin-Coated Nanoparticles. *Sensors* **2009**, *9*, 8996-9010. [CrossRef] [PubMed]

119. Lu, Z.; Zhou, Q.; Xu, L.; Gui, Y.; Zhao, Z.; Tang, C.; Chen, W. Synthesis and Characterization of Highly Sensitive Hydrogen (H₂) Sensing Device Based on Ag Doped SnO₂ Nanospheres. *Materials* **2018**, *11*, 492. [CrossRef]

120. Liu, L.; Guo, C.; Li, S.; Wang, L.; Dong, Q.; Li, W. Improved H₂ sensing properties of Co-doped SnO₂ nanofibers. *Sens. Actuators B Chem.* **2010**, *150*, 806-810. [CrossRef]

121. Liewhiran, C.; Tamaekong, N.; Tuantranont, A.; Wisitsoraat, A.; Phanichphant, S. The effect of Pt nanoparticles loading on H₂ sensing properties of flame-spray-made SnO₂ sensing films. *Mater. Chem. Phys.* **2014**, *147*, 661-672. [CrossRef]

122. Nazari, A. Prediction performance of PEM fuel cells by gene expression programming. *Int. J. Hydrogen Energy* **2012**, *37*, 18972-18980. [CrossRef]

123. Nabipour, N.; Qasem, S.N.; Salwana, E.; Bagban, A. Evolving LSSVM and ELM models to predict solubility of non-hydrocarbon gases in aqueous electrolyte systems. *Measurement* **2020**, *164*, 107999. [CrossRef]

124. Moosavi, S.R.; Vaferi, B.; Wood, D.A. Auto-detection interpretation model for horizontal oil wells using pressure transient responses. *Adv. Geo-Energy Res.* **2020**, *4*, 305-316. [CrossRef]

125. Ghate, V.N.; Dudul, S.V. Cascade Neural-Network-Based Fault Classifier for Three-Phase Induction Motor. *IEEE Trans. Ind. Electron.* **2011**, *58*, 1555-1563. [CrossRef]

126. Kumar, M.; Singh Bhati, V.; Ranwa, S.; Singh, J.; Kumar, M. Pd/ZnO nanorods based sensor for highly selective detection of extremely low concentration hydrogen. *Sci. Rep.* **2017**, *7*, 236. [CrossRef]

127. Zhu, J.; Zhan, Y.; Ni, X.; Gao, Y. Artificial Intelligence of Things in Hydrogen Sensing: Toward Optic and Intelligent System. *Research* **2025**, *8*, 0750. [CrossRef]

128. Kwon, Y.M.; Son, Y.; Lee, D.H.; Lim, M.H.; Han, J.K.; Jang, M.; Park, S.; Kang, S.; Yim, S.; Myung, S.; et al. Enhancing selectivity and sensitivity in gas sensors through noble metal-decorated ZnO and machine learning. *Appl. Surf. Sci.* **2025**, *693*, 162750. [CrossRef]

129. Durand, B. Conception et Réalisation D'une Nouvelle Génération de Nano-Capteurs de gaz à base de Nanofils Semiconducteurs. Ph.D. Thesis, UPS Toulouse-Université Toulouse 3 Paul Sabatier, Toulouse, France, 2016.

130. Tang, X.-Y.; Yang, W.-W.; Liu, Z.; Li, J.-C.; Ma, X. Deep learning performance prediction for solar-thermal-driven hydrogen production membrane reactor via bayesian optimized LSTM. *Int. J. Hydrogen Energy* **2024**, *82*, 1402-1412. [CrossRef]

131. Kalanur, S.S.; Yoo, I.-H.; Seo, H. Pd on MoO₃ nanoplates as small-polaron-resonant eye-readable gasochromic and electrical hydrogen sensor. *Sens. Actuators B Chem.* **2017**, *247*, 357-365. [CrossRef]

132. Diao, S.; Li, H.; Wang, J.; Wei, C.; Yao, Y.; Yu, M. Hydrogen leakage location prediction for fuel cell vehicles in parking lots: A combined study of CFD simulation and CNN-BiLSTM modeling. *Int. J. Hydrogen Energy* **2025**, *109*, 115-128. [CrossRef]

133. Sun, Y.; Zhang, H.; Zhao, T.; Zou, Z.; Shen, B.; Yang, L. A New Convolutional Neural Network With Random Forest Method for Hydrogen Sensor Fault Diagnosis. *IEEE Access* **2020**, *8*, 85421-85430. [CrossRef]
134. Zhai, Z.; Liu, Y.; Li, C.; Wang, D.; Wu, H. Electronic Noses: From Gas-Sensitive Components and Practical Applications to Data Processing. *Sensors* **2024**, *24*, 4806. [CrossRef] [PubMed]

135. Zhang, L.-S.; Du, Y.; Guo, X.-M. Gas-sensing performance of Au loading Sn<sub>0.97</sub>Cu<sub>0.03</sub>O<sub>2</sub> and its use on quanti-fying CO and H2 concentration by BP-temperature modulation method. *Mater. Sci. Semicond. Process.* **2023**, *15*, 107291. [CrossRef]

136. Zeng, X.; Shahzeb, M.; Cheng, X.; Shen, Q.; Xiao, H.; Xia, C.; Xia, Y.; Huang, Y.; Xu, J.; Wang, Z. An Enhanced Gas Sensor Data Classification Method Using Principal Component Analysis and Synthetic Minority Over-Sampling Technique Algorithms. *Micromachines* **2024**, *15*, 1501. [CrossRef]

137. Yu, Y.; Cao, X.; Li, C.; Zhou, M.; Liu, T.; Liu, J.; Zhang, L. A Review of Machine Learning-Assisted Gas Sensor Arrays in Medical Diagnosis. *Biosensors* **2025**, *15*, 548. [CrossRef]

138. Wang, X.; Zhu, Y.; Gao, W. Design of hydrogen sensor relying on Pd-MWCNT/WO<sub>3</sub> sensing materials for selective and rapid hydrogen detection. *Sens. Actuators B Chem.* **2025**, *42*, 136648. [CrossRef]

139. Mirzaei, H.; Ramezankhani, M.; Earl, E.; Tasnim, N.; Milani, A.S.; Hoorfar, M. Investigation of a Sparse Autoencoder-Based Feature Transfer Learning Framework for Hydrogen Monitoring Using Microfluidic Olfaction Detectors. *Sensors* **2022**, *22*, 7696. [CrossRef]

140. Seo, J.; Noh, Y.; Kang, Y.-J.; Lim, J.; Ahn, S.; Song, I.; Kim, K.C. Graph neural networks for anomaly detection and diagnosis in hydrogen extraction systems. *Eng. Appl. Artif. Intell.* **2024**, *135*, 108846. [CrossRef]

141. Machine Learning Based Approach to Selective Measurements of Hydrogen for Catalytic Gas Sensors. Available online: https://xplorestaging.ieee.org/document/10036195 (accessed on 12 June 2025).

142. Selective Low-Temperature Hydrogen Catalytic Sensor. Available online: https://xplorestaging.ieee.org/document/9759978 (accessed on 12 June 2025).

143. Gao, D.; Gao, S.; Deng, H.; Liu, H.; Hou, D.; Lu, Q.; He, X.; Huang, S. Modulation de la structure électronique de surface de PdO/SnO<sub>2</sub> par chargement de Pd pour des performances de détection d'hydrogène supérieures. *Chem. Eng. J.* **2025**, *515*, 163694. [CrossRef]

144. Franić, N.; Pivac, I.; Barbir, F. A review of machine learning applications in hydrogen electrochemical devices. *Int. J. Hydrogen Energy* **2025**, *102*, 523–544. [CrossRef]

145. Wang, L.; Song, J. Review-Recent Progress in the Design of Chemical Hydrogen Sensors. *J. Electrochem. Soc.* **2024**, *171*, 017510. [CrossRef]

146. Thanh, H.V.; Rahimi, M.; Tangparitkul, S.; Promsuk, N. Modeling the thermal transport properties of hydrogen and its mixtures with greenhouse gas impurities: A data-driven machine learning approach. *Int. J. Hydrogen Energy* **2024**, *83*, 1–12. [CrossRef]

147. Gardner, E.L.W.; Gardner, J.W.; Udrea, F. Micromachined Thermal Gas Sensors—A Review. *Sensors* **2023**, *23*, 681. [CrossRef]

148. Zhang, X.; Li, X.; Zhang, X.; Peng, W. Fiber Optics-Mechanics Coupling Sensor for High-Performance Hydrogen Detection. *Photonic Sens.* **2025**, *15*, 250314. [CrossRef]

149. Zhang, X.; Li, X.; Peng, W. Status and development of fiber optic hydrogen sensing technology (invited). *Infrared Laser Eng.* **2025**, *54*, 20250072. [CrossRef]

150. Tanaka, M.; Iwata, C.; Nakada, M.; Kurita, S.; Kakiuchi, H. Chromatographic Analysis of Molecular Hydrogen (H<sub>2</sub>) in the Atmosphere for Understanding Atmospheric Tritiated Hydrogen (HT)). *Plasma Fusion Res.* **2023**, *18*, 2405038. [CrossRef]

151. Sutarya, D.; Mahendra, A. Virtual Sensor for Time Series Prediction of Hydrogen Safety Parameter in Degussa Sintering Furnace. In Proceedings of the 2015 2nd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), Semarang, Indonesia, 16–18 October 2015; Isnanto, R., Facta, M., Widianto, E., Eridani, D., Eds.; Diponegoro University: Semarang, Indonesia, 2015; pp. 81–86.

152. Kawano, T.; Tsuboi, N.; Tsujii, H.; Sugiyama, T.; Asakura, Y.; Uda, T. Infinitesimal concentration hydrogen Analyzer using trace reduction detector (TRD). *Jpn. J. Appl. Phys. Part 2-Lett.* **2003**, *42*, L549–L551. [CrossRef]

153. Ohira, S.-I.; Toda, K. Micro gas analyzers for environmental and medical applications. *Anal. Chim. Acta* **2008**, *619*, 143–156. [CrossRef]

154. Arrhenius, K.; Buker, O.; Fischer, A.; Persijn, S.; Moore, N.D. Development and evaluation of a novel analyser for ISO14687 hydrogen purity analysis. *Meas. Sci. Technol.* **2020**, *31*, 075010. [CrossRef]

155. Higuchi, K.; Yamamoto, K.; Kajioka, H.; Toiyama, K.; Honda, M.; Orimo, S.; Fujii, H. Remarkable hydrogen storage properties in three-layered Pd/Mg/Pd thin films. *J. Alloys Compd.* **2002**, *330*, 526–530. [CrossRef]

156. Züttel, A. Materials for hydrogen storage. *Mater. Today* **2003**, *6*, 24–33. [CrossRef]

157. The Role of Palladium in a Hydrogen Economy. Scilit. Available online: https://www.scilit.com/publications/bb5261e34ecb7df566a58e03bf0c442a (accessed on 11 June 2025).

158. Lin, B.; Wu, X.; Xie, L.; Kang, Y.; Du, H.; Kang, F.; Li, J.; Gan, L. Atomic Imaging of Subsurface Interstitial Hydrogen and Insights into Surface Reactivity of Palladium Hydrides. *Angew. Chem. Int. Ed.* **2020**, *59*, 20348–20352. [CrossRef]

159. Li, X.; Fu, L.; Karimi-Maleh, H.; Chen, F.; Zhao, S. Innovations in WO<sub>3</sub> gas sensors: Nanostructure engineering, functionalization, and future perspectives. *Heliyon* **2024**, *10*, e27740. [CrossRef]
160. Li, H.; Wu, C.-H.; Liu, Y.-C.; Yuan, S.-H.; Chiang, Z.-X.; Zhang, S.; Wu, R.-J. Mesoporous $WO_3$-TiO$_2$ heterojunction for a hydrogen gas sensor. *Sens. Actuators B Chem.* **2021**, *341*, 130035. [CrossRef]

161. Yamada, T.; Yamaguchi, T.; Hara, K. $WO_3$-based Hydrogen Gas Sensors Using Stacked Thin Films with Interspace. In Proceedings of the 2019 IEEE Sensors, Montreal, QC, Canada, 27–30 October 2019; IEEE: New York, NY, USA, 2019.

162. Boudiba, A.; Zhang, C.; Umek, P.; Bittencourt, C.; Snyders, R.; Olivier, M.-G.; Debliquy, M. Sensitive and rapid hydrogen sensors based on Pd-$WO_3$ thick films with different morphologies. *Int. J. Hydrogen Energy* **2013**, *38*, 2565–2577. [CrossRef]

163. Kishnani, V.; Yadav, A.; Mondal, K.; Gupta, A. Palladium-Functionalized Graphene for Hydrogen Sensing Performance: Theoretical Studies. *Energies* **2021**, *14*, 5738. [CrossRef]

164. Harun-Or-Rashid, M.; Mirzaei, S.; Nasiri, N. Nanomaterial Innovations and Machine Learning in Gas Sensing Technologies for Real-Time Health Diagnostics. *ACS Sens.* **2025**, *10*, 1620–1640. [CrossRef] [PubMed]

165. Saint-Cirgue, G. Apprendre le ML en une Semaine. Machinelearnia. 2019. Available online: https://machinelearnia.com (accessed on 20 February 2025).

166. MATLAB for Machine Learning, 2nd edition. Available online: https://fr.mathworks.com/academia/books/matlab-for-machine-learning-ciaburro.html (accessed on 22 April 2025).

167. Djeziri, M.; Djedidi, O.; Morati, N.; Seguin, J.-L.; Bendahan, M.; Contaret, T. A temporal-based SVM approach for the detection and identification of pollutant gases in a gas mixture. *Appl. Intell.* **2021**, *52*, 6065–6078. [CrossRef]

168. Dreyfus, G. *Apprentissage Statistique*, 3rd ed.; Eyrolles: Paris, France, 2008; ISBN 978-2-212-12229-9.

169. Ding, X.; Liu, J.; Yang, F.; Cao, J. Random radial basis function kernel-based support vector machine. *J. Frankl. Inst.* **2021**, *358*, 10121–10140. [CrossRef]

170. Biernat, É. Data Science: Fondamentaux et Études de Cas. Available online: https://elmoukrie.com/wp-content/uploads/2022/05/eric-biernat-michel-lutz-yann-lecun-data-science_-_fondamentaux-et-etudes-de-cas_-_machine-learning-avec-python-et-r-eyrolles-2015.pdf (accessed on 22 April 2025).

171. Cortes, C.; Vapnik, V. Support-vector networks. *Mach. Learn.* **1995**, *20*, 273–297. [CrossRef]

172. Ferko, N.; Djeziri, M.A.; Al Sheikh, H.; Moubayed, N.; Bendahan, M.; Seguin, J.-L. Detection and Estimation of Ethanol Concentration in a Disturbed Environment Utilizing Random Forest. In Proceedings of the 2024 IEEE International Conference on Design, Test and Technology of Integrated Systems (DTTIS), Aix-En-Provence, France, 14–16 October 2024; pp. 1–5. Available online: https://ieeexplore.ieee.org/abstract/document/10780114/ (accessed on 30 October 2025).

173. Shomope, I.; Al-Othman, A.; Tawalbeh, M.; Alshaideh, H.; Almomani, F. Machine learning in PEM water electrolysis: A study of hydrogen production and operating parameters. *Comput. Chem. Eng.* **2025**, *194*, 108954. [CrossRef]

174. Zhu, X.; Goldberg, A.B. Introduction to Statistical Machine Learning. In *Introduction to Semi-Supervised Learning*; Zhu, X., Goldberg, A.B., Eds.; Springer International Publishing: Cham, Switzerland, 2009; pp. 1–8. ISBN 978-3-031-01548-9.

175. Song, W.; Gao, C.; Zhao, Y.; Zhao, Y. A Time Series Data Filling Method Based on LSTM—Taking the Stem Moisture as an Example. *Sensors* **2020**, *20*, 5045. [CrossRef]

176. Katterbauer, K.; Al Shehri, A.; Qasim, A.; Yousef, A. Analyzing Hydrogen Flow Behavior Based on Deep Learning Sensor Selection Optimization Framework. *J. Fluids Eng.-Trans. ASME* **2024**, *146*, 071112. [CrossRef]

177. Rapid Forecasting of Hydrogen Concentration Based on a Multilayer CNN-LSTM Network | Request PDF. ResearchGate. Available online: https://www.researchgate.net/publication/368705758_Rapid_forecasting_of_hydrogen_concentration_based_on_aMultilayer_CNN-LSTM_network (accessed on 10 June 2025).

178. Hassan, A.; Refaat, M.; Hemeida, A.M. Image classification based deep learning: A Review. *Aswan Univ. J. Sci. Technol.* **2022**, *2*, 11–35. [CrossRef]

179. Li, A.; Lang, Z.; Ni, C.; Tian, H.; Wang, B.; Cao, C.; Du, W.; Qian, F. Deep learning-based source term estimation of hydrogen leakages from a hydrogen fueled gas turbine. *Int. J. Hydrogen Energy* **2024**, *86*, 875–889. [CrossRef]

180. Zhang, J.; Li, C.; Yin, Y.; Zhang, J.; Grzegorzek, M. Applications of artificial neural networks in microorganism image analysis: A comprehensive review from conventional multilayer perceptron to popular convolutional neural network and potential visual transformer. *Artif. Intell. Rev.* **2023**, *56*, 1013–1070. [CrossRef]

181. Raheli, B.; Aalami, M.T.; El-Shafie, A.; Ghorbani, M.A.; Deo, R.C. Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: A case study of Langat River. *Environ. Earth Sci.* **2017**, *76*, 503. [CrossRef]

182. IBM SPSS Neural Networks 31. Available online: https://www.ibm.com/docs/en/SSLVMB_31.0.0/nl/fr/pdf/IBM_SPSS_Neural_Network.pdf (accessed on 17 June 2025).

183. Karri, V.; Ho, T.; Madsen, O. Artificial neural networks and neuro-fuzzy inference systems as virtual sensors for hydrogen safety prediction. *Int. J. Hydrogen Energy* **2008**, *33*, 2857–2867. [CrossRef]

184. Mendizabal, J.; Vernon, D.; Martin, B.; Bajon-Fernandez, Y.; Soares, A. Short-term memory artificial neural network modelling to predict concrete corrosion in wastewater treatment plant inlet chambers using sulphide sensors. *J. Water Process Eng.* **2025**, *69*, 106821. [CrossRef]
185. Park, H.; Kim, Y.; Jung, E.S.; Kwon, S. Implantable hybrid chrome silicide temperature sensor for power MEMS devices. *Micro Nano Lett.* **2011**, *6*, 895-899. [CrossRef]

186. Banerjee, T.; Chowdhury, K.; Agrawal, D.P. Tree based data aggregation in sensor networks using polynomial regression. In *Proceedings of the 2005 7th International Conference on Information Fusion (Fusion), Vols 1 and 2, Philadelphia, PA, USA*, 25-28 July 2005; IEEE: New York, NY, USA, 2005; pp. 1146-1153.

187. Lee, G.; Jung, M.; Song, M.; Choo, J. Unsupervised anomaly detection of the gas turbine operation via convolutional auto-encoder. In *Proceedings of the 2020 IEEE International Conference on Prognostics and Health Management (ICPHM), Detroit, MI, USA*, 8-10 June 2020; pp. 1-6.

188. Xing, Y.; Wang, B.; Gong, Z.; Hou, Z.; Xi, F.; Mou, G.; Du, Q.; Gao, F.; Jiao, K. Data-Driven Fault Diagnosis for PEM Fuel Cell System Using Sensor Pre-Selection Method and Artificial Neural Network Model. *IEEE Trans. Energy Convers.* **2022**, *37*, 1589-1599. [CrossRef]

189. Arroyo, P.; Meléndez, F.; Suárez, J.I.; Herrero, J.L.; Rodríguez, S.; Lozano, J. Electronic Nose with Digital Gas Sensors Connected via Bluetooth to a Smartphone for Air Quality Measurements. *Sensors* **2020**, *20*, 786. [CrossRef]

190. Gwiżdż, P.; Brudnik, A.; Zakrzewska, K. Hydrogen Detection with a Gas Sensor Array—Processing and Recognition of Dynamic Responses Using Neural Networks. *Metrol. Meas. Syst.* **2015**, *22*, 3-12. [CrossRef]

191. Yan, J.; Guo, X.; Duan, S.; Jia, P.; Wang, L.; Peng, C.; Zhang, S. Electronic Nose Feature Extraction Methods: A Review. *Sensors* **2015**, *15*, 27804-27831. [CrossRef]

192. Cai, J.; Luo, J.; Wang, S.; Yang, S. Feature selection in machine learning: A new perspective. *Neurocomputing* **2018**, *300*, 70-79. [CrossRef]

193. Attouri, K.; Mansouri, M.; Hajji, M.; Kouadri, A.; Bouzrara, K.; Nounou, H. Enhanced Neural Network Method-Based Multiscale PCA for Fault Diagnosis: Application to Grid-Connected PV Systems. *Signals* **2023**, *4*, 381-400. [CrossRef]

194. Velliangiri, D.S. A Review of Dimensionality Reduction Techniques for Efficient Computation. *Procedia Comput. Sci.* **2019**, *165*, 104-111. [CrossRef]

195. Visalakshi, S.; Radha, V. A literature review of feature selection techniques and applications: Review of feature selection in data mining. In *Proceedings of the 2014 IEEE International Conference on Computational Intelligence and Computing Research, Coimbatore, India*, 18-20 December 2014; pp. 1-6.

196. Feature Selection Methods: Case of Filter and Wrapper Approaches for Maximising Classification Accuracy. ResearchGate. Available online: https://www.researchgate.net/publication/322920304_Feature_selection_methods_Case_of_filter_and.wrapper_aproaches_for_maximising_classificationendority (accessed on 30 June 2025).

197. Zebari, R.; Abdulazeez, A.; Zeebaree, D.; Zebari, D.; Saeed, J. A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction. *J. Appl. Sci. Technol. Trends* **2020**, *1*, 56-70. [CrossRef]

198. Zhu, X.; Goldberg, A.B. Semi-Supervised Support Vector Machines. In *Introduction to Semi-Supervised Learning*; Zhu, X., Goldberg, A.B., Eds.; Springer International Publishing: Cham, Switzerland, 2009; pp. 57-67. ISBN 978-3-031-01548-9.

199. Zhu, X.; Goldberg, A.B. Overview of Semi-Supervised Learning. In *Introduction to Semi-Supervised Learning*; Zhu, X., Goldberg, A.B., Eds.; Springer International Publishing: Cham, Switzerland, 2009; pp. 9-19. ISBN 978-3-031-01548-9.

200. Nkulikiyinka, P.; Yan, Y.; Gulec, F.; Manovic, V.; Clough, P.T. Prediction of sorption enhanced steam methane reforming products from machine learning based soft-sensor models. *Energy AI* **2020**, *2*, 100037. [CrossRef]

201. Algamili, A.S.; Khir, M.H.M.; Dennis, J.O.; Ahmed, A.Y.; Alabsi, S.S.; Ba Hashwan, S.S.; Junaid, M.M. A Review of Actuation and Sensing Mechanisms in MEMS-Based Sensor Devices. *Nanoscale Res. Lett.* **2021**, *16*, 16. [CrossRef]

202. Gong, J.; Wang, Z.; Tang, Y.; Sun, J.; Wei, X.; Zhang, Q.; Tian, G.; Wang, H. MEMS-based resistive hydrogen sensor with high performance using a palladium-gold alloy thin film. *J. Alloys Compd.* **2023**, *930*, 167398. [CrossRef]

203. Bévenot, X.; Trouillet, A.; Veillas, C.; Gagnaire, H.; Clément, M. Hydrogen leak detection using an optical fibre sensor for aerospace applications. *Sens. Actuators B Chem.* **2000**, *67*, 57-67. [CrossRef]

204. Samsudin, M.R.; Shee, Y.G.; Adikan, F.R.M.; Razak, B.B.A.; Dahari, M. Fiber Bragg Gratings (FBG) Hydrogen Sensor for Transformer Oil Degradation Monitoring | Request PDF. *IEEE Sens. J.* **2016**, *16*, 2993-2999. [CrossRef]

205. Wang, H.; Xiong, S.; Song, J.; Zhao, F.; Yan, Z.; Hong, X.; Zhang, T.; Zhang, W.; Zhou, K.; Li, C.; et al. High temperature resistant ultra-short DBR Yb-doped fiber laser. *Appl. Opt.* **2019**, *58*, 4474-4478. [CrossRef] [PubMed]

206. Liu, C.; Sun, Y.; Guo, J.-Y.; Li, X.-L.; Tao, L.; Hu, J.-Y.; Cao, J.-X.; Tang, P.-H.; Zhang, Y. Gas sensor array based on carbon-based thin-film transistor for selective detection of indoor harmful gases. *RARE Met.* **2024**, *43*, 4401-4411. [CrossRef]

207. Viejo, C.G.; Fuentes, S.; Godbole, A.; Widdicombe, B.; Unnithan, R.R. Development of a low-cost e-nose to assess aroma profiles: An artificial intelligence application to assess beer quality. *Sens. Actuators B Chem.* **2020**, *308*, 127688. [CrossRef]

208. Rivai, M.; Aulia, D. Use of Electronic Nose to Identify Levels of Cooking Cookies. *IEEE Access* **2024**, *12*, 97235-97247. [CrossRef]
209. Ferko, N.; Djeziri, M.A.; Sheikh, H.A.; Moubayed, N.; Bendahan, M.; El Rafei, M.; Seguin, J.-L. Methodology for estimating ethanol concentration with artificial intelligence in the presence of interfering gases and measurement delay. *Sens. Actuators B Chem.* **2024**, *421*, 136502. [CrossRef]

210. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. *MetaArXiv* **2020**. [CrossRef]

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