# p-/n-type conduction in activated carbon NO2 sensors with enhanced low-concentration sensitivity at room temperature

Proscovia Kyokunzire, Jean Zaraket, Maria Teresa Izquierdo, Vanessa Fierro, Alain Celzard

► To cite this version:

Proscovia Kyokunzire, Jean Zaraket, Maria Teresa Izquierdo, Vanessa Fierro, Alain Celzard. p-/n-type conduction in activated carbon NO2 sensors with enhanced low-concentration sensitivity at room temperature. Carbon, 2026, 246, pp.120891. 10.1016/j.carbon.2025.120891. hal-05323026

HAL Id: hal-05323026

https://hal.univ-lorraine.fr/hal-05323026v1

Submitted on 21 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

# p-/n-type conduction in activated carbon NO₂ sensors with enhanced low-concentration sensitivity at room temperature

Proscovia Kyokunzire<sup>a</sup>, Jean Zaraket<sup>a</sup>, Maria Teresa Izquierdo<sup>b, c</sup>, Vanessa Fierro<sup>a, d</sup>, Alain Celzard<sup>a, c, e</sup>

## ARTICLE INFO

### Keywords:

- Activated carbon

- Nitrogen dioxide

- Adsorption

- Resistive gas sensors

- Sensing parameters

## ABSTRACT

Gas sensors play a vital role in monitoring air quality across a wide range of applications, including industry, transport and healthcare. The present study investigates the nitrogen dioxide (NO₂) sensing capability of four commercial activated carbons (ACs), including two coal-based (MSC 30 and CW 30) and two bio-based (A supra and PK1-3) ACs at room temperature (25 °C). The ACs exhibited distinct textural properties, with specific surface areas ranging from 916 to 2233 m²g⁻¹. Key sensing parameters including responses, R and Rₐi (%), response time, recovery time, sensitivity, linearity, repeatability, reversibility and stability were examined under controlled NO₂ exposures. The fabricated sensors were exposed to continuous cycles of varying NO₂ concentrations, from 1 to 10 ppm, in 1 ppm increments, and from 1 to 20 ppm in 5 ppm increments. The sensors exhibited p- or n-type conduction behavior, depending on the AC, confirmed by Mott-Schottky measurements. Reversible sensing was governed by weak physical interactions (physisorption) of NO₂ gas on the sensor surface and charge transport via charge hopping. These findings offer valuable guidance for selecting appropriate materials in the development of high-performance, room temperature AC-based NO₂ sensors, which are an essential component of effective environmental monitoring.

## 1. Introduction

The growing demand for energy in modern everyday life has led to greater dependence on non-renewable resources. This, in turn, gives rise to the emission of various hazardous gases including NO₂, CO, SO₂, NH₃ and H₂S, which pose serious threats to human health and the environment [1]. The development of NO₂ gas sensors has been the subject of a significant number of research studies. The aim of these studies is to identify sensing materials that enable an NO₂ gas sensor to be simultaneously more sensitive, more selective, with short response times, operating at room temperature, and inexpensive.

Many types of gas sensor systems have been developed for continuous monitoring of trace concentrations of pollutants. These include chromatographic [2], electrochemical [3], conductometric [4], potentiometric [5], surface acoustic wave (SAW) [6] and chemiresistive gas sensors [7]. Among the various technologies, chemiresistive gas sensors have emerged as an attractive option due to several advantages, including their cost-effectiveness, simple preparation, ease of use,

portable size, ability to detect a wide range of toxic gases and low fabrication cost [8]. Chemiresistors comprise a chemically sensitive conductive or semi-conductive film deposited between two metal electrodes. When exposed to a target chemical analyte, these sensors exhibit a measurable change in electrical resistance. Their sensing mechanism relies on a change in the electrical resistance of the sensitive layer resulting from interactions between the sensor surface and gas molecules [8,9].

Various types of materials have been used as sensing layers in chemiresistive devices for NO₂ gas detection, including conductive polymers [10] and metal oxides [11]. The latter are sensitive at relatively high operating temperatures (i.e. > 300 °C), which significantly increases energy costs and hampers their practical applications [12]. Furthermore, the limitations associated with temperature have led to considerable challenges in the monitoring of toxic gases, especially in explosive environments and fuel-powered household appliances [13]. Conductive polymers (CPs) have been identified as potentially viable candidates for the development of gas sensors, due to their outstanding

<footnote><sup>a</sup> Université de Lorraine, Centre National de la Recherche Scientifique (CNRS), Institut Jean Lamour (IJL), F-88000, Épinal, France</footnote>

<footnote><sup>b</sup> Instituto de Carboquimica (ICB-CSIC), Miguel Luesma Castan 4, E-50018, Zaragoza, Spain</footnote>

<footnote><sup>c</sup> Institut Universitaire de France (IUF), 75231, Paris, France</footnote>

<footnote>* Corresponding author. Université de Lorraine, Centre National de la Recherche Scientifique (CNRS), Institut Jean Lamour (IJL), F-88000, Épinal, France.
E-mail address: alain.celzard@univ-lorraine.fr (A. Celzard).</footnote>
electrical performance, tunable morphology, scalable manufacturing processes and cost-effectiveness [14]. However, CP-based sensing films suffer from environmental instability [15].

Carbon-based sensors (e.g., those based on single- [16] and multi-walled [17] carbon nanotubes, graphene [18], graphene oxide [19], reduced graphene oxide [20], carbon nanofibers [21], or carbon hydrids [22,23]) are particularly important due to their relatively low cost compared with many metal oxide or noble metal-based sensors, semiconducting electrical properties, and ability to be modified and to operate at room temperature [12,24]. However, these materials also present limitations, such as low detection sensitivity, difficulty in handling and large-scale fabrication, slow response time, and sometimes require complicated recovery procedures such as stimulation by UV light [25]. More importantly, their operation strongly depends on defect density, the generation of which necessitates multiple processing steps [26]. It is therefore imperative to consider the advantages and disadvantages of existing sensing materials when designing and fabricating gas sensors. The use of alternative sensing materials that can operate at room temperature, while at the same time reducing fabrication complexity and minimizing processing steps, is essential to achieve high performance gas sensors.

Conversely, activated carbons (ACs) offer many advantages over the aforementioned metal oxides, CPs and carbon-based materials when used as sensitive layers for toxic gas sensors. In our recent work, we indeed demonstrated that the performance of standalone nanoporous carbon materials is comparable to that reported in the literature for gas sensors based on modified graphene, CPs and other metal oxides [12, 27]. This is attributed to the high surface area, rich surface chemistry, high degree of surface reactivity and highly developed porous structure of these materials, responsible for maintaining the transport pathway of charge carriers while providing passages for the diffusion of target gas molecules [28,29]. These characteristics make ACs particularly promising candidates for use in efficient and scalable gas sensing devices.

Although activated carbon (AC) has been the subject of numerous studies for the detection of gases such as ammonia (NH₃) and nitric oxide (NO), few studies have been conducted on sulfur dioxide (SO₂), hydrogen (H₂), and volatile organic compounds (VOCs). However, these gases have physicochemical properties that differ from those of NO₂, making direct extrapolation of performance unreliable. Our recent review article revealed that, to date, only one study has focused on B-doped ACs for NO₂ sensing at room temperature [12], while the performance of unmodified/pristine ACs for NO₂ detection has not been systematically examined. In this work, we fill this gap by evaluating the NO₂ sensing performance of four commercial ACs with distinct physicochemical properties at room temperature. To the best of our knowledge, this is the first study demonstrating that the type of conduction of ACs in NO₂ gas sensing can be influenced by their origin, coal-based ACs exhibiting n-type behavior, and bio-based ACs exhibiting p-type behavior with respect to the same target gas. This conclusion could not be drawn from tests performed on a single material in our previous work. These results highlight the importance of material origin in the design of AC-based NO₂ sensors and open up new perspectives for the development of high-performance sensing devices at room temperature.

In this study, four commercial ACs were used as sensitive layers to detect NO₂ gas at room temperature (25 °C). The textural properties, elemental chemical compositions and electrochemical properties of the ACs were analyzed. Their sensing performance was measured at various NO₂ gas concentrations in the ranges 1–10 ppm or 1–20 ppm. Our results show distinct NO₂ sensing behaviors between two types of ACs, demonstrating n- and p-type sensing behaviors for coal- and bio-based ACs, respectively. High responses were obtained with small concentration increments, compared to large increments, probably due to diffusion effects [30,31]. While previous studies have reported the use of external recovery stimuli such as heating [32] and UV light irradiation [25,33], the sensors in the present work were tested without any preliminary treatment or modification steps and in the absence of such

external recovery stimuli. This approach was adopted to avoid the use of external energy, which has been shown to have detrimental effects on sensor performance, resulting in a reduction in durability and long term stability [34]. Our results thus demonstrate that commercially available ACs offer a practical approach for NO₂ detection without the need for chemical modification steps. Our comparative approach also provides criteria for selecting suitable ACs from the wide range of commercially available products for use in high-performance gas sensors to detect NO₂, a major environmental pollutant.

## 2. Experimental details

### 2.1. Materials/chemicals

In the present study, we used four commercial activated carbons (ACs), namely A Supra, PK1-3, CW 30 and MSC 30. The first two ACs were supplied by the company Norit (Netherlands), while CW 30 was provided by Silcarbon Aktivkohle (Germany) and MSC 30 by Kansai Coke & Chemicals™ (Japan). Nafion® (5 % w/w in H₂O) was purchased from Sigma Aldrich, isopropanol (99.9 %) from VWR, and interdigitated electrodes (IDEs) (28 mm × 5 mm × 0.4 mm) from Nano SPR LLC, Chicago, USA. All materials were used as received.

### 2.2. Preparation of sensing layers

The sensing layers were prepared according to our previous work [27]. Typically, each carbon sample was dispersed separately in an isopropanol/water solution (20/80, v/v) containing 0.2 wt% Nafion® binder, followed by sonication for 60 min to obtain a uniform dispersion. Four 2-µL drops of each of the resulting dispersion were drop-cast onto pre-cleaned gold-plated IDEs, with an interdigitated gap of 30 µm on a glass substrate. The coated IDEs were dried at room temperature under an infrared lamp, and subsequently placed in a vacuum oven at 80 °C for 12 h to ensure complete drying.

### 2.3. Gas sensing measurements

The sensing properties of the fabricated layers were investigated by measuring their electrical resistance in a flow-through gas chamber, as presented in our previous work [27]. A cylindrical steel test chamber (diameter: 100 mm, height: 100 mm, and volume: 780 mL) was used, equipped with a gas inlet and outlet to allow for controlled flow of the target gas and air. The resistance was measured using a two-wire resistance measurement technique, generating a potential difference of approximately 50 mV across the sensor. For each activated carbon sample, measurements were performed on four individual sensors to account for variations between samples and ensure the statistical reliability of the results. Certified gas cylinders were used as sources of NO₂, the target gas, and synthetic air for sensor purging. A constant total flow rate of 90 mL min⁻¹ was maintained during all experiments. Dry gases were used, with no additional humidity introduced (RH = 0 % during all measurements). The temperature inside the gas chamber was maintained at 25 °C and monitored using a commercial temperature probe. A standard NO₂ gas mixture (NO₂ cylinder: 90 ppm in dry synthetic air) was further diluted with synthetic air, using electronic mass flow controllers to generate various NO₂ concentrations (i.e., either from 1 to 10 ppm or from 1 to 20 ppm). The response, R (%), of the sensors to NO₂ was calculated using either of the following formulas:

$$ Response, R (\%) = \frac{R_{AIR} - R_{GAS}}{R_{GAS}} \times 100\% \qquad (1) $$

where R<sub>AIR</sub> and R<sub>GAS</sub> are the electrical resistances in the presence of synthetic air and NO₂ gas, respectively. On the other hand, the initial or baseline resistance of the sensors was fixed to calculate this other response definition, R<sub>ci</sub> (%):
$$ Response, R_{ci} (%) = \frac{R_{AIR_{ci}} - R_{GAS}}{R_{GAS}} \times 100 \quad (2) $$

where $R_{AIR_{ci}}$ is the resistance of each new cycle and was used as the initial resistance to calculate the responses $R_{ci}$ [27].

## 2.4. Material characterization

### 2.4.1. Surface area and porosity of ACs

Nitrogen adsorption isotherms at -196 °C were obtained after out-gassing the AC samples under secondary vacuum at 110 °C for at least 48 h using a Micromeritics ASAP 2020 automatic adsorption apparatus. The specific surface area was determined from $N_2$ isotherms by applying the BET equation to obtain $A_{BET}$ (m² g⁻¹), or by applying the 2D Non-Local Density Functional Theory (2D-NLDFT) to obtain $S_{2D-NLDFT}$ (m² g⁻¹) using SAIEUS software supplied by Micromeritics, which also allows the pore size distribution (PSD) to be determined. By integrating the PSD in the adequate pore diameter range, the volumes of micropores $V_{micro}$ (cm³ g⁻¹) (pore size w < 2 nm), mesopores $V_{meso}$ (cm³ g⁻¹) (2 < w < 50 nm), and the total volume of pores $V_T$ (cm³ g⁻¹), were determined.

### 2.4.2. Chemical composition and surface analysis

Elemental Analysis (EA) was conducted using a Vario EL Cube analyzer (Elementar, Germany) to measure the bulk carbon (C), hydrogen (H), nitrogen (N) and oxygen (O) contents. Prior to analysis, the samples were dried at 105 °C for 24 h to remove moisture. 2 mg were burned in a furnace, from which the gas was separated using trapping and chromatographic columns. A thermal conductivity detector quantified the gases from which C, H and N contents could be calculated. The oxygen content was measured separately in another column using a similar procedure.

XPS spectra were acquired using an OMICRON ESCAPlus spectrometer to study the surface chemistry of the materials. The instrument was equipped with a hemispherical electron energy analyzer, and the measured analysis area was 1.75 mm × 2.75 mm. Operating parameters included a voltage of 10 kV and a current of 15 mA, utilizing a non-monochromatized Mg X-ray source (1253.6 eV) under vacuum conditions. To improve the accuracy of spectral data, background subtraction using a Shirley-type model, peak fitting and subsequent quantification were carried out using CASA software.

To measure the surface pH of the carbon samples, 0.1 g of dry carbon sample was added to 20 mL of distilled water, and the suspension was stirred overnight to reach equilibrium. The pH of the solution was then measured. This measurement provided information on the acidity or basicity of the material surface.

Potentiometric titration was used to identify the nature of the surface functional groups on AC surfaces and quantify them before and after $NO_2$ adsorption, using an automatic titrator (905 Titrando, Metrohm commanded with Tiamo® software V2.2). The suffix -E is added to the name of the samples after $NO_2$ exposure. This technique provides detailed information on the acid-base behavior and pKa distribution of surface groups, which are directly relevant to gas-surface interactions. Although Boehm titration is commonly used to quantify oxygen-containing functional groups, our interest in acidity distribution and strength led us to adopt potentiometric titration exclusively. This approach further aligns with previous comparative studies of AC surface chemistry [35,36], which demonstrate that potentiometric and Boehm titrations provide comparable results, with potentiometric methods offering details on acidity distribution that are more precise.

For that purpose, 0.1 g of AC was placed in 50 mL of $NaNO_3$ solution (0.01 mol L⁻¹). 1 mL of 1 M HCl was then added to the solution. The mixture was stirred for 24 h and titrated with 0.1 M NaOH, under continuous $N_2$ saturation to eliminate the influence of atmospheric $CO_2$. Each AC sample was thus titrated with the base after acidifying the sample suspension. It is important to note that the sample solutions containing ACs exposed to $NO_2$ were not acidified with HCl prior to

titration with the base. The experimental data was then transformed into proton-binding curves representing the total amount of protonated sites. The pKa distributions and the numbers of groups represented by certain pKa values in selected ranges were thus determined [37].

### 2.4.3. Electrochemical characterization

The type of conduction in the ACs was analyzed using impedance spectroscopy. The measurements were carried out in a three-electrode cell using 0.5 M $Na_2SO_4$ as electrolyte. The reference electrode was Ag/AgCl (3 M KCl), while the active material (AC) was used as the working electrode, and a platinum rod as the counter-electrode. The working electrode was prepared by coating a graphite foil of effective area 0.2 cm² with a dispersion of AC containing Nafion® as a binder. The electrodes were dried at 105 °C for 24 h. Staircase Potentio Electrochemical Impedance Spectroscopy (SPEIS) measurements were performed using a VSP-300 electrochemical workstation (Bio-logic) in the frequency range 500 Hz to 100 kHz with a 10 mv ac amplitude, at various potentials relative to the reference electrode, ranging from -1.6 to 1.2 V.

## 3. Results and discussion

### 3.1. Textural properties

Fig. 1a shows the nitrogen adsorption-desorption isotherms of the ACs. Three samples, MSC 30, A supra and PK1-3 have type Ib isotherms with no (or almost no) hysteresis loop, characteristic of ACs with a large fraction of super micropores and narrow mesopores, according to the IUPAC classification [38]. The main difference is the relative pressure at which the adsorption plateau is reached: 0.1 for PK1-3, 0.2 for A supra and 0.4 for MSC 30. CW 30 shows a combination of type I and type IV isotherms, and exhibits a hysteresis loop at relative pressures above 0.5, suggesting the presence of capillary condensation in mesopores [39]. Fig. 1b and c shows the pore size distributions (PSDs) and cumulative pore volumes, respectively, of all the ACs studied, calculated using the NLDFT model. PK1-3 features two distinct peaks centered at ≈ 0.4 and 0.7 nm, suggesting a highly microporous structure, while A supra, MSC 30 and CW 30 show a combination of micro- and meso-porosity, with CW 30 presenting the highest mesopore content, up to 60 % [27,39]. MSC 30 presents the largest $S_{NLDFT}$ surface area of 2233 m² g⁻¹, followed by A supra and CW 30, both with ≈1550 m² g⁻¹, and finally PK1-3 with 916 m² g⁻¹. Total pore volumes of 1.65, 1.46, 0.85 and 0.53 cm³ g⁻¹ were obtained for MSC 30, CW 30, A supra and PK1-3, respectively. Fig. 1d shows the pore volumes corresponding to the ultra-micropore, super micropore and mesopore ranges, as well as $A_{BET}$ and $S_{NLDFT}$ areas. A supra has the highest micropore fraction (79 %), followed by MSC 30 (60 %), PK 1-3 (53 %), and CW 30 with the lowest at 40 %. Table S1 presents a comprehensive list of the textural properties of all AC samples in detail.

### 3.2. Chemical properties

The results of bulk composition (C, H, O and N, wt.%) of the ACs, determined by elemental analysis, are presented in Table S2. While MSC 30, A supra and PK1-3 have a high carbon content (above 94 wt%), CW 30 has a C content slightly below 80 wt%. The O content ranges from 2.8 to 17.7 wt% for A supra and CW 30, respectively, whereas the N content is highest in PK1-3 at 0.59 wt%. These findings indicate that the ACs differ significantly not only in terms of textural properties but also in terms of chemical composition.

XPS analysis was performed to study the nature of the functional groups present on the AC surfaces. The C, O and N contents in atomic % are shown in Table S2. The results of the high-resolution spectra of C 1s, O 1s and N 1s reveal the presence of several peaks for each element, as shown in Fig. 2. Deconvolution of all C 1s spectra yields several contributions with different binding energies (Fig. 2a and Table S3), which
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** (a) Nitrogen adsorption (solid symbols) and desorption (empty symbols) isotherms at –196 °C; (b) Corresponding pore size distribution; (c) Cumulative pore volumes calculated by the 2D-NLDFT model; (d) $A_{BET}$, $S_{NLDFT}$ and pore volumes in the ultra-micropore (<0.7 nm), super micropore (0.7–2 nm) and mesopore (2–50 nm) ranges for activated carbons MSC 30, CW 30, A supra and PK1-3. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** (a) C 1s, (b) O 1s and (c) N 1s high-resolution XPS spectra and corresponding deconvolution peaks for MSC 30, CW 30, A supra and PK1-3 samples. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

can be assigned to CI (hydrocarbons, aromatics, aliphatics) at ≈ 284.5 eV [40,41], CII (C–O single bond associated with ethers, phenols and anhydrides; C sp³; C ≡ N groups and C–N bonds in aromatic rings) at ≈

286.1 eV [42,43], CIII (double bonds C=O in carbonyls and quinones, and C=N groups) at ≈ 287.4 eV [41,42], CIV (C–O bonds in carboxyls and CO–N–CO groups) at ≈ 289.3 eV [40], and CV (shake-up π–π
satellite) at ≈ 290.4 eV [44]. All these assignments are in agreement with extensive XPS studies performed on commercially available ACs as referenced above.

The O 1S core level spectra presented in Fig. 2b shows asymmetric peaks centered at ≈ 533.3 eV for MSC 30 and CW 30 and at ≈ 532.8 eV for A supra and PK1-3. The fitting of each peak showed the presence of four contributions with binding energies and their respective areas reported in Table S3. The fitted peaks were assigned to different O bonds: OI was assigned to C=O double bonds in quinones and carboxylic acids, OII to -OH bonds in phenols, C–O–C to ether groups and C=O bonds in ester and anhydride groups. OIII was assigned to non-carboxylic C–O bonds in esters and anhydrides, while OIV can be assigned to adsorbed water and oxygen molecules [40,42]. The N 1s core level spectra for CW 30 and PK1-3 show the presence of four contributions with binding energies shown in Fig. 2c and Table S3, assigned to N-6 (pyridinic nitrogen, C ≡ N linkages), N-5 (pyrrolic nitrogen, arylnitrile groups), N-Q (quaternary nitrogen) and N-X (oxidized N) [45,46]. There was no N 1s fitting for both MSC 30 and A supra, as the XPS spectra (not shown) were not useable due to the negligible amount of nitrogen present at the surface of these ACs.

### 3.3. Gas sensing properties

#### 3.3.1. Semiconducting properties of ACs

The NO₂ gas sensing properties of the AC-based sensors were evaluated by monitoring changes in electrical resistance before and after exposure to NO₂ at 25 °C. The interaction between NO₂ molecules and the sensing layer surface leads to a change in charge carrier concentration, resulting in a measurable change in material resistance [8]. When sensing layers based on A supra and PK1-3 were exposed to NO₂ gas, a decrease in resistance was observed, which is typical of p-type semiconductors [27]. It has been reported in the literature that carbon-based materials typically exhibit p-type semiconductor behavior, linked to a certain degree of disappearance of π states, caused by the interaction of π electrons with oxygen-containing groups on the AC surface [29,47]. When exposed to oxidizing gases such as NO₂ (electron acceptors), charge transfer occurs from the carbon material to the gas molecules [48]. This interaction brings the Fermi level closer to the valence band, thereby increasing the concentration of hole carriers in the material. This process decreases the resistance of the sensing layer [49], consistent with the behavior observed for the ACs A supra and PK1-3.

In contrast, MSC 30 and CW 30 exhibited an unexpected increase in resistance when exposed to NO₂, characteristic of n-type semiconductors [29]. This unexpected n-type behavior can be correlated with the XPS results, even though the two ACs exhibit distinct surface properties. The sensing layer based on CW 30 is dominated by pyrrolic nitrogen (N5, 51.3 %). Pyrrolic nitrogen is a strong electron donor group, as its lone pair can interact with the π electrons of the AC framework, introducing donor-type states that shift the Fermi level upward or closer to the conduction band and promote electron conduction, thus giving rise to n-type behavior [50,51]. When exposed to NO₂, a strong electron acceptor gas, a charge transfer occurs from these donor states and the π network to the adsorbed NO₂ molecules. This withdrawal of electrons decreases the density of free electrons in the conduction band, resulting in an increase in electrical resistance, a behavior characteristic of n-type semiconductors when exposed to oxidizing gases [52].

On the other hand, XPS analysis confirmed the absence of nitrogen functionalities in MSC 30, thus ruling out nitrogen-related doping effects as the cause of the n-type behavior observed. However, the C1s and O1s spectra indicate the presence of oxygen-containing surface groups (C–O, C=O, O=C=O), which are known to be thermally unstable [53]. During high-temperature KOH activation of this specific AC, the partial decomposition of these functionalities and the restructuring of the carbon lattice can generate oxygen vacancies at the carbon surface [54]. These vacancies act as electron-donating defects, giving electrons to the

conduction band and shifting the Fermi level toward the conduction band, thus conferring n-type characteristics [55], even in the absence of nitrogen dopants. Upon exposure to NO₂ gas, a strong electron acceptor, gas molecules adsorb onto the MSC 30 surface and withdraw electrons from the conduction band. This reduces the number of available mobile carriers, decreasing conduction and increasing resistance, consistent with the observed n-type sensing response [56].

Thus, the n-type behavior of MSC 30 can be explained by the combined role of donor states related to oxygen vacancies and the electron-withdrawing nature of adsorbed NO₂ molecules, whereas the dominance of pyrrolic nitrogen, which induces strong electron donor states in CW 30, explains the observed behavior. Thus, although both ACs exhibit n-type responses, the underlying donor states differ: linked to oxygen-vacancy for MSC 30 and dominated by pyrrolic nitrogen for CW 30. Therefore, these results indicate that the observed p- and n-type characteristics result from the interplay of surface chemistry, defect states, and adsorbed NO₂ species.

The initial (baseline) resistances of the sensing layers were measured at ≈ 500, 149 and 37 Ω when based on MSC 30, A supra and PK1-3, respectively. In contrast, the layer based on CW 30 exhibited a larger initial resistance of 3.6 MΩ. The differences in electrical resistance of the ACs are likely related to their inherent nature and composition, rather than their n- or p-type character. More specifically, ACs derived from coal (MSC 30 and CW 30) and from biomass (A supra and PK1-3) contain different impurities, in terms of nature and quantities, which contribute to the observable differences between the two categories [57].

To further analyze and confirm the conductivity behavior of the ACs, the Mott-Schottky approach, based on impedance measurements, was applied [24]. With the AC material serving as the working electrode, a positive slope in the plot of 1/C² (C = capacitance) vs potential indicates n-type conductivity, whereas a negative slope represents p-type conductivity of the working electrode [29]. Thus, Fig. 3a and b exhibit a characteristic negative slope for A supra and PK1-3, respectively, indicating p-type conductivity and implying that holes (h⁺), positively charged, are the dominant charge carriers. In contrast, Fig. 3c and d shows a characteristic positive slope, indicating n-type conductivity and suggesting that electrons (e⁻), negatively charged, are the dominant charge carriers in MSC 30 and CW 30, respectively.

#### 3.3.2. Dynamic response from 1 to 20 ppm

The dynamic response-recovery characteristics of the sensors, shown in Fig. 4a and b, were investigated by injecting NO₂ gas at concentrations ranging from 1 to 20 ppm, with an increment of 5 ppm, using dry air as the background atmosphere. Sensors based on MSC 30 and CW 30, exhibiting n-type semiconducting behavior, demonstrated increasing responses, R (%), to increasing NO₂ concentrations. Conversely, sensors based on A supra and PK1-3, with p-type conductivity, exhibited opposite behaviors. The sensor based on CW 30 showed higher response values than all the other sensors at each concentration, and its response increased more sharply, indicating high sensitivity to NO₂ gas. At 20 ppm, the CW 30-based sensor exhibited a maximum response of 41.7 %, which was approximately 10 times higher than that of its n-type counterpart, MSC 30 (4.2 %). It also showed an approximately 14- and 7-fold greater response (in absolute value) than PK1-3 (−2.9 %) and A supra (−6 %), respectively. Since ACs are characterized by a high microporosity and surface area with a multitude of adsorption sites, a greater number of NO₂ molecules at high concentration interact with the available sites, resulting in a higher degree of adsorption. This is directly correlated with higher sensor responses observed at 20 ppm, indicating that more NO₂ is detected [27]. Furthermore, the individual responses observed can be attributed to the presence of surface oxygen groups, as revealed by XPS analysis. In particular, the presence of high levels of OII groups for each AC, predominantly associated with hydroxyl (–OH) groups, has been reported to facilitate sensitivity to NO₂ at ambient temperature. This is due to the short bonding distances and high binding energy of OII groups, in comparison to other oxygen groups [58,59].
<figure><img src="image_5.png" /><figcaption>Fig. 3. Mott-Schottky plots for: (a) A supra; (b) PK1-3; (c) MSC 30 and (d) CW 30, showing different conduction types. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

However, it is important to note that MSC 30 did not present the highest response, despite having the largest surface area (2233 m²g⁻¹) and a high volume of micropores (see again Fig. 1d). This suggests that most gas molecules were blocked by the small pores, limiting the rate of diffusion. As a large number of inner channels contributing to the high surface area were inaccessible to NO₂ molecules, the sensor response was attributed to adsorption and reaction on the outermost surface [60]. This led to the lower sensing response observed for MSC 30. Comparing the response of CW 30 to that of A supra, and the fact that both have similar surface areas (1540 m²g⁻¹ and 1546 m²g⁻¹, respectively), the high response of CW 30 observed in this study suggests that surface area alone is not responsible for the sensor's performance. The improved response of CW 30 can instead be attributed to the synergistic effect of its surface area and substantial 60 % mesoporous volume fraction, which facilitates the efficient diffusion of a great number of NO₂ molecules to reach the micropores [26]. This improved accessibility enables a greater number of reactive sites, particularly in the micropores, to participate in gas detection, emphasizing the key role of surface chemical heterogeneity and pore architecture in determining sensor performance [57]. The superior response of A supra compared with PK1-3 can be attributed to the effect of surface area (S<sub>NLDFT</sub> area of 1540 m²g⁻¹ vs 916 m²g⁻¹) and a higher degree of microporosity (79 % vs 53 %) [27].

Fig. 4c shows the response and recovery times of PK1-3 and A supra, and Fig. 4d those of MSC 30 and CW 30, as a function of NO₂ gas con- centration. Response time was defined as the time required for the variation in resistance to reach 90 % of the steady-state value when exposed to the target gas. Recovery time was defined as the time required for the sensor to return to 10 % of the total variation in resis- tance when exposed to air [61,62]. Since the sensors do not fully return to their original baseline, and each sensing cycle (corresponding to a different NO₂ concentration) has an offset baseline, the recovery time for each cycle was calculated relative to the resistance measured at the start

of that specific cycle. The response/recovery times calculated for A supra, PK1-3, MSC 30 and CW 30 are 148/264 s, 126/222 s, 185/283 s and 152/258 s, respectively, for 20 ppm NO₂. Response times exhibit a general decrease, whereas recovery times display an opposite trend as gas concentration increases from 1 to 20 ppm for all sensors. A similar dependence has been reported and is in good agreement with Freund- lich's law of gas diffusion reaction on a semiconducting surface [63], i.e. diffusion and reaction processes intensify with increasing gas concen- tration on the surface of the sensing layer. Therefore, at higher con- centrations, more NO₂ gas molecules diffuse more efficiently into the AC pores, resulting in a shorter response time with a greater change in resistance [64].

The prolonged recovery times observed with increasing gas con- centration can be attributed to the microporosity of these ACs. Whereas this feature is responsible for the favorable response of the sensor, it is also responsible for the slow desorption of gas molecules during the air purge stage [24]. The overly long recovery times observed for MSC 30 can also be attributed to its extremely high interaction surface area, which is occupied by a greater number of NO₂ molecules adsorbed at high ppm levels, which must be extracted during purging [27,65]. Re- covery times could be shortened by purging the system with a higher air flow rate. However, this approach has a negative effect on sensor sensitivity, stability and repeatability [24]. Therefore, the differences observed in response on the one hand, and on response and recovery times on the other hand, are related to differences in the chemical and structural surface properties of the four ACs, which affect their reactivity with NO₂ gas in different ways.

### 3.3.3. *Dynamic response from 1 to 10 ppm and sensor sensitivity*

The dynamic performance of the AC-based sensors was further tested in the presence of NO₂ gas at lower concentrations, ranging from 1 to 10 ppm by increments of 1 ppm. The representative real-time dynamic
<figure><img src="image_6.png" /><figcaption>Fig. 4. Real-time dynamic response of: (a) PK1-3 and A supra, and (b) CW 30 and MSC 30. The inset shows a zoom on the dynamic response plot of MSC 30. Response time (solid lines and solid symbols) and recovery time (dotted lines and empty symbols) of sensors based on (c) PK1-3 and A supra, and (d) MSC 30 and CW 30, exposed to NO₂ gas at concentrations ranging from 1 ppm to 20 ppm at 25 °C. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

transient resistance curves, obtained from one of four gas-sensing devices prepared for each material A supra, PK1-3, MSC30 and CW 30, as well as the average responses obtained, are shown in Fig. 5a, b and 5c for A supra and PK1-3 respectively, and Fig. 5d, e and 5f for MSC 30 and CW 30, respectively. The individual responses of four sensing devices based on PK1-3, from which the average response was calculated, are provided in the Supplementary Information (Fig. S1) for clarity. Although the individual sensors exhibit variations in their response, particularly device 3, which shows lower response values likely due to differences in baseline resistance resulting from non-uniform material deposition, the overall response trend is maintained and is consistent for all AC materials studied. When exposed to 5 ppm NO₂, sensors based on A supra, PK 1–3, MSC 30 and CW 30 display average responses in the order 4.6 %, 1.1 %, 4.6 % and 36.2 %, respectively. It is important to note that these response values are higher than those obtained for 5 ppm when the sensors were run from 1 to 20 ppm using increments of 5 ppm. Indeed, the corresponding response values were 1.3 %, 0.9 %, 0.3 % and 3.24 % for A supra, PK1-3, MSC 30 and CW 30, respectively. The response was observed to follow the same trend for 10 ppm NO₂.

This unexpected phenomenon might be explained as follows. For very dilute gases, individual NO₂ molecules are able to penetrate deeply into narrow micropores, allowing efficient charge transfer on either side of the molecule, i.e. on both walls of the pores in which they are confined. This results in a favorable charge hopping mechanism, as it implies a very short jump length. On the other hand, the presence of more concentrated gases on the sensor surface blocks pore entrances due to the simultaneous influx of NO₂ molecules, thus limiting penetration depth. Only molecules in contact with the wider pore walls, because they are closer to the external surface, can transfer their charges. As a

result, charge transfer becomes less efficient, with greater hopping lengths as the pores are wider near the AC surface [30,66,67].

Detailed plots illustrating this difference in response can be found in Fig. S2. It is also clear that the sensors have not fully recovered their initial baseline resistance values after purging with synthetic air. Due to this incomplete recovery, baseline drift can be observed in Fig. 5a, b, 5d and 5e in the dynamic resistance curves. To compensate for this, our future work will aim to improve the overall performance of the sensor by optimizing the desorption phase. Strategies such as pulsed thermal desorption to improve the release of NO₂ from the micropores of AC will be explored. In addition, functionalizing the AC surface with nanoparticles could promote more efficient desorption by creating weaker bonds with the gas to be detected.

The relationship between the sensor response and NO₂ gas concentration was obtained by linear fitting, as shown in Fig. 5g and h. This was performed for sensors exposed to concentrations from 1 to 20 ppm (with a 5 ppm increment) and from 1 to 10 ppm (with a 1 ppm increment). For each NO₂ concentration, four independent sensors from each AC sample were tested, and the average response (R %) and standard deviation (SD) are reported in Table S4. With the exception of MSC 30, with an R² of 0.91, the three sensors showed excellent linearity (R² > 0.98) in the 1–20 and 1–10 ppm NO₂ ranges. Sensor sensitivity, S, was calculated using the following formula:

$$S(\% \text{ ppm}^{-1}) = \frac{\Delta R (\%)}{\Delta C (\text{ppm})} \qquad (3)$$

where ΔR (%) is the change in sensor response and ΔC (ppm) is the change in gas concentration [25]. S was found to be higher in the concentration ranges 1–10 ppm than 1–20 ppm NO₂. Sensitivities at 1–10
<figure><img src="image_7.png" /><figcaption>Fig. 5. Representative dynamic transient resistance curves for sensors, exposed to NO₂ gas at 25 °C from 1 to 10 ppm (with a 1 ppm increment), based on: (a) A supra and (b) PK1-3, and (c) Concentration-dependent average response values from four gas-sensing devices. (d), (e), (f): Same as (a), (b), (c) but for MSC 30 and CW 30, respectively. Dependence of average response (%) on NO₂ concentration for all sensors exposed to: (g) 1–20 ppm (5 ppm increment), (h) 1–10 ppm (1 ppm increment). (i) Average response (R %) curves of CW 30 in the 1–5 ppm range (and its linear fit 1, corresponding to the high-sensitivity mode), and in the 6–10 ppm range (and its linear fit 2, corresponding to the low-sensitivity mode). The error bars in (c), (f) and (g–i) represent the standard deviation for four devices tested per concentration. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

and 1–20 ppm varied from 0.16 to 0.32 % ppm⁻¹, 0.31–1.06 % ppm⁻¹, 0.19–1.50 % ppm⁻¹ and 2.35–5.88 % ppm⁻¹ for PK1-3, A supra, MSC 30 and CW 30, respectively, which is again attributed to diffusion effects.

The plot for the CW 30-based sensor in Fig. 5i displays a unique linear behavior below 6 ppm, with a steeper slope (8.95 % ppm⁻¹) corresponding to a high-sensitivity mode in a relatively low concentration range of NO₂ gas. With an increase in concentration from 6 to 10 ppm, the response changes to a different linear dependence with a lower slope (2.12 % ppm⁻¹), termed low-sensitivity mode [30,68]. This is consistent with the response presented in Fig. 5f.

### 3.3.4. Sensor repeatability and reversibility

The repeatability of the AC-based sensors was investigated by cyclic sensing tests performed over five cycles of adsorption and desorption at a concentration of 5 ppm NO₂ gas, as shown in Fig. 6. To do this, we used the resistance, R<sub>AIRci</sub>, of each new cycle as the initial resistance to calculate the response, R<sub>ci</sub> (%), and plot the curves as described in one of our previous works [27]. It can be observed that the sensor response, R<sub>ci</sub> (%) is almost constant for A supra (Fig. 6a), PK1-3 (Fig. 6b) and CW 30 (Fig. 6d), indicating good repeatability. The sensor based on MSC 30

showed a drift in response as observed in Fig. 6c, which is attributable to its extremely large surface area that slows down the desorption process, and thus limits response recovery.

The reversibility of the sensors was also studied by gradually varying the NO₂ concentration from 1 to 20 ppm by increments of 5 ppm, then from 20 to 1 ppm, as shown in Fig. 7a to d. The sensors show reversible sensing cycles, with observable variations in the responses, R<sub>ci</sub> (%), obtained when exposed to NO₂ concentrations increasing from 1 to 20 ppm, and then decreasing from 20 to 1 ppm. Fig. 7e and f shows the response hysteresis generated in the AC-based sensors during NO₂ adsorption and desorption, corresponding to cycles of increasing (green upward arrows) and decreasing (orange downward arrows) concentration. The sensor response R<sub>ci</sub> (%) varies with increasing and decreasing NO₂ concentration.

The hysteresis was calculated using the following equation:

$$H (\%) = \frac{\Delta H_{max}}{2F_{fs}} \qquad (4)$$

where ΔH<sub>max</sub> is the maximum difference between the value of the
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Repetitive gas sensing performance of sensors based on: (a) A supra; (b) PK1-3; (c) MSC 30; and (d) CW 30, exposed to 5 ppm NO₂ over five cycles at 25 °C. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Reversible response ($R_{ci}$) curves (NO₂ concentration from 1 to 20 ppm and return from 20 to 1 ppm) at 25 °C for sensors based on: (a) A supra; (b) PK1-3; (c) MSC 30; and (d) CW 30. Corresponding hysteresis for: (e) A supra and PK1-3, and (f) CW 30 and MSC 30. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

response $R_{ci}$ (%) during adsorption at increasing concentration and its value during desorption at decreasing concentration. $F_{fs}$ is the full-scale response $R_{ci}$ (%) from the adsorption (increasing concentration) curve,

calculated as the difference between the maximum and minimum responses [69,70]. The hysteresis was found to be ≈ 2.9 %, 5.6 %, 3.5 % and 14 % for sensors based on A supra, PK1-3, CW 30 and MSC 30,
respectively. This observed hysteresis can be attributed to the different adsorption and desorption rates at each concentration [71]. However, the significant hysteresis observed for MSC 30 could also be attributed to its highly developed microporous structure, which potentially limits desorption and thus makes it slower and incomplete, resulting into poor reliability for this specific sensor [24]. Although MSC 30 may be excellent for adsorption purposes, its application in sensing devices would require optimization, such as surface modifications or its hybridization as a composite to reduce the effect of hysteresis and improve its overall performance.

### 3.3.5. Sensing mechanisms

Based on surface characterization, electrical response and impedance measurements of the ACs, sensing mechanisms can be proposed for the ACs studied. As revealed above by impedance measurements, ACs A supra and PK1-3 exhibit p-type conductivity. This p-type behavior is linked to a certain degree of disappearance of π states, caused by the interaction of π electrons with oxygen-containing groups on the AC surface. Adsorption of O₂ molecules from air onto the AC surface results in electron transfer from AC to adsorbed O₂ [29,72]. This leads to the formation of ionized oxygen species, thereby reducing further the number of mobile electrons available [29]. Upon exposure to NO₂ gas, more electrons are withdrawn from the AC surface due to its high electron affinity. However, as these p-type ACs already possess positively charged sites or holes as dominant charge carriers, this induces an increase in electrical conductivity and a subsequent decrease in resistance, observed during reversible exposure to NO₂ gas [27].

On the other hand, MSC 30 and CW 30 exhibited opposite sensing behavior to A supra and PK1-3 when exposed to NO₂ gas. The increase in resistance can also be explained by the electron-withdrawing property of NO₂, which reduces the concentration of mobile electrons, which are the main charge carriers in these two ACs. Continuous interaction with NO₂ results in the loss of more electrons from their surface, leading to a continuous decrease in electron carrier density and thus an increase in resistance [73].

### 3.3.6. Effect of NO₂ adsorption on AC

Surface acidity or basicity is one of the surface characteristics expected to affect the reactive adsorption of NO₂ on ACs [74]. As demonstrated in Fig. S3 of the SI, NO₂ adsorption leads to significant changes in the acid-base properties of the AC surface. A noticeable decrease in pH values was observed for all AC samples after NO₂ adsorption, which can be attributed to a mild oxidation of the carbon matrix [75]. Fig. S4a and S4b summarize the amounts of acidic and basic surface oxygen groups present on AC surfaces before and after NO₂ adsorption. It can be observed that NO₂ causes significant changes in the content and type of oxygen-containing surface groups. For all AC samples, a significant increase in the amount of surface oxygen groups of acidic character was observed, resulting from oxidation of the AC surface [76,77], following the reduction of NO₂ and the possible formation of HNO₃/HNO₂ as intermediates [78].

A drastic decrease in the amount of basic surface oxygen groups was observed for A supra and PK 1-3 AC samples. This can be attributed to the effect of the reaction of basic oxygen groups with NO₂ or to their neutralization by nitric acid formed during the adsorption process [79]. This is consistent with a well-known effect of oxidation of the carbon surface by nitric acid, which mainly results in an increase in the number of carboxylic acids [80]. A specific observation was made for the basic surface oxygen groups of MSC 30 and CW 30 with a drastic increase in the amount of such groups from 0.28 to 3.65 mmol g⁻¹, and from 0.59 to 5.64 mmol g⁻¹, respectively. This unexpected behavior was confirmed by repeating the experiments at least three times, and is attributable to surface oxidation phenomena by NO₂, which varied from one AC to another. A similar observation was reported by Nowicki et al. [79], following the use of different activation methods, which oxidize carbons differently. However, increasing the amount of basic groups did not

prevent the pH of the ACs from decreasing due to the numerous NO₂ molecules adsorbed. Detailed changes in peak positions as a function of pKₐ distributions and number of groups before and after NO₂ adsorption are presented in Fig. S5 and Table S5.

### 3.3.7. Effect of slight temperature variation on sensor response

In order to evaluate the influence of slight temperature fluctuations around room temperature on sensor performance, measurements were taken on two samples (A supra and CW 30) at temperatures between 23 °C and 27 °C (in increments of 1 °C). Gas concentrations were varied from 1 to 20 ppm. As shown in Fig. 8a, the response curves for both samples show a nearly linear increase with gas concentration over the entire range of temperatures tested. Calculating the percentage increase in response at 1 ppm NO₂ between 23 and 27 °C for CW 30 and A Supra gives ≈2.5 % and ≈3.1 %, respectively. Although Fig. 8a shows that the curves are almost indistinguishable, this calculation clearly indicates a difference in response attributable to temperature variations, which affect the adsorption-desorption kinetics of NO₂ gas at room temperature [81]. As observed previously, CW 30 exhibits higher overall responses than A supra, but both remain consistent, further suggesting good stability for room-temperature applications. This behavior is in line with the findings of studies on gas sensors at room temperature, which show that slight increases in temperature can improve the mobility of charges without significantly altering the interactions between the gas and the surface [82].

Fig. 8b shows the variation in response as a function of temperature at a fixed concentration of 10 ppm in a temperature range between 23 °C and 27 °C. An increase in the response of both samples was observed, with that of CW 30 increasing from 13.12 % to 13.64 %, corresponding to a total increase of 3.96 % over 4 °C, or ≈0.99 % per °C. On the other hand, the response of A supra increased from 2.79 % to 2.88 %, corresponding to an increase of 3.2 % or ≈0.81 % per °C. These results indicate a higher temperature sensitivity of CW 30 compared to A supra. This difference can be attributed to the material-specific adsorption-desorption kinetics resulting from the different surface chemical properties of the two ACs, or to better charge carrier mobility at higher temperature [83]. This behavior is consistent with reports in the literature, according to which the response often increases with temperature due to improved surface interactions and accelerated electron transfer dynamics [84].

Fig. 8c shows the sensitivities S of the sensors based on CW 30 and A supra, plotted as a function of temperatures between 23 °C and 27 °C for 1–20 ppm NO₂. The effect of temperature on S was quantified by calculating the slope of the sensitivity as a function of temperature (ΔS/ΔT). For CW 30, the slope was 0.03 % ppm⁻¹ °C⁻¹, meaning that each 1 °C increase raises sensitivity by 0.03 % per ppm. For Supra, the slope was 0.004 % ppm⁻¹ °C⁻¹, with each 1 °C increase raising sensitivity by 0.004 % per ppm. Expressed relative to gas concentration, this translates to an additional response of 0.030–0.594 % per °C between 1 and 20 ppm NO₂ for CW 30, and 0.004–0.068 % per °C for A supra, demonstrating that sensors based on CW 30 are more sensitive to temperature changes. Details of the values for individual concentrations are available in Table S6 of the Supplementary Information.

### 3.3.8. Short- and mid-term sensor stability

The stability of sensors based on CW 30, PK1-3 and A supra was investigated by exposing them to 10 ppm NO₂ for 7 continuous days (short-term), after which they were tested at 2-day intervals for a further 14 days (medium-term), as shown in Fig. 9a. An initial observation revealed a stable response R (%) in the short term (the first 7 consecutive days of testing), and a negligible decrease in response was observed in the medium term (the following 14 days of testing). This slight degradation may be attributed to two main factors: (1) incomplete sensor recovery after each sensing run, resulting in a progressive reduction in responses over time [27], and (2) contamination of the sensor surface by the atmosphere in which it was stored, since, for practical reasons, it did
<figure><img src="image_10.png" /><figcaption>Fig. 8. (a) Dependence of the average response (%) of CW 30 and A supra on NO₂ concentration at different temperatures (23–27 °C). (b) Dependence of the response on temperature at 10 ppm NO₂. (c) Dependence on temperature of sensitivity (S), calculated over a concentration range of 1–20 ppm. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 9. (a) Short- and medium-term stability of AC-based sensors exposed to 10 ppm NO₂ gas; (b) Response (R %) versus surface area of AC-, MOF- and zeolite-based sensors for NO₂ gas sensing at room temperature. The area of the circles corresponds to different NO₂ concentrations ranging from 1 to 20 ppm at which measurements were performed. The concentration values are represented by grey circles of different sizes. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

not remain in the test chamber during 3 weeks. Consequently, successive returns to the lab atmosphere every 2 days after the first 7 days of testing may have created a barrier made of adsorbed species between the sensing material interaction surface and the analyte molecule [85]. Consequently, effective sensor handling is important to ensure medium- and long-term response stability. It was not possible to determine the stability of the MSC 30-based sensors, due to the overly large surface area of MSC 30 that makes response recovery impossible. The sensing layer also exhibited insufficient adhesion to the electrodes, which resulted in detachment during consecutive days of sensor handling and testing.

Fig. 9b shows a comparative analysis of NO₂ sensing response and surface area in sensing materials based on ACs and other porous materials, including metal-organic frameworks (MOFs) and zeolites, at NO₂ concentrations ranging from 1 to 20 ppm. The response, R %, is plotted as a function of surface area, with the size of the circles corresponding to different NO₂ concentrations, the larger the circles, the higher the concentration. The graph shows that the AC sensors marked in green and surrounded by a dotted line (corresponding to the present work), despite their larger surface areas, exhibit moderate responses compared to those previously reported elsewhere for sensors based on MOFs [86–94] and on zeolites [95–98]. This is because the heterogeneity of the results used for comparison highlights significant difficulties in making surface area the sole parameter influencing sensing responses. The graph indeed clearly shows that higher surface areas do not always translate into higher responses.

It is also worth noting that reports on the use of pristine ACs, MOFs and zeolites for NO₂ gas sensing at room temperature are scarce, limiting the data for comparison purposes. Moreover, the relatively low electrical

conductivity and chemical stability of MOFs preclude their application in their pristine state [99]. As a result, the MOFs and zeolites referenced in the comparative data have all been functionalized or hybridized into composites, which explains their good responses compared with the pristine AC-based sensors studied in this work. Further details on the porous materials and other properties of the sensors used for comparison are presented in Table S7 of the supplementary information. In Table S7, performance comparisons in terms of response cannot be made directly, as the definition of response varies from one author to another, as indicated in the footnote to this table. Nevertheless, response times and recovery times are of the same order of magnitude as values for other porous materials, when these are available.

## 4. Conclusion

The results obtained in this study introduce a novel perspective in understanding the performance of different activated carbon (AC) materials for NO₂ gas sensing at room temperature. Contrary to the prevailing view that carbon materials are exclusively p-type semiconductors, this study has confirmed a distinct sensing property of unmodified ACs regarding p- and n-type semiconducting behavior for A supra and PK1-3, and MSC 30 and CW 30, respectively, for the same gas. This was attributed to the difference in surface chemical states of the AC materials, which resulted in different response trends when exposed to NO₂ gas. Furthermore, the study revealed that variation in concentration increment has a profound effect on sensing responses. Significantly higher responses were obtained with a smaller increment of 1 ppm, using a concentration range of 1–10 ppm, compared to a larger increment of 5 ppm for the range 1–20 ppm. This property may contribute to
the design of highly responsive gas sensors. In addition, the series of ACs studied, including A supra, PK1-3 and CW 30, exhibited desirable properties for NO₂ gas sensors, including good repeatability, reversibility and stability. On the other hand, MSC 30 exhibited insufficient performance, signifying the necessity for further optimization (e.g. surface modifications or composite hybridization) prior to its effective use in gas sensor applications. Analysis of the performance of the ACs studied compared with MOFs and zeolites indicates that, with a few exceptions for which some unmodified ACs proved excellent for NO₂ sensing, surface functionalization and hybridization with other materials may still be necessary steps to achieve competitive, high-performance NO₂ sensors based on ACs. This will be the subject of a forthcoming work. Future work will also focus on evaluating the effect of variable but controlled humidity conditions on the sensing performance of the most promising AC-based sensors, as soon as the necessary experimental setup becomes available. In addition, follow-up experiments to evaluate sensor selectivity with various interfering gases, such as CO₂, SO₂, H₂S and NH₃ at room temperature, are also being designed.

### CRediT authorship contribution statement

**Proscovia Kyokunzire:** Writing – original draft, Investigation, Formal analysis. **Jean Zaraket:** Writing – review & editing, Validation, Supervision, Funding acquisition, Data curation, Conceptualization. **Maria Teresa Izquierdo:** Investigation. **Vanessa Fierro:** Writing – review & editing, Resources. **Alain Celzard:** Writing – review & editing, Validation, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization.

### Declaration of competing 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.

### Acknowledgements

P.K. is grateful to the French Ministry of Higher Education for funding this research work.

### Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.carbon.2025.120891.

### References

[1] P. Bharathi, S. Harish, M. Shimomura, M.K. Mohan, J. Archana, M. Navaneethan, Ultrasensitive and reversible NO2 gas sensor based on SnS2/TiO2 heterostructures for room temperature applications, Chemosphere 346 (2024) 140486, https://doi.org/10.1016/j.CHEMOSPHERE.2023.140486.

[2] Y. Lee, H. Son, J. Lee, S.-H. Lim, Review on micro-gas chromatography system for analysis of multiple low-concentration volatile organic compounds: preconcentration, separation, detection, integration, and challenges, (n.d.), http://doi.org/10.1186/s40486-024-00203-3.

[3] H.A.M. Khan, M. V Rao, Q. Li, Recent advances in electrochemical sensors for detecting toxic gases: NO2, SO2 and H2S, Sensors 19 (2019) 905, https://doi.org/10.3390/s19040905.

[4] Jun Zhang, Xianghong Liu, Giovanni Neri, Nicola Pinna, Nanostructured materials for Room-TemperatureGas sensors, Adv. Mater. 28 (2016) 785-974, https://doi.org/10.3390/s23136055.

[5] Hong Zhang, Haowei Zhu, Shibin Nie, Siyu Wu, Xiaoyong Liu, Songfeng Yin, Room temperature potentiometric sensor for hydrogen sulfide and sulfur dioxide based on a Zn-doped Na3Zr2Si2PO12 electrolyte, Sensor. Actuator. B Chem. 427 (2025).

[6] Xue Li, Yuan Feng, Jingyu Long, Haifeng Lv, Yuanjun Guo, Xiaotao Zu*, MXene-activated graphene oxide enhancing NO2 capture and detection of surface acoustic wave sensors, Sensor. Actuator. B Chem. 401 (2024) 135006, https://doi.org/10.1016/j.snb.2023.135006.

[7] M. Shaik, V.K. Rao, M. Gupta, K.S.R.C. Murthy, R. Jain, Chemiresistive gas sensor for the sensitive detection of nitrogen dioxide based on nitrogen doped graphene nanosheets, RSC Adv. 6 (2015) 1527-1534, https://doi.org/10.1039/C5RA21184K.

[8] Pegah Najafi, Ahad Ghaemi, Chemiresistor gas sensors: design, challenges, and strategies: a comprehensive review, Chem. Eng. J. 498 (2024) 154999, https://doi.org/10.1016/j.cej.2024.154999.

[9] F.J. Ibañez, F.P. Zamborini, Chemiresistive sensing with chemically modified metal and alloy nanoparticles, Small 8 (2012) 174-202, https://doi.org/10.1002/smll.201002232.

[10] G. Jeong, H.J. Cheon, S.Y. Shin, E. Wi, P. Kyokunzire, H. Cheon, V. Van Tran, T. T. Vu, M. Chang, Improved NO2 gas sensing performance of nanoporous conjugated polymer (CP) thin films by incorporating preformed CP nanowires, Dye. Pigment. 214 (2023) 111235, https://doi.org/10.1016/j.dye pig.2023.111235.

[11] Q. Li, W. Zeng, Y. Li, Metal oxide gas sensors for detecting NO2 in industrial exhaust gas: recent developments, Sensor. Actuator. B Chem. 359 (2022) 131579, https://doi.org/10.1016/j.snb.2022.131579.

[12] P. Kyokunzire, J. Zaraket, V. Fierro, A. Celzard, Recent developments in the use of activated carbon-based materials for gas sensing applications, J. Environ. Chem. Eng. 12 (2024) 113702, https://doi.org/10.1016/j.jece.2024.113702.

[13] T. Wang, Y. Guo, P. Wan, H. Zhang, X. Chen, X. Sun, Flexible transparent electronic gas sensors, Small 12 (2016) 3748-3756, https://doi.org/10.1002/SMLL.201601049. PAGE:STRING:ARTICLE/CHAPTER.

[14] M. Jeong, G. Shin, S.Y. Kyokunzire, P. Cheon, H.J. Wi, E. Woo, M. Chang, High-performance nitric oxide gas sensors based on an ultrathin nanoporous Poly(3-hexylthiophene) film, Biosensors 13 (2023) 132, https://doi.org/10.3390/bios13010132.

[15] K. Proscovia, H.J. Cheon, S.Y. Shin, G. Jeong, S. Go, K.S. Kim, R.-S. Park, Y.-I. Huh, M. Chang, Enhanced charge transport of conjugated polymer/reduced graphene oxide composite films by solvent vapor pre-treatment, Funct. Compos. Struct. 4 (2022) 025002, https://doi.org/10.1088/2631-6331/ac65d7.

[16] K. Xu, C. Wu, X. Tian, J. Liu, M. Li, Y. Zhang, Z. Dong, Single-walled carbon nanotube-based gas sensors for NO 2 detection, Integr. Ferroelectr. 135 (2012) 132-137, https://doi.org/10.1080/10584587.2012.685424.

[17] D. Ma, Y. Su, T. Tian, H. Yin, T. Huo, F. Shao, Z. Yang, N. Hu, Y. Zhang, Highly sensitive room-temperature NO2Gas sensors based on three-dimensional multiwalled carbon nanotube networks on SiO2Nanospheres, ACS Sustain. Chem. Eng. 8 (2020) 13915-13923, https://doi.org/10.1021/acssuschemeng.0c02707.

[18] G. Ko, H.Y. Kim, J. Ahn, Y.M. Park, K.Y. Lee, J. Kim, Graphene-based nitrogen dioxide gas sensors, Curr. Appl. Phys. 10 (2010) 1002-1004, https://doi.org/10.1016/j.cap.2009.12.024.

[19] J. Park, Y. Kim, S.Y. Park, S.J. Sung, H.W. Jang, C.R. Park, Band gap engineering of graphene oxide for ultrasensitive NO2 gas sensing, Carbon N. Y. 159 (2020) 175-184, https://doi.org/10.1016/j.carbon.2019.11.063.

[20] G. Lu, L.E. Ocola, J. Chen, Reduced graphene oxide for room-temperature gas sensors, Nanotechnology 20 (2009) 445502, https://doi.org/10.1088/0957-4484/20/44/445502.

[21] A.G. Bannov, N.I. Lapekin, P.B. Kurmashov, A.V. Ukhina, A. Manakhov, Room-temperature NO2 gas sensors based on granulated carbon nanofiber material, Chemosensors 10 (2022) 525, https://doi.org/10.3390/chemosensors10120525.

[22] S. Liu, Z. Wang, Y. Zhang, C. Zhang, T. Zhang, High performance room temperature NO2 sensors based on reduced graphene oxide-multiwalled carbon nanotubes-tin oxide nanoparticles hybrids, Sensor. Actuator. B Chem. 211 (2015) 318-324, https://doi.org/10.1016/j.SNB.2015.01.127.

[23] P. Dariyal, S. Sharma, G.S. Chauhan, B.P. Singh, S.R. Dhakate, Recent trends in gas sensing via carbon nanomaterials: outlook and challenges, Nanoscale Adv. 3 (2021) 6514-6544, https://doi.org/10.1039/d1na00707f.

[24] N.A. Travlou, T.J. Bandoz, Nanoporous carbon-composites as gas sensors: importance of the specific adsorption forces for ammonia sensing mechanism, Carbon N. Y. 121 (2017) 114-126, https://doi.org/10.1016/j.carbon.2017.05.081.

[25] I.S. Saggu, L. Singh, S.S. Kushvaha, M. Singh, S. Sharma, Dual discrimination of xylene and NO2 with UV-Boosted recovery at room temperature using SnSe2/MWCNT composite-based sensors, ACS Appl. Electron. Mater. (2025), https://doi.org/10.1021/acsaelm.4c02272.

[26] D.W. Kim, S. Ha, Y. il Ko, J.H. Wee, H.J. Kim, S.Y. Jeong, T. Tojo, C.M. Yang, Y. A. Kim, Rapid, repetitive and selective NO2 gas sensor based on boron-doped activated carbon fibers, Appl. Surf. Sci. 531 (2020) 147395, https://doi.org/10.1016/j.apsusc.2020.147395.

[27] P. Kyokunzire, J. Zaraket, V. Fierro, A. Celzard, Systematic optimization of NO2 detection using activated carbon-based resistive sensors, ACS Appl. Electron. Mater. (2025), https://doi.org/10.1021/ACSALEM.4C02151/SUPPL_FILE/EL4C02151_S1_001.PDF.

[28] N.A. Travlou, C. Ushay, M. Seredych, E. Rodríguez-Castellón, T.J. Bandoz, Nitrogen-doped activated carbon-based ammonia sensors: effect of specific surface functional groups on carbon electronic properties, ACS Sens. 1 (2016) 591-599, https://doi.org/10.1021/acssensors.6b00093.

[29] N.A. Travlou, M. Seredych, E. Rodríguez-Castellón, T.J. Bandoz, Activated carbon-based gas sensors: effects of surface features on the sensing mechanism, J. Mater. Chem. A 3 (2015) 3821-3831, https://doi.org/10.1039/c4ta06161f.

[30] S. Cui, H. Pu, S.A. Wells, Z. Wen, S. Mao, J. Chang, M.C. Hersam, J. Chen, Ultrahigh sensitivity and layer-dependent sensing performance of phosphorene-based gas sensors, Nat. Commun. 6 (2015), https://doi.org/10.1038/ncomms9632.

[31] A.C. Forse, C. Merlet, C.P. Grey, J.M. Griffin, NMR studies of adsorption and diffusion in porous carbonaceous materials, Prog. Nucl. Magn. Reson. Spectrosc. 124-125 (2021) 57-84, https://doi.org/10.1016/j.PNMRS.2021.03.003.

[32] L. Valentini, I. Armentano, J.M. Kenny, C. Cantalini, Sensors for sub-ppm NO2 gas detection based on carbon nanotube thin films, Artic. Appl. Phys. Lett. (2003), https://doi.org/10.1063/1.1545166.
[33] Z. Zhang, Z. Gao, R. Fang, H. Li, W. He, C. Du, UV-assisted room temperature NO2 sensor using monolayer graphene decorated with SnO2 nanoparticles. Ceram. Int. 46 (2020) 2255-2260, https://doi.org/10.1016/j.CERAMINT.2019.09.211.

[34] H. Chai, Z. Zheng, K. Liu, J. Xu, K. Wu, Y. Luo, H. Liao, M. Debliquy, C. Zhang, Stability of metal oxide semiconductor gas sensors: a review, IEEE News. J. 22 (2022) 5470-5481, https://doi.org/10.1109/JSEN.2022.3148264.

[35] A. Contescu, C. Contescu, K. Putyera, J.A. Schwarz, Surface acidity of carbons characterized by their continuous pK distribution and boehm titration, Carbon N. Y. 35 (1997) 83-94, https://doi.org/10.1016/S0008-6223(96)00125-X.

[36] T.J. Bandosz, J. Jagiello, C. Contescu, J.A. Schwarz, Characterization of the surfaces of activated carbons in terms of their acidity constant distributions, Carbon N. Y. 31 (1993) 1193-1202, https://doi.org/10.1016/0008-6223(93) 90072-I.

[37] M. Seredych, M.J. Biggs, T.J. Bandosz, Oxygen reduction on chemically heterogeneous iron-containing nanoporous carbon: the effects of specific surface functionalities, Microporous Mesoporous Mater. 221 (2016) 137-149, https://doi.org/10.1016/j.MICROMESO.2015.09.032.

[38] M. Thommes, K. Kaneko, A. V Neimark, J.P. Olivier, F. Rodríguez-reinoso, J. Rouquerol, K.S.W. Sing, Physisorption of gases, with special reference to the evaluation of surface area and pore size distribution (IUPAC Report), Pure Appl. Chem. 87 (2015) 1051-1069, https://doi.org/10.1515/pac-2014-1117.

[39] R.L.S. Canevesi, S. Schaefer, M.T. Izquierdo, A. Celzard, V. Fierro, Roles of surface chemistry and texture of nanoporous activated carbons in CO2 capture, ACS Appl. Nano Mater. 5 (2022) 3843-3854, https://doi.org/10.1021/acsanm.1c04474.

[40] A. Sánchez-Sánchez, V. Fierro, M.T. Izquierdo, A. Celzard, Functionalized, hierarchical and ordered mesoporous carbons for high-performance supercapacitors, J. Mater. Chem. A 4 (2016) 6140-6148, https://doi.org/10.1039/c6ta00738d.

[41] P. Burg, P. Fydrych, D. Cagniant, G. Nanse, J. Bimer, A. Jankowska, The characterization of nitrogen-enriched activated carbons by IR, XPS and LSER methods, Carbon N. Y. 40 (2002) 1521-1531, https://doi.org/10.1016/S0008-6223(02)00044-0.

[42] A. Swiatkowski, M. Pakula, S. Biniak, M. Walczyk, Influence of the surface chemistry of modified activated carbon on its electrochemical behaviour in the presence of lead(II) ions, Carbon N. Y. 42 (2004) 3057-3069, https://doi.org/10.1016/j.carbon.2004.06.043.

[43] H. Grajek, A. Swi' Atkowski, Z. Witkiewicz, M. Paku, La, S. Biniak, Changes in the Surface Chemistry and Adsorptive Properties of Active Carbon Previously Oxidised and heat-treated at Various Temperatures. I. Physicochemical Properties of the Modified Carbon Surface, (n.d.).

[44] A. Sánchez-Sánchez, M.T. Izquierdo, S. Mathieu, J. González-Álvarez, A. Celzard, V. Fierro, Outstanding electrochemical performance of highly N- and O-doped carbons derived from pine tannin, Green Chem. 19 (2017) 2653-2665, https://doi.org/10.1039/c7gc00491e.

[45] D. Hulicova-Jurcakova, M. Seredych, G.Q. Lu, T.J. Bandosz, Combined effect of Nitrogen- and oxygen-containing functional groups of microporous activated carbon on its electrochemical performance in supercapacitors, Adv. Funct. Mater. 19 (2009) 438-447, https://doi.org/10.1002/ADFM.200801236.

[46] A. Sánchez-Sánchez, F. Suárez-García, A. Martín-Alonso, J.M.D. Tascan, Aromatic polyamides as new precursors of nitrogen and oxygen-doped ordered mesoporous carbons, Carbon N. Y. 70 (2014) 119-129, https://doi.org/10.1016/j.CARBON.2013.12.080.

[47] N.I. Lapekin, P.B. Kurmashov, T.V. Larina, Y.A. Chesalov, D.S. Kurdyumov, A. V. Ukhina, E.A. Maksimovskiy, A.V. Ishchenko, V.I. Soev, A.G. Bannov, Carbon nanofibers synthesized at different pressures for detection of NO2 at room temperature, Chemosensors 11 (2023) 1-17, https://doi.org/10.3390/chemosensors11070381.

[48] S. Kumar, V. Pavelyev, P. Mishra, N. Tripathi, A review on chemiresistive gas sensors based on carbon nanotubes: device and technology transformation, Sensors Actuat. A Phys. 283 (2018) 174-186, https://doi.org/10.1016/j.SNA.2018.09.061.

[49] S.C. Kang, J.S. Im, Y. Lee, Improved sensitivity of an NO gas sensor by chemical activation of electrospun carbon fibers, Carbon Lett. 12 (2011) 21-25, https://doi.org/10.5714/CL.2011.12.01.21.

[50] A.E. Torres, S. Fomine, Electronic structure of graphene nanoribbons doped with nitrogen atoms: a theoretical insight, Phys. Chem. Chem. Phys. 17 (2015) 10608-10614, https://doi.org/10.1039/C5CP00227C.

[51] C. Walleni, S.B. Malik, G. Missaoui, M.A. Alouani, M.F. Nsib, E. Llobet, Selective NO2 gas sensors employing nitrogen- and boron-doped and codoped reduced graphene oxide, ACS Omega (2023), https://doi.org/10.1021/ ACSOmega.3C09460/ASSET/IMAGES/LARGE/A03C09460_0012.JPEG.

[52] N. Lim, J.S. Lee, Y.T. Byun, Negatively-doped single-walled carbon nanotubes decorated with carbon dots for highly selective NO2 detection, Nanomaterials 10 (2020) 2509, https://doi.org/10.3390/NANO10122509.

[53] L. Li, X. Yao, H. Li, Z. Liu, W. Ma, X. Liang, Thermal stability of oxygen-containing functional groups on activated carbon surfaces in a thermal oxidative environment, J. Chem. Eng. Jpn. 47 (2014) 21-27, https://doi.org/10.1252/JCEJ.13WE193.

[54] W. Chen, M. Gong, K. Li, M. Xia, Z. Chen, H. Xiao, Y. Fang, Y. Chen, H. Yang, H. Chen, Insight into KOH activation mechanism during biomass pyrolysis: chemical reactions between O-containing groups and KOH, Appl. Energy 278 (2020) 115730, https://doi.org/10.1016/j.APENERGY.2020.115730.

[55] H. Cui, S. Hu, T. Yang, L. He, K. Xu, L. Jiang, S. Su, Y. Wang, J. Xu, J. Xiang, Influence of pore structure and surface characteristics of balsa wood-derived activated carbon on hydrogen production from methane decomposition, Int. J. Hydrogen Energy 121 (2025) 1-13, https://doi.org/10.1016/j.IJHYDENE.2025.03.329.

[56] J.K. Ko, I.H. Park, K. Hong, K.C. Kwon, Recent advances in chemoresistive gas sensors using two-dimensional materials, Nanomaterials 14 (2024) 1397, https://doi.org/10.3390/NANO14171397.

[57] N.A. Travlou, M. Seredych, E. Rodríguez-Castellón, T.J. Bandosz, Insight into ammonia sensing on heterogeneous S- and N- co-doped nanoporous carbons, Carbon N. Y. 96 (2016) 1014-1021, https://doi.org/10.1016/j.carbon.2015.10.049.

[58] X. Wang, X. Li, Y. Zhao, Y. Chen, J. Yu, J. Wang, The influence of oxygen functional groups on gas-sensing properties of reduced graphene oxide (rGO) at room temperature, R. Soc. Chem. 6 (2016) 52339-52346, https://doi.org/10.1039/c6ra05659h.

[59] W.T. Jung, H.S. Jang, J.W. Jeon, B.H. Kim, Effect of oxygen functional groups in reduced graphene oxide-coated silk electronic textiles for enhancement of NO2 gas-sensing performance, ACS Omega 6 (2021) 27080-27088, https://doi.org/10.1021/ACSOMEGA.1C03658/SUPPL_FILE/A01C03658_S1_001.PDF.

[60] J. Wu, D. Zeng, S. Tian, Keng Xu, D. Li, Changsheng Xie, Competitive influence of surface area and mesopore size on gas-sensing properties of SnO 2 hollow fibers, J. Mater. Sci. 50 (n.d.), https://doi.org/10.1007/s10853-015-9339-8.

[61] X. Zhang, Z. Dong, S. Liu, Y. Shi, Y. Dong, W. Feng, Maize straw-templated hierarchical porous zno:ni with enhanced acetone gas sensing properties, Sensor. Actuator. B Chem. 243 (2017) 1224-1230, https://doi.org/10.1016/j.SNB.2016.12.076.

[62] A.A. Baharuddin, B.C. Ang, A.S.M.A. Haseeb, Y.C. Wong, Y.H. Wong, Advances in chemiresistive sensors for acetone gas detection, Mater. Sci. Semicond. Process. 103 (2019) 104616, https://doi.org/10.1016/j.MSSP.2019.104616.

[63] M. Shafiei, J. Bradford, H. Khan, C. Piloto, W. Wlodarski, Y. Li, N. Motta, Low-operating temperature NO2 gas sensors based on hybrid two-dimensional SnS2-reduced graphene oxide, Appl. Surf. Sci. 462 (2018) 330-336, https://doi.org/10.1016/j.APSUSC.2018.08.115.

[64] W.J. Yan, M. Hu, J.R. Liang, D.F. Wang, Y.L. Wei, Y.X. Qin, Preparation and room temperature NO2-sensing performances of porous silicon/V205 nanorods, Chin. Phys. B 25 (2016) 1-7, https://doi.org/10.1088/1674-1056/25/4/047072.

[65] W. Li, M. Shahbazi, K. Xing, T. Tesfamichael, N. Motta, D.C. Qi, Highly sensitive NO2 gas sensors based on MoS2@MoO3 magnetic heterostructure, Nanomaterials 12 (2022), https://doi.org/10.3390/nano12081303.

[66] H.S. Chiu, P.I. Lin, H.C. Wu, W.H. Hsieh, C.D. Chen, Y.T. Chen, Electron hopping conduction in highly disordered carbon coils, Carbon N. Y. 47 (2009) 1761-1769, https://doi.org/10.1016/j.carbon.2009.03.002.

[67] B.C. Bai, T. Bae, Pore Structure Control of Activated Carbon Fiber for CO Gas Sensor Electrode, 18, 2016, pp. 76-79, https://doi.org/10.5714/CL.2016.18.076.

[68] X. Yan, Y. Wu, R. Li, C. Shi, R. Moro, Y. Ma, L. Ma, High-performance UV-Assisted NO2 sensor based on chemical vapor deposition graphene at room temperature, ACS Omega 4 (2019) 14179-14187, https://doi.org/10.1021/acsomega.9b00935.

[69] S. Yu, H. Zhang, J. Zhang, Z. Li, Effects of pH on high-performance ZnO resistive humidity sensors using one-step synthesis, Sensors (Basel) 19 (2019) 5267, https://doi.org/10.3390/S19235267.

[70] V.K. Tomer, S. Duhan, A.K. Sharma, R. Malik, S.P. Nehra, S. Devi, One pot synthesis of mesoporous ZnO-SiO2 nanocomposite as high performance humidity sensor, Colloids Surfaces A Physicochem. Eng. Asp. 483 (2015) 121-128, https://doi.org/10.1016/j.COLSURFA.2015.07.046.

[71] H. Wang, L. Chen, J. Wang, Q. Sun, Y. Zhao, A micro gas sensor based on a nano sol-gel TiO2 thin film, Sensors (Switzerland) 14 (2014) 16423-16433, https://doi.org/10.3390/s140916423.

[72] H.K. Jeong, C. Yang, B.S. Kim, K.J. Kim, Valence band of graphite oxide, Europhys. Lett. 92 (2010) 37005, https://doi.org/10.1209/0295-5075/92/37005.

[73] X. Li, Y. Tang, W. Cheng, Y. Liu, H. Dong, X. Li, C. Shao, S. Fu, Y. Liu, Highly flexible all-inorganic nanofiber networks with stress-adcomming microstructure for light-activated wearable chemiresistive sensor, Chem. Eng. J. 455 (2023) 140768, https://doi.org/10.1016/j.CEJ.2022.140768.

[74] M. Jeguirim, M. Belhachemi, L. Limousy, S. Bennici, Adsorption/Reduction of nitrogen dioxide on activated carbons: textural properties versus surface chemistry - a review, Chem. Eng. J. 347 (2018) 493-504, https://doi.org/10.1016/j.cej.2018.04.063.

[75] S. Bashkova, D. Deoki, T.J. Bandosz, Effect of silver nanoparticles deposited on micro/mesoporous activated carbons on retention of NOx at room temperature, J. Colloid Interface Sci. 354 (2011) 331-340, https://doi.org/10.1016/j.JCIS.2010.10.031.

[76] R. Pietrzak, T.J. Bandosz, Activated carbons modified with sewage sludge derived phase and their application in the process of NO2 removal, Carbon N. Y. 45 (2007) 2537-2546, https://doi.org/10.1016/j.carbon.2007.08.030.

[77] W.J. Zhang, A. Bagreev, F. Rasouli, Reaction of NO2 with activated carbon at ambient temperature, Ind. Eng. Chem. Res. 47 (2008) 4358-4362, https://doi.org/10.1021/ie800249s.

[78] Y.W. Lee, J.W. Park, S.J. Jun, D.K. Choi, J.E. Yie, NOx adsorption-temperature programmed desorption and surface molecular ions distribution by activated carbon with chemical modification, Carbon N. Y. 42 (2004) 59-69, https://doi.org/10.1016/j.carbon.2003.09.019.

[79] P. Nowicki, M. Skrzypczak, R. Pietrzak, Effect of activation method on the physicochemical properties and NO2 removal abilities of sorbents obtained from plum stones (Prunus domestica), Chem. Eng. J. 162 (2010) 723-729, https://doi.org/10.1016/j.cej.2010.06.040.

[80] Z.H. Zhu, L.R. Radovic, G.Q. Lu, Effects of acid treatments of carbon on N2O and NO reduction by carbon-supported copper catalysts, Carbon N. Y. 38 (2000) 451-464, https://doi.org/10.1016/S0008-6223(99)00127-X.

[81] S. Rani, M. Kumar, P. Garg, R. Parmar, A. Kumar, Y. Singh, V. Baloria, U. Deshpande, V.N. Singh, Temperature-dependent n-p-n switching and highly
selective room-temperature n-SnSe2/p-SnO/n-SnSe heterojunction-based NO2Gas sensor, ACS Appl. Mater. Interfaces 14 (2022) 15381–15390, https://doi.org/10.1021/ACSAMI.1C24679/SUPPL_FILE/AM1C24679 SI 001.PDF.

[82] Y. Li, Z. Yuan, H. Zhu, B. Wang, H. Wang, F. Meng, Study on the impact of minor ambient temperature variations on the gas sensing performance of zinc cobaltate semiconductor metal oxide sensor for toluene detection, Sensor. Actuator. B Chem. 433 (2025) 137570, https://doi.org/10.1016/j.SNB.2025.137570.

[83] Z. Li, S. Yan, M. Sun, H. Li, Z. Wu, J. Wang, W. Shen, Y.Q. Fu, Significantly enhanced temperature-dependent selectivity for NO₂ and H₂S detection based on In₂O₃ nano-cubes prepared by CTAB assisted solvothermal process, J. Alloys Compd. 816 (2020) 152518, https://doi.org/10.1016/j.JALLCOM.2019.152518.

[84] W. Meng, X. Cheng, C. Guo, T. Wang, X. Dong, X. Zhang, C. Huang, Z. Major, L. Huo, Y. Xu, Temperature dependent dual functional SnO₂ sensor for Cl₂ and NO₂, Talanta 288 (2025) 127731, https://doi.org/10.1016/j.TALANTA.2025.127731.

[85] Y. Zhou, G. Liu, X. Zhu, Y. Guo, Ultrasensitive NO₂ gas sensing based on rGO/MoS₂ nanocomposite film at low temperature, Sensor. Actuator. B Chem. 251 (2017) 280–290, https://doi.org/10.1016/j.snb.2017.05.060.

[86] J.-H. Lee, T.-B. Nguyen, D.-K. Nguyen, J.-H. Kim, J.-Y. Kim, B.T. Phan, S.S. Kim, Gas sensing properties of Mg-Incorporated metal-organic frameworks. https://doi.org/10.3390/s19153323, 2019.

[87] M. Zhan, S. Hussain, T.S. AlGarni, S. Shah, J. Liu, X. Zhang, A. Ahmad, M.S. Javed, G. Qiao, G. Liu, Facet controlled polyhedral ZIF-8 MOF nanostructures for excellent NO2 gas-sensing applications, Mater. Res. Bull. 136 (2021) 111133, https://doi.org/10.1016/j.MATERRESBULL.2020.111133.

[88] C. Arul, K. Moulaee, N. Donato, D. Iannazzo, N. Lavanya, G. Neri, C. Sekar, Temperature modulated Cu-MOF based gas sensor with dual selectivity to acetone and NO2 at low operating temperatures, Sensor. Actuator. B Chem. 329 (2021) 129053, https://doi.org/10.1016/j.SNB.2020.129053.

[89] L. Yin, X. Chu, H. Chen, B. Liu, P. Zhang, L. Du, G. Cui, L. Lv, Room temperature NO2 sensing with a ZIF-67/rGO nanocomposite: a highly sensitive approach, J. Alloys Compd. 1021 (2025) 179621, https://doi.org/10.1016/j.JALLCOM.2025.179621.

[90] D. Pandey, T. Samarth, V.K. Verma, C. Patel, L. Ponvijayakanthan, N.K. Jaiswal, S. Mukherjee, A. Raghuvanshi, Two-dimensional Cu(I)-MOF with mesoporous architecture towards chemiresistive NO2 sensing, J. Mater. Chem. A 13 (2025) 11416–11424, https://doi.org/10.1039/D4TA07702D.

[91] X. Su, Z. Zhong, X. Yan, T. Zhang, C. Wang, Y.X. Wang, G. Xu, L. Chen, Facile synthesis of metallosalphen-based 2D conductive metal-organic frameworks for NO2 sensing: metal coordination induced planarization, Angew. Chem. Int. Ed. 62 (2023) e202302645, https://doi.org/10.1002/ANIE.202302645.

[92] W.T. Koo, S.J. Kim, J.S. Jang, D.H. Kim, I.D. Kim, Catalytic metal nanoparticles embedded in conductive metal-organic frameworks for chemiresistors: highly active and conductive porous materials, Adv. Sci. 6 (2019) 1900250, https://doi.org/10.1002/ADVS.201900250. PAGE:STRING:ARTICLE/CHAPTER.

[93] J.O. Kim, W.T. Koo, H. Kim, C. Park, T. Lee, C.A. Hutomo, S.Q. Choi, D.S. Kim, I. D. Kim, S. Park, Large-area synthesis of nanoscopic catalyst-decorated conductive MOF film using microfluidic-based solution shearing, Nat. Commun. 121 (2021) 1–8, https://doi.org/10.1038/s41467-021-24571-1.

[94] Y. Zhang, Q. Liu, Q. Sun, H. Li, J. Shen, H. Liu, W. Chen, Y. Zhang, Y. Chen, Metalloporphyrin-based metal-organic frameworks for the ultrasensitive chemiresistive detection of NO2: effect of the central metal on tuning the sensing performance, ACS Sens. 8 (2023) 4353–4363, https://doi.org/10.1021/ACSSENSORS.3C01740/ASSET/IMAGES/LARGE/SE3C01740 0006.JPEG.

[95] E.H. Kwon, H. An, M.B. Park, M. Kim, Y.D. Park, Conjugated polymer-zeolite hybrids for robust gas sensors: effect of zeolite surface area on NO2 sensing ability, Chem. Eng. J. 420 (2021) 129588, https://doi.org/10.1016/j.CEJ.2021.129588.

[96] Y.E. Hahm, S. Kweon, M.B. Park, Y.D. Park, Highly sensitive and selective organic gas sensors based on nitrided ZSM-5 zeolite, ACS Appl. Mater. Interfaces 15 (2023) 7196–7203, https://doi.org/10.1021/ACSAMI.2C18498/SUPPL_FILE/AM2C18498 SI 001.PDF.

[97] J.H. Park, S. Kweon, D. Jang, M.B. Park, B. Kang, Y.D. Park, Organic-transistor-based NO2Sensor fabricated with surface-modified faujasite-type zeolite as an efficient nanochannel for gas analytes, ACS Appl. Electron. Mater. 4 (2022) 3686–3693, https://doi.org/10.1021/acsaeml.2c00650.

[98] P. Varsani, A. Afonja, D.E. Williams, I.P. Parkin, R. Binions, Zeolite-modified WO3 gas sensors – enhanced detection of NO2, Sensor. Actuator. B Chem. 160 (2011) 475–482, https://doi.org/10.1016/j.SNB.2011.08.014.

[99] L.H.T. Nguyen, A. Mirzaei, J.Y. Kim, T.B. Phan, L.D. Tran, K.C.W. Wu, H.W. Kim, S. Kim, T.L.H. Doan, Advancements in MOF-Based resistive gas sensors: synthesis methods and applications for toxic gas detection, Nanoscale Horizons (2025), https://doi.org/10.1039/D4NH00662C.