# Application of Organic Light-Emitting Diodes and Photodiodes in Optical Control and Detection of Neuronal Activity

Marcin Kielar, Matthew Kenna, Philippe Blanchard, Pankaj Sah

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Marcin Kielar, Matthew Kenna, Philippe Blanchard, Pankaj Sah. Application of Organic Light-Emitting Diodes and Photodiodes in Optical Control and Detection of Neuronal Activity. Photonics, 2025, 12 (3), pp.281. 10.3390/photonics12030281. [hal-05057114](https://hal-05057114)

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Review

# Application of Organic Light-Emitting Diodes and Photodiodes in Optical Control and Detection of Neuronal Activity

Marcin Kielar<sup>1,2,*</sup> and Matthew Kenna<sup>2</sup>, Philippe Blanchard<sup>1</sup> and Pankaj Sah<sup>2,*</sup>

**Abstract:** Optical techniques to study neuronal activity have greatly advanced the field of neuroscience over recent decades. Multichannel silicon-based recording probes combined with optical fibers allow for simultaneous recording and manipulation of neuronal activity that underpins cognitive processes and behavior. The recent development of neural probes incorporating organic light-emitting diodes (OLEDs) and photodiode-based organic photodetectors (OPDs) offer additional advantages of biocompatibility, ultra-small footprint, multifunctionality, and low cost. These developments are ushering in a new generation of devices that are ideal for the interrogation of neuronal activity in vitro and in vivo. In this review, we discuss recent progress in OLED- and OPD-based neural probes, their applications in the optical control of neuronal function, and current challenges and prospects for the future.

**Keywords:** organic light-emitting diodes; OLED; organic photodetectors; organic photodiodes; OPD; neuronal activity; optical stimulation; optogenetics; calcium indicator; voltage indicator

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Received: 31 January 2025

Revised: 15 March 2025

Accepted: 15 March 2025

Published: 18 March 2025

**Citation:** Kielar, M.; Kenna, M.; Blanchard, P.; Sah, P. Application of Organic Light-Emitting Diodes and Photodiodes in Optical Control and Detection of Neuronal Activity.
*Photonics* **2025**, *12*, 281.

https://doi.org/10.3390/
photonics12030281

**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/).

## 1. Introduction

Mapping large-scale neural activity in the brain is crucial to understanding neuronal circuits that are involved in cognitive processes and behavioral outcomes, as well as to track neurological impairments such as Alzheimer's or Parkinson's diseases (PD) [1–6]. This knowledge is needed to develop new treatments for brain diseases and disorders. In the long term, these developments will lead to improvements in the overall quality of life of patients suffering from these illnesses. To progress our understanding of brain function, numerous neuroengineering techniques have been developed over past decades, ranging from electrical recordings to optical manipulation of neuronal activity that can be applied across many brain regions [7,8]. Multichannel silicon-based probes incorporating metals such as titanium and gold, as well as metal alloys (e.g., platinum–iridium), optical fibers, monochromatic light sources, and photodetection camera systems (CMOS- and CCD-based), and entire microscope setups have been developed to specifically image and optically control the activity of defined neural populations [9–11].

However, despite providing invaluable data and insights into the neural activity that underpins behavior and cognition, these techniques come with several restrictions, such as biocompatibility, invasiveness, and high cost. To tackle these limitations, neural probes based on organic optoelectronic devices have emerged as complementary tools to map neuronal activity with high temporal and spatial resolution [12–15]. This review summarizes recent progress in the development of optical neural probes, often referred to

<footnote n="1">1 MOLTECH-Anjou Laboratory, The University of Angers, CNRS, SFR MATRIX, F-49000 Angers, France; philippe.blanchard@univ-angers.fr</footnote>

<footnote n="2">2 Queensland Brain Institute, The University of Queensland, St Lucia, QLD 4072, Australia; matthew.kenna@uq.edu.au</footnote>

<footnote>* Correspondence: marcin.kielar@univ-angers.fr (M.K.); pankaj.sah@uq.edu.au (P.S.)</footnote>
as optoprobes, based on organic semiconductors: organic light-emitting diodes (OLEDs) and photodiode-based organic photodetectors (OPDs). The advantages of using organic electronics to study neuronal activity are discussed with an emphasis on device design, working principles, and key differences from current technologies. In addition, the practical applications of OLED and OPD technology applied in vitro and in vivo are discussed in chronological order. Finally, current challenges, such as flexibility and miniaturization, are examined in detail. Overall, this review aims to provide an understanding of the current capabilities of organic optoprobes, as well as the hurdles to their development, and prospects for the future.

## 2. Tracking Neuronal Activity

### 2.1. Communication: Action Potentials and Synapses

A nerve cell or neuron is the basic unit of the nervous system composed of a cell body (soma), branching extensions (dendrites), and a single long extension (axon) that branches into terminals (Figure 1a). A single neuron expresses a variety of ion channels across its cell membrane that allow positive or negative ions to flow into or out of the cell [16]. The relative ratio of these ions, extracellular to intracellular, and the permeability of each ion channel determines the membrane potential or voltage across the cell membrane [17]. The opening of cationic ion channels can initiate a rapid sequence of changes in this voltage and, once past a defined threshold, will trigger a nerve impulse (electrical signal) called an action potential (AP). This signal travels from the cell body down the length of the axon to the axon terminal. At the junction between two neurons (synapse), when an AP reaches the presynaptic terminal, a neurotransmitter (chemical molecule) is released into the space between the (presynaptic) axon terminal and the (postsynaptic) dendrite of a nearby neuron [18,19]. Neurotransmitters bind to specialized receptors on the postsynaptic cell, causing various ion channels that span the membrane to be opened. This allows positive or negative ions to travel through the channel, leading to changes in the membrane potential. A given neuron receives hundreds or thousands of excitatory or inhibitory inputs from neighboring cells, resulting in dynamic changes in membrane potential. When the membrane voltage reaches the threshold potential, an AP is triggered, propagating the signal along a neural network. Neuronal communication via bioelectrical signals is intricately regulated as a balance between these excitatory and inhibitory influences [20].

### 2.2. Mapping the Brain's Electrical Activity

A key finding in neuroscience was the recognition that the activity of highly structured neural networks consisting of large numbers of interconnected neurons is responsible for complex processes that are fundamental for behavior and survival. However, how these neural circuits process and represent information to elicit specific brain functions (such as the control of movement or the storage of information as memories) is just starting to be understood [21]. A central tenet of the neuroscientific approach to deciphering the code used by the brain is to record and/or manipulate neuronal activity during defined tasks. Single neuron electrical activity can be recorded by inserting a small electrode into a neuron, allowing direct measurement of intracellular neuronal activity (Figure 1a). Voltage-, current-, and patch-clamp techniques have been developed to track the activity of single neurons [22]. If the electrode is located externally from the neuron, in continuity with the extracellular space, single-unit or multi-unit extracellular activity can be tracked, depending on the electrode size and geometry of the system. For large electrodes, only the net activity of many cells is recorded, often referred to as local field potential (LFP). One can note here that with the support of many electrodes, individual cells can still be identified using a process called spike sorting (Figure 1b) [23]. Historically, these extracellular recording
techniques were developed to record individual or, at most, several neurons. These probes have now advanced to house many electrical contacts, allowing simultaneous recordings from hundreds of neurons (e.g., silicon neural probes) [10,24]. Stimulating neural probes have also been engineered, with the most notable example being platinum/iridium-based electrodes that can also be implanted into humans to provide therapeutic benefits in a procedure called Deep Brain Stimulation (DBS) [25]. DBS was initially implemented for the treatment of PD and is now routinely used to treat a range of movement disorders, such as essential tremor, dystonia, and others [26]. More recently, DBS electrodes are also being used to treat some psychiatric disorders [27–29].

<figure><img src="image_3.png" /><figcaption>**Figure 1.** Electrical and optical approaches for studying neuronal activity. (a) Representation of a neuronal circuit depicting the neuron's soma, dendrites, axon, and terminal. Neurons communicate by propagating electrical signals called action potentials (APs), and an intracellular electrode (patch pipette) is placed to record this activity via a patch-clamp method. (b) Neuronal firing (spiking activity) and collective oscillatory dynamics (local field potential) can also be detected extracellularly with a recording electrode being surrounded by multiple neurons. (c) Optical approaches for mapping neuronal activity in which fluorescent molecules or light-gated ion channels are introduced to render neurons responsive to light. In neurons, an AP is accompanied by a rapid influx of Ca²⁺ ions. The addition of molecules that respond to the binding of Ca²⁺ ions via fluorescence can thus be used to monitor the electrical activity using a light detector. Light-gated ion channels act as a photoswitch controlling the membrane potential and thus neuronal activity.</figcaption></figure>

## 2.3. *Optical Interrogations of Neural Circuits*

While electrodes provide high temporal resolution and directly measure electrical activity, they are limited in their ability to selectively record or manipulate neuronal activity within specific neural populations. However, the advent of genetic tools for identifying
neuronal subtypes and pathways has enabled the introduction of genetically encoded calcium indicators (GECIs), such as GCaMP6, into genetically-defined neurons [30]. GECIs are fluorescent proteins that undergo a change in their fluorescence intensity in response to fluctuations in cytosolic calcium levels, providing a reliable proxy for neuronal activity, as action potentials (APs) are closely associated with an influx of calcium ions into the cell. To visualize these fluorescence changes, GECIs must be excited with light of a specific wavelength, traditionally using a fluorescence microscope or high-powered laser. By coupling this with high-speed cameras, researchers can track activity in specific neurons both in vitro and in vivo (Figure 1c).

In parallel, light-responsive proteins, known as opsins, have been developed to use light to control the electrical activity of neuronal populations. These so-called “optogenetic” proteins are genetically inserted into the neuronal membrane and respond to specific wavelengths of light by allowing the flow of charged ions into or out of the cell, thus controlling neuronal activity (Figure 1c). For example, Channelrhodopsin-2 (ChR2) is an ion channel that opens in response to blue light, leading to depolarization and the initiation of action potentials in the targeted neurons. With these tools, it is possible to monitor and manipulate the neuronal activity of defined subsets of neurons to understand their function [31].

Although the development of calcium imaging and optogenetics has revolutionized the study of the brain, their practical implementation requires a suite of advanced optical equipment. For example, using optogenetic approaches to manipulate neuronal activity requires the insertion of high-grade optical fibers that deliver light to the targeted cell population. Similarly, recording neuronal activity through fluorescent markers involves the relay of spectrally separated fluorescence emissions to external detectors for filtering and digitalization. Notably, the insertion of large fiber optic implants and camera systems results in damage to the overlying brain tissue, which is often aggravated by low levels of biocompatibility. Similarly, when recordings are made in awake behaving animals, the presence of the tether, together with the optical cable, can induce motion artefacts in the camera images that limit signal fidelity [32]. This can also produce micro-motions that are detrimental to the neuronal interface due to glial scarring [33]. Lastly, the cameras generally used to record the activity of GECI-expressing cells operate at normal video rates (33 to 50 Hz) and are unable to record with the higher temporal fidelity required for more advanced imaging. For example, recording neuronal activity through the burgeoning approach of using genetically encoded voltage indicators (GEVIs) requires the ability to detect small fluorescence changes over very short timescales (in the kHz range) [34]. Thus, advanced camera systems are necessary to benefit from the resolution that GEVIs provide, which has slowed the uptake of this approach in neuroscience. Finally, the sheer size of these implantable devices means that typically only a single device can be inserted in one or a few brain regions. As such, these imaging techniques are limited in their capacity to provide information at the level of the whole brain.

## 2.4. Complementary Role of Magnetic Resonance Imaging

To record neuronal activity from the whole brain, functional magnetic resonance imaging (fMRI) has been developed as a complementary tool to electrical and optical interrogations of neural circuits. It enables monitoring of neuronal activity from subjects over long temporal spans, providing a general guide to the brain regions active during behavioral tasks [35]. Notably, fMRI does not provide information about specific neural activity, and combining fMRI with direct neural recordings would be desirable. However, the aforementioned silicon neural probes and camera-based imaging techniques are not compatible with fMRI [36]; the commercially available probes lead to imaging artefacts
due to distortion of the magnetic field [37,38], which is caused by high concentrations of paramagnetic metals. This is unfortunate, as the fusion of calcium imaging, optogenetics, and fMRI would provide deep insights given the complementary spatiotemporal properties of these techniques. To synchronize the fMRI imaging with optoelectrical signals in the brain, fMRI-compatible electrodes that align with the susceptibility of their environment would be highly desirable.

## 2.5. *Advantages of Organic Neural Probes*

### 2.5.1. Optoelectronic Performance

To address the above limitations, a new type of neuroengineering optoelectronic device based on soft and flexible organic components has begun to be optimized for neuroscience applications. After the discovery of conductive plastics resulting from the doping (oxidation) of π-conjugated polymers [39], the subsequent development of organic semiconductors associated with π-conjugated systems in their neutral states has led to rapid growth in the field of organic electronics. This has led to the development of next-generation organic solar panels (organic photovoltaic cells, OPVs) [40] and reliable organic light-emitting diodes (OLEDs) [41]. OLED technology has progressed into the commercial landscape and is widely used in smartphones, television displays, and light sources for general lighting and automotive applications. Organic photodiodes (OPDs) [42] and transistors (OFETs) [43] have also shown potential in biosensing applications by converting a biological response into an electrical signal (e.g., pulse oximeter) [44,45]. Importantly, these devices not only demonstrate detection speeds up to the kHz and MHz ranges [46] but also feature a detectivity level that rivals low-noise silicon photodiodes [47,48]. The electronic noise of the photodetector signal sets the device sensitivity and defines the limit of what can be detected, that is, the minimum incident light power at which the signal-to-noise ratio (SNR) is unity [49]. This threshold is called the noise equivalent power (NEP), and a smaller NEP corresponds to a more sensitive detector. Many mechanisms have been reported as sources of electronic noise inside organic photodiodes, and more importantly, these sources are frequency-dependent [49,50]. The reciprocal of the NEP normalized per square root of the sensor's area and frequency bandwidth reflects the device-specific detectivity ($D^*$) and allows direct comparison of different devices in terms of sensitivity [51]. A higher $D^*$ corresponds to a more sensitive detector. Whether it is for chemical, biological, or photosensing applications, low-noise next-generation optoelectronic sensors largely benefit from functionally engineered conjugated organic semiconductors [52–54].

### 2.5.2. Biocompatibility, Flexibility, and MRI-Compatible

Importantly, these carbon-based materials have the additional advantage of biocompatibility when compared to inorganic devices. It has been observed that metal electrodes implanted in the brain trigger an inflammatory and wound-healing response in surrounding neurons and glial cells, including astrocytes and microglia [55]. Therefore, probes based on organic materials are ideal candidates to mitigate the inflammatory response and improve recording performance in vivo [56,57]. Organic semiconductor materials are easily color-tunable (i.e., they can be tuned to sense and emit different wavelengths of light) [58–61], resulting in a versatile suite of organic materials that can align with the array of proteins used in neuroscience investigations [62]. Furthermore, the complete probe can be deposited on flexible substrates with a minimal thickness of 1.5 µm [63] or even become substrateless [64]. The use of ultra-thin plastic substrates allows the probe to be flexible and/or stretchable, substantially minimizing the invasiveness of the approach. Indeed, it has been found that rigid electrodes based on metal or glass are subject to micromotions that can be detrimental to the neuronal interface due to glial scarring [33,65]. Although
metal electrodes are still present in many organic optoelectronic devices (e.g., the cathode is made of silver, gold, or aluminum), they are classed as non-magnetic materials, and their thickness is usually below 200 nm. OLEDs featuring these ultrathin and nonmagnetic metals generate no artefact under MRI and a minimal signal loss [66], which gives them significant advantages for applications in neuroimaging. In addition, the presence of thin metal layers in OLEDs and OPDs does not hinder their biocompatibility, as these light-emitting and -absorbing devices can be entirely encapsulated using polymeric dielectric materials such as parylene [66].

### 2.5.3. Multifunctionality

Organic semiconductors are able to incorporate two or more distinct optoelectronic functions in the same device, which is a significant improvement over conventional optogenetic systems. To simultaneously record and manipulate neuronal activity, multiple light sources and camera systems are typically needed, each emitting or being sensitive to a particular wavelength of light [67]. In case of spectral overlap and crosstalk, additional cut-off filters, amplification lenses, or noise-reduction systems are used, which contributes to the complexity of the experimental equipment. As such, the most appealing advantage of OLEDs and organic photodiodes in the context of optogenetic approaches is their ability to provide fine spatial optical control of neurons in a lens-free configuration [15,68]. Organic electronics not only provide stable, ultra-narrowband, low-power light sources [69,70] but can also offer dual-color emission or detection within a single device or with two OLEDs stacked [71–74]. Finally, single devices featuring both OLED and OPD functions, as well as OPV (organic solar cell) functions, have been suggested [75–77]. Such all-in-one systems offer the potential of optically manipulating and recording neuronal activity while maintaining the ease of fabrication and ultra-small footprint. State-of-the-art OPVs demonstrate power conversion efficiencies exceeding 20% [78]. The addition of the OPV function could lead to wireless, solar-powered optoelectronic systems for in vivo studies [79].

Multifunctional organic neural probes incorporating OLEDs and OPDs could also host ultrathin metal or polymeric electrodes (bioelectrodes), enabling both optical and electrical interrogations of the brain (Figure 2). Organic conducting polymers such as poly(3,4-ethylenedioxythiophene) (PEDOT:PSS) could be used as an additional coating, which has been shown to greatly reduce electrode impedance and enhance inflammatory stability [80,81]. Metal-free polymeric bioelectrodes based on PEDOT:PSS for neural recordings in vivo have also demonstrated an adequate signal-to-noise ratio and good stability [81,82]. Taken together, due to the unique properties of organic materials and fabrication methods, multifunctional neural probes offering bioelectrical stimulation and recording, as well as photoexcitation and photodetection, are on the verge of being applied to the study of the brain.
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Illustration of a multifunctional fMRI-compatible organic neural probe. The probe incorporates organic light-emitting diodes (OLEDs), organic photodiodes (OPDs), and organic bioelectrodes. Such a probe has the potential to be fMRI-compatible, i.e., functional under the magnetic field, and given its dimensions, to electrically and optogenetically track and manipulate individual neurons with high accuracy. Owing to organic materials, the probe footprint and invasiveness are significantly reduced, and the probe can be flexible and elastic.</figcaption></figure>

## 2.6. Applications of OLEDs and OPDs in Mapping Neuronal Activity

### 2.6.1. Organic Light-Emitting Diodes

OLEDs are ultra-thin light-emitting devices in which an emissive organic layer (EML) is embedded between interlayers and two electrodes (Figure 3a) [83]. Devices are driven using direct current (DC), and when power is supplied, positive charges (holes) injected from the anode migrate to the cathode, while electrons injected from the cathode migrate to the anode [84]. Low and high work function (WF) electrodes are needed for the effective injection of electrons and holes, respectively. Before reaching the emissive material, positive and negative charges travel through a series of thin organic interlayers that assist their movement and improve the OLED efficiency by preventing non-radiative losses and short circuits [83–85]. Depending on the device architecture, one can utilize hole and electron injection layers (HILs and EILs, respectively), hole and electron transport layers (HTLs and ETLs, respectively), and electron and hole blocking layers (EBLs and HBLs, respectively) for this purpose. The highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels of these interlayers are crucial for enhancing charge transport from electrodes to the EML [86,87]. Typically, a cascade energy level alignment of these interlayers for holes (HOMO levels) and electrons (LUMO levels) is preferred (Figure 3b). Hole and electron pairs (excitons) formed in the EML can recombine from a high-energy state to a ground state, resulting in the emission of light. One of the two electrodes and the associated interlayers must remain highly transparent or ultra-thin for the light to escape the device and reach neurons expressing light-gated proteins. Interlayers are generally thinner than 50 nm, and the overall OLED structure is usually less than 500 nm thick, with the substrate—often transparent—typically being the thickest part.
<figure><img src="image_5.png" /><figcaption>**Figure 3.** OLED systems used to optically control neuronal function. (a) Typical OLED device structure consisting of an emissive layer (EML) embedded between interlayers and two electrodes. Interlayers improve the OLED optoelectronic parameters (efficiency and luminance), while one of the electrodes must remain transparent to let light out in the directions of neurons expressing light-sensitive proteins. (b) A flat energy band structure illustrating photon generation in OLEDs. (c) Chemical structures of organic emissive materials used in OLEDs to study and control neuronal activity. (d) Selected emission spectra of OLEDs used in neuroscience studies demonstrating color-tunability. Adapted from the references shown as numbers. (e) Excitation spectra of common light-sensitive proteins used to study neuronal function. Adapted from the references in (d) and from supplier datasheets. OLEDs are selected to match the excitation spectra of these proteins.</figcaption></figure>

OLEDs offer high tunability, and the selection of an organic material for the EML (Figure 3c) as well as the overall device architecture will determine the device's emission spectrum (Figure 3d) [88,89]. Early efforts in stimulating neuronal activity focused on designing blue OLED systems capable of exciting the first family of light-gated ion channels, Channelrhodopsin-1 (ChR1) and Channelrhodopsin-2 (ChR2), with their excitation spectra peaking at 490 and 460 nm, respectively (Figure 3e). To target single cells of *Chlamydomonas reinhardtii*, cultured HEK-293 cells, or cortical neurons in vitro, organic OLEDs relied on an inorganic support, either a silicon-based complementary metal-oxide semiconductor (CMOS) backplane [68,90] or a thin film transistor (TFT) backplane [91,92]. These are necessary to drive the microarrays of OLEDs efficiently without resorting to supplying hundreds of pixels individually, which would be inconceivable given geometric and
electronic constraints. OLED devices have also been developed on silicon (Si) substrates, acting as surrogates for future implementation on silicon CMOS chips to allow optogenetic control of retinal cells [93]. Without being “completely organic”, most of these OLED-based hybrid systems have proven efficient in controlling the activity of single live cells and in optogenetic manipulation of individual neurons.

OLED systems with completely organic materials began to emerge from 2016 in a series of in vitro and fluorescence imaging studies that demonstrated their ability to excite common opsins used in neuroscience. Aside from being able to stimulate ChR1 and ChR2, blue OLEDs based on TBPe:MADN emissive systems (Figure 3c) were optimized to activate high-current channelrhodopsin CheRiff (excitation peak 465 nm) in primary cultured neurons [14]. Importantly, the authors also showed that OLED devices were stable across large numbers of stimulation pulses, indicating that such devices offer the durability required for neuronal investigations. A similar OLED architecture has been used to visualize the expression of enhanced green fluorescent protein eGFP (excitation peak 490 nm) in a live culture of fibroblasts [94]. The same OLED device was used as a light source for fluorescence microscopy and calcium imaging, targeting GCaMP6s, a highly sensitive GECI with an excitation peak nearing 500 nm (Figure 3e). Further into the 500 nm range, cultured cortical neurons expressing a green-light sensitive chimeric channel-rhodopsin (C1V1tt) (excitation peak 530 nm) were successfully stimulated in vitro using a green source OLED [95].

While blue and green OLEDs offer significant versatility in neuroscience studies, red-shifted opsins are increasingly used since red light maximizes the penetration depth of the excitation light source into the tissue. To this effect, OLEDs based on the PDY-132 (Super Yellow) emissive compound (Figure 3c) have been shown to be capable of stimulating cultured hippocampal neurons expressing the red-shifted opsin, Chrimson (excitation peak 590 nm) [15,96]. Critically, the Super Yellow OLED device did not require a conventional optical system to activate neuronal activity and was able to triangulate the location of a single neuron when using a multi-OLED approach [15]. In recent years, neuroscience studies have adopted an approach of using multiple opsins in the same neuron, which allows bidirectional modulation of neuronal activity (i.e., activation and/or inhibition) upon photostimulation. Blue and orange OLEDs based on PFO and MDMO-PPV (Figure 3c) have been used for photoactivation of the opsin SSFO (excitation peak at 460 nm) and ChrimsonR (590 nm) in the same neuronal preparation [97]. Similarly, dual-color OLEDs capable of switching between blue and red, or blue and green light were reported to elicit both optogenetic inhibition and excitation in cell cultures [12]. To achieve this, the device featured stacked OLEDs based on MADN:TBPe (blue), Ir(MDQ)₂(acac) (red) and Ir(ppy)₂(acac) (green) emissive materials (Table 1). Such dual blue-red OLEDs have been used to selectively stimulate GtACR2 (excitation peak 470 nm) and Chrimson, while dual blue-green devices could reliably stimulate cells expressing GtACR2 and ChRmine (520 nm) opsins [12] (Figure 3e). Overall, these proof-of-principle studies in simple neuronal systems have demonstrated that OLED devices are adaptable to a wide range of light-based tools used to investigate neuronal function.
<table><caption>**Table 1.** OLED- and OPD-based optical systems to map neuronal activity. Where possible, OLED emissive material is shown.</caption><thead><tr><th>Year</th><th>Device</th><th>Active/Emissive Material</th><th>Dominant Wavelengths</th><th>Study Type/Biological Media</th><th>Targeted Indicators</th><th>Features</th><th>Refs.</th></tr></thead><tbody><tr><td>2011</td><td>OPD</td><td>rr-P3HT:PC<sub>61</sub>BM</td><td>525–550 nm</td><td>Cultured hippocampal neurons (in vitro)</td><td>-</td><td>Bioorganic interface</td><td>[98]</td></tr><tr><td>2014</td><td>OLED</td><td>n/a</td><td>455 nm</td><td>-</td><td>ChR2</td><td>Flexible TFT-based OLED display</td><td>[91]</td></tr><tr><td>2015</td><td>OLED</td><td>n/a</td><td>475 nm</td><td>Single cells of Chlamydomonas reinhardtii</td><td>ChR1, ChR2</td><td>CMOS backplane</td><td>[90]</td></tr><tr><td>2016</td><td>OLED</td><td>n/a</td><td>475 nm</td><td>Cultured HEK-293 cells (in vitro)</td><td>ChR2, EYFP, mCherry</td><td>CMOS backplane</td><td>[68]</td></tr><tr><td>2016</td><td>OLED</td><td>TBPe:MADN<br>Ir(ppy)<sub>3</sub><br>Ir(MDQ)<sub>2</sub>(acac)</td><td>464 nm<br>515 nm<br>606 nm</td><td>Locomotor behavior of Drosophila melanogaster (in vivo)</td><td>ChR2</td><td>Irradiance: 250–400 µW mm<sup>-2</sup></td><td>[99]</td></tr><tr><td>2016</td><td>OLED</td><td>n/a</td><td>455 nm, (620 nm)</td><td>Cultured cortical neurons (in vitro)</td><td>ChR2, YFP, (Chrimson)</td><td>TFT-based OLED display</td><td>[92]</td></tr><tr><td>2016</td><td>OLED</td><td>TBPe:MADN</td><td>465, 493 nm</td><td>Behavioral changes of Drosophila melanogaster</td><td>ChR2, (H134R)</td><td>Irradiance: 10 µW mm<sup>-2</sup></td><td>[100]</td></tr><tr><td>2018</td><td>OPD</td><td>P3HT:PC<sub>61</sub>BM</td><td>Visible (white)</td><td>Changes in transmittance in mice slices (ex vivo)</td><td>-</td><td>Halogen light source</td><td>[101]</td></tr><tr><td>2019</td><td>OLED</td><td>TBPe:MADN</td><td>460 nm</td><td>Fixed tissue slices, live cells and preparations of D. melanogaster</td><td>eGFP, GCaMP6s</td><td>Narrowband OLEDs</td><td>[94]</td></tr><tr><td>2019</td><td>OLED</td><td>PFO, SO-PPV & others</td><td>469 nm<br>573 nm</td><td>Cultured hippocampal neurons (in vitro)</td><td>ChrimsonR SSFO</td><td>Irradiance: 100–150 µW mm<sup>-2</sup></td><td>[97]</td></tr><tr><td>2019</td><td>OLED</td><td>MADN:TBPe</td><td>463 nm</td><td>Cultured primary neurons (in vitro)</td><td>CheRiff</td><td>High-power and stable OLEDs. Irradiance: 60–800 µW mm<sup>-2</sup></td><td>[14]</td></tr><tr><td>2020</td><td>OLED</td><td>n/a</td><td>455 nm<br>520 nm</td><td>Cultured cortical neurons (in vitro), and transgenic mouse</td><td>ChR2, C1V1tt</td><td>Irradiance: 1000 µW mm<sup>-2</sup></td><td>[95]</td></tr><tr><td>2020</td><td>OLED</td><td>n/a (STSB010)</td><td>455 nm</td><td>Transgenic rat</td><td>ChR2</td><td>Flexible, MRI-compatible OLEDs. Irradiance: 500 µW mm<sup>-2</sup></td><td>[66]</td></tr><tr><td>2020</td><td>OLED</td><td>TBPe:MADN</td><td>460–560 nm</td><td>Drosophila melanogaster</td><td>CsChrimson, GtACR2</td><td>Locomotor behavior. Irradiance: 15–30 µW mm<sup>-2</sup></td><td>[102]</td></tr></tbody></table>
<table><caption>**Table 1. Cont.**</caption><thead><tr><th>Year</th><th>Device</th><th>Active/Emissive Material</th><th>Dominant Wavelengths</th><th>Study Type/Biological Media</th><th>Targeted Indicators</th><th>Features</th><th>Refs.</th></tr></thead><tbody><tr><td>2021</td><td>OPD</td><td>Rubrene/C<sub>60</sub></td><td>400–575 nm</td><td>Cultured cortical neurons (in vitro)</td><td>Cal-520</td><td>Direct detection of neuronal activity<br>Sensitivity: 0.5–20 nW cm<sup>-2</sup></td><td>[13]</td></tr><tr><td>2022</td><td>OLED</td><td>Ir(MDQ)<sub>2</sub>(acac)</td><td>600 nm</td><td>(Retinal cells)</td><td>(ChrimsonR)</td><td>Tandem-stack architecture, silicon substrate</td><td>[93]</td></tr><tr><td>2022</td><td>OLED</td><td>Super-Yellow</td><td>556 nm</td><td>Cultured hippocampal neurons (in vitro)</td><td>Chrimson</td><td>Spatial localization of neurons.<br>Irradiance: 10–38 µW mm<sup>-2</sup></td><td>[15]</td></tr><tr><td>2022</td><td>OLED</td><td>TBPe:MADN<br>Ir(ppy)<sub>2</sub>(acac)<br>Ir(MDQ)<sub>2</sub>(acac)</td><td>462 nm<br>557 nm<br>620 nm</td><td>ND7/23 cells and neurons in Drosophila melanogaster</td><td>ChRmine, GtACR2<br>&amp; Chrimson<br>(BiPOLEs)</td><td>OLED bicolor emission.<br>Irradiance: 1–55 µW mm<sup>-2</sup></td><td>[12]</td></tr><tr><td>2023</td><td>OLED</td><td>TBPe<br>Ir(MDQ)<sub>2</sub>(acac)</td><td>460, 500 nm<br>620 nm</td><td>Transgenic mice (in vivo)</td><td>ChR2<br>ChRmine</td><td>CMOS-based</td><td>[103]</td></tr><tr><td>2023</td><td>OLED<br>OPD</td><td>Rubrene/C<sub>60</sub><br>Super-Yellow</td><td>400–575 nm<br>556 nm</td><td>Cultured cortical and hippocampal neurons (in vitro)</td><td>Cal-520, Chrimson</td><td>Direct detection and stimulation</td><td>[96]</td></tr><tr><td>2024</td><td>OLED</td><td>MADN:TBPe<br>Ir(MDQ)<sub>2</sub>(acac)</td><td>460 nm<br>607 nm</td><td>Localized stimulation of Drosophila melanogaster</td><td>GtACR2 &amp; Chrimson<br>(BiPOLEs)</td><td>Dual-color OLEDs; behavioral change.<br>Irradiance: 134–238 µW mm<sup>-2</sup></td><td>[72]</td></tr></tbody></table>
Although OLED devices have proven to be powerful tools for in vitro studies, their
significant contribution to neuroscience is through applications to in vivo investigations.
Early work harnessing OLED technology in vivo has involved using either fruit flies
*Drosophila melanogaster* [12,72,99,100,102] or transgenic rodents [66,95,103] as model sys-
tems. In *Drosophila* larvae, OLED devices have been used to excite ChR2-expressing motor
neurons responsible for locomotion, altering head and tail trajectory [99], as well as contrac-
tion of the larvae [100]. With the miniaturization of OLED technology, small linear arrays
of OLEDs have been developed, where light can be delivered in a more spatially controlled
manner [102]. Such devices have been employed to target specific regions of *Drosophila*,
providing insight into the functioning of the *Drosophila* neural system [102]. Similar to
the aforementioned approach in vitro, dual blue-red OLED systems have been used to
target activation and inhibition of motor neurons in *Drosophila* larvae expressing Chrimson
and GtACR2 opsins [12]. Recently, by combining linear arrays of miniaturized OLEDs
with dual-color capability, researchers have developed a powerful tool to provide segment-
specific modulation of neuronal activity [72]. These devices enable simultaneous multi-site
activation and inhibition, as well as the ability to switch between colors without the need
for complex optical elements. It is worth noting that these miniaturized dual-color OLED
systems could, in principle, be applied to other small model organisms or brain slices.

In other model systems, OLEDs have been reported to evoke an optical stimulation-
locked response in a ChR2-expressing transgenic mouse model [95]. In a proof-of-concept
experiment, at least three single neuronal units recorded in awake animals were found
to respond to blue OLED stimulation. Recently, an implantable deep-brain light delivery
system based on OLED technology has been proposed to selectively target brain regions
as deep as 5 mm [103]. The neural probe relied on a CMOS backplane to access 1024
individually addressable OLEDs with a 24.5 µm pitch. Orange and blue neural probes based
on Ir(MDQ)₂(acac) and MADN:TBPe (Figure 3c) were fabricated to selectively activate
individual ChRmine- and ChR2-expressing neurons with millisecond-level precision in
mice. Although showing superior performance, OLED integration on CMOS significantly
increases the complexity of fabrication, as well as the probe thickness and invasiveness. In
addition, these devices are unable to benefit from the main advantages of OLEDs, such
as flexibility and fMRI-compatibility. To tackle this, an alternative approach has been
demonstrated with ultrathin (2 µm), ultraflexible OLEDs, which have been shown to
activate ChR2-expressing neurons in rats [66]. Furthermore, optical stimulation from this
thin blue OLED configuration to nerve fibers induced contractions of the downstream
innervated muscles. A minimal footprint device design reduces the mechanical damage
from implantation compared to inorganic counterparts [33]. Importantly, as this optoprobe
was MRI compatible, neuronal activity induced by direct OLED stimulation of the brain
could also be visualized using MRI. Taken together, these first in vivo studies have shown
that organic-based light sources offer considerable advantages in a range of experimental
designs and animal models.

2.6.2. Organic Photodiodes

As OLEDs aim to replace conventional light sources, fiber optics, and lenses, photodiode-based OPDs have the potential to replace CMOS and CCD camera systems in fluorescence imaging studies. Their typical device structure consists of an organic active layer nestled between two interlayers and two electrodes [50] (Figure 4a). The active layer consists of electron donor (ED) absorbing photons and generating excitons (electron-hole pairs) and an electron acceptor (EA) assisting in photo-induced electron transfer and exciton dissociation. Although not shown in Figure 4a, we note that the EA also features an absorption spectrum that also contributes to photon absorption, resulting in exciton
generation [104]. Interlayers, typically hole and electron blocking layers—HBLs and EBLs, respectively—improve the OPD efficiency by reducing parasitic noise referred to as dark current [105]. WFs of electrodes, as well as HOMO and LUMO levels of organic materials, are carefully selected for efficient photocurrent generation [48] (Figure 4b). As with OLEDs, one of the electrodes must remain transparent to let light in for successful photodetection. The OPD thickness is typically below 400 nm, with the total thickness limited by the choice of substrate.

<figure><img src="image_6.png" /><figcaption>**Figure 4.** OPD systems used to optically control or visualize neuronal function. (a) Typical OPD device structure consisting of an active layer sandwiched between interlayers and two electrodes. The active layer consists of an electron donor (ED) and an electron acceptor (EA). Interlayers improve the OPD efficiency while one of the electrodes must remain transparent to let light in for successful photodetection. (b) A flat energy band structure illustrating photon absorption in OPDs. Both EDs and EAs can absorb light generating excitons in both materials, leading to efficient charge dissociation at the ED/EA interface. The case of the absorption of light by the ED is described as follows: (1) light emitted by a fluorescent marker is absorbed by the ED and an exciton (electron-hole pair) is created, (2) exciton diffusion toward the ED/EA interface, (3) charge transfer and charge dissociation of the exciton, (4) free charge collection at electrodes, (5) to accelerate charge collection, the OPD is typically negatively biased, holes are attracted to a negative charge at the anode, electrons are attracted to a positive charge at the cathode. Hole and electron blocking interlayers (HBLs and EBLs) are often added to reduce dark current. (c) Chemical structures of organic materials used as active layers in OPDs to study neuronal activity. (d) Responsivity spectra of common OPDs applied to neuroscience studies. Adapted from [98,106]. (e) Emission spectra of common calcium indicators used in neuroscience to record neuronal activity. Adapted from supplier datasheets.</figcaption></figure>

An early example of an OPD for neuronal photodetection consisted of a hybrid bioorganic interface based on P3HT and PCBM (Figure 4c) [98], which was able to record activity from a network of cultured primary neurons. This was an important stepping stone for further work moving toward the development of an artificial retina based on organic materials [107]. Similarly, OPDs with the same active layer were used to demonstrate the detection of a wide range of natural physiological activities and pathophysiological events, such as epileptiform activity in mouse brain slices (ex vivo). The OPD featured a wide responsivity spectrum (Figure 4d) to measure transmittance changes through brain slices
using a halogen (white) light source [101]. These changes are caused by cellular volume variations due to ion fluxes in and out of the neuronal network's cells.

The use of calcium imaging techniques to record neuronal activity can also be augmented by supplanting traditional camera systems with OPDs. In a set of in vitro experiments on cultured cortical neurons loaded with the fluorescent calcium indicator Cal-520 (Figure 4e), organic photodiodes based on rubrene and $C_{60}$ (Figure 4c) could reliably detect electrically evoked fluorescent activity with high fidelity and signal-to-noise ratio [13]. With the detection threshold measured at 500 pW cm$^{-2}$, the fabricated photodiodes could directly detect changes in fluorescent neuronal activity as low as 2.3 nW cm$^{-2}$. This OPD configuration was also able to record time-locked spontaneous fluorescent transients, demonstrating adequate sensitivity for the detection of physiological events.

Although calcium imaging is currently the most prominent method for the optical mapping of brain activity, the $Ca^{2+}$ photoresponse is only an indirect measure of neuronal electrical activity since the measurements are performed at low temporal resolution [108]. GECI fluorescence signals typically feature slow decay times (in the order of seconds), while action potentials (APs) operate at millisecond time scales, which cannot be resolved using this technique. To address this limitation, a novel technique of voltage sensing using genetically encoded voltage indicators (GEVIs) has recently been developed [34]. These indicators directly measure the change in voltage inside a neuron, which has been shown to possess a temporal resolution between 50 ms and 1 ms [109]. To date, there have been no attempts to combine GEVIs with OPDs, but a proof-of-concept study using rubrene-based organic photodiodes has demonstrated that these devices have the ability to track ultra-low light pulses at high frequencies [96]. By taking advantage of a novel method of light detection [106], these devices would, therefore, be compatible with GEVIs. Taken together, these first demonstrations and in vitro studies have shown that organic photodiodes offer complementary advantages to join the OLED technology in a range of experimental designs to map neuronal function using light-sensitive proteins as well as GECIs and GEVIs.

### 3. Current Challenges and Future Directions

The preliminary efforts in applying organic technology to neuroscience have shown that OLEDs and OPDs offer significant advantages to current optical systems. However, the number of research articles in this field remains limited (Figure 5a), especially for OPDs designed to detect neuronal activity (Figure 5b). Initially developed for other types of applications, OLEDs and OPDs need to be adapted to match the specific criteria required for optogenetic stimulation and optical mapping of neuronal activity in vivo: miniaturization, flexibility, and biocompatibility. This creates several challenges that are currently preventing the uptake of this technology in the field of neuroscience.

#### 3.1. *Flexibility of Implantable Neural Probes*

One of the current limitations reflects the use of glass or silicon (hybrid) substrates (Figure 5c) in organic devices, which are incompatible with the brain for in vivo studies. The major barrier to the switch to flexible substrates is the significant increase in the difficulty of fabrication [110,111]. Typically, fabricating organic devices on flexible substrates requires low-temperature processing, which is hard to achieve with standard vacuum processing techniques. Therefore, current fabrication processes would need to be repurposed to fit the needs of organic neural probes for in vivo studies (Figure 5d). Nevertheless, a considerable number of flexible substrates could be tested for suitability in neural probes, including PDMS [112], polyethylene terephthalate (PET) [113], polyethylene naphthalate (PEN) [114], poly(*p*-xylylene) (parylene) [115], polyimide [116], silicone elastomers such as Ecoflex [117], shape-memory polymers [118], and bio-based polymers [119]. While exploring these
opportunities, it is crucial to match the Young's modulus of the substrate with the elastic modulus of the brain for improved interfacing [120–122].

<figure><img src="image_7.png" /><figcaption>**Figure 5.** Organic optoelectronic devices used in neuroscience studies. (a) Number of publications per type and per year listing organic electronic devices (OLEDs and OPDs) in neuroscience applications: 25 publications in total, including 4 reviews. Source: Elsevier Scopus (December 2024). (b) Among 21 research articles, 18 used OLEDs, and 4 used OPDs (1 article described both OLEDs and OPDs). OLEDs with hybrid solutions (CMOS) and/or transistors (TFT) are included. (c) Among the reported OLEDs and OPDs, the substrate choice was mostly glass or silicon wafer (18 counts), and 4 research articles described flexible substrates. (d) In vitro studies using OLEDs/OPDs are dominant (14 counts). (e) Evolution of the active area of optical pixels (OLEDs/OPDs) as a function of year. Ideally, to allow single neuron detection or excitation, organic optoelectronic devices should be roughly the size of a neuron (10–20 µm in diameter, termed here as “Desirable active area”). CMOS- and TFT-based hybrids are not included.</figcaption></figure>

A final consideration for using ultra flexible substrates in organic neural probes is that these devices become difficult to implant into the brain. However, this could be addressed with sugar shuttles that dissolve in the brain shortly after being implanted [123,124]. As the brain readily metabolizes caramelized sugar and can export through the blood-brain barrier, no residue is left, leading to minimal footprint implantation [124].

## 3.2. Transparent Electrodes on Flexible Substrates

As previously stated, OLEDs and OPDs require one of the electrodes to be at least semi-transparent for the light to be transmitted into or out of the device. This requirement also applies to the substrate on which the organic device is deposited. Due to high transparency, low sheet resistance (10 Ω per square), and, therefore, high electrical conductivity, indium tin oxide (ITO) is commonly used as a transparent electrode in organic optoelectronic devices [125]. ITO is typically deposited by sputtering under high vacuum [126], although other techniques such as sol-gel deposition have been attempted [127]. The deposited thin layer (typically 100–200 nm), once on a substrate, must still be patterned into the desired electrode shape to match particular applications. Patterning ITO can be achieved via photolithography [128], a multi-step process in which a photosensitive polymer is selectively exposed to light through a mask, leaving a latent image in the polymer that can then be selectively dissolved to provide patterned access to the underlying substrate. Typically
performed on a silicon wafer or glass, this process is harder to achieve with the flexible substrates needed for neural probes. Most flexible substrates would not withstand the conditions required for photolithography [129] nor the mechanical stress during handling, especially with a desired thickness of 10–50 µm.

Due to the energy-intensive manufacturing process required to fabricate and pattern ITO substrates, as well as the use of the rare metal indium, more environment-friendly electrodes have been suggested to replace ITO. In the context of optogenetic studies, ITO is far from being an ideal candidate for flexible substrates due to its lack of mechanical flexibility (brittleness) [130]. Alternative options include highly conducting polymers (CPs) such as PEDOT:PSS [131], ultrathin (semi-transparent) metal electrodes [132], metal nanowires [133,134], carbon nanotubes [135], graphene composite electrodes [136], and hybrid solutions combining CPs or metal oxides (other than ITO) with ultrathin metal layers or grids [137–139]. Efficient ITO-free OLEDs and OPDs have been used previously [140–142], including devices on flexible substrates or fabricated using solution-processed methods such as inkjet printing. Among the few ITO-free examples in optogenetic studies are OLEDs using transparent electrodes based on ultra-thin silver and gold layers [12,72]. The ultimate choice of the transparent electrode material is often a compromise between its intrinsic mechanical flexibility, transmittance, conductivity (low sheet resistance), and ideally low-temperature fabrication methods.

### 3.3. Miniaturization of Devices

Along with a lack of flexibility, current fully organic devices feature very large optical pixels or channels (Figure 5e), typically between 1 and 10 mm². This size is convenient for fabrication, as it is easily achievable using vacuum deposition methods or screen-printing techniques [48]. Standard vacuum and solution-processed deposition methods that are widely used, such as thermal evaporator or screen-printing, do not allow engineering patterns below 100 µm [143]. However, being three orders of magnitude larger than average neurons [144], these devices are typically too large to be effective in vivo, as they cannot resolve the activity of individual neurons. A “workaround” to produce ultra-small light-emitting pixels that can stimulate single neurons is to use inorganic CMOS or TFT backplanes [68,90–92]. Furthermore, these backplanes could be combined with organic photodiodes. In such systems, individual pixels can switch on or off without the need for hundreds or even thousands of wires, a design that would be unfeasible in normal circumstances [145]. As these systems are quite complex to manufacture and offer poor mechanical flexibility, the use of organic TFT (OTFT) backplanes that feature a low-temperature fabrication process and excellent mechanical flexibility [146,147] could fit the criteria for miniaturized flexible organic neural probes.

### 3.4. Spectral Overlap of Multifunctional Neural Probes

Finally, for organic neural probes incorporating both OLEDs and OPDs (i.e., simultaneous manipulation and recording of neuronal activity), it is possible that OLED emission would overlap with the OPD absorption, producing undesirable crosstalk in the device. It is conceivable that such an arrangement could mean OLED light could reach the adjacent photodetector via waveguiding mode (light internally reflected from a substrate or trapped in a device), leading to saturation of the optical signal [148–150]. Thus, important future directions are to engineer devices with narrowband emission and detection, and multiple approaches have been demonstrated in the past to achieve these functions [94,151–153]. Despite these challenges, it is possible that modifications to device fabrication and parameters through existing manufacturing technologies can produce organic devices that have wide-ranging applications in vivo.
### 3.5. Venues for Future Development

To address the above challenges, low-temperature fabrication methods compatible with flexible substrates and yielding ultra-small and precise deposition patterns are much needed. Among available options, a significant progression in miniaturization could be achieved through the use of advanced microdeposition technologies such as subfemtoliter inkjet and electrohydrodynamic jet (e-jet) printing and femtosecond laser micromachining [154–156].

Inkjet printing is a solution-dispensing technique characterized by its non-contact, maskless, reproducible processing that benefits from the solubility of organic conductors (i.e., polymers or small molecules in organic solvents) [155]. This technique has been successfully harnessed to fabricate organic solar cells [157,158], organic transistors [159], OPDs [160,161], and OLEDs [162,163]. Standard inkjet print heads have discharge volumes in the order of several picoliters, creating patterns (dots) with a minimum diameter of 30–50 µm [159], slightly larger than the typical diameter of a neuronal soma (10–20 µm) [144]. The size of droplets determines the printing resolution, which is typically limited by surface tension and aggravated by the placement error. To improve these metrics, surface functionalization and relief, as well as high-resolution e-jet printing (that uses electric fields rather than thermal or acoustic energy to drive the printing process), have been shown to bring the resolution down to micrometer and submicrometer levels [156,164,165]. The rapid development of OTFTs gave rise to the subfemtoliter inkjet systems that could also dispense droplets with a volume of less than 1 femtoliter and a diameter of less than 1 µm [159,166]. As such, these techniques would be ideal for fabricating micrometer-sized patterns for organic neural probes. To date, this technology has been used to fabricate soft neural probes with nine channels using a conducting polymer ink or a polydimethylsiloxane (PDMS) ink [167].

Future research efforts could also utilize pulsed lasers to remove materials from a substrate or to pattern electrodes for generating micro- or even nanometer structures [168–170]. The challenge for laser micromachining is to remove only the desired material, usually through localized heating, while at the same time minimizing the extent of the damage caused to the remaining material. To minimize this, ultra-short pulse laser technology can operate within a femtosecond regime, yielding minimal heat transfer into the bulk of the material and controlling the heat-affected zone [171,172]. Such femtosecond laser technology would not only be ideal for patterning inorganic and organic layers with submicrometric resolution (example for ITO cited above) but could also give access to electrical contacts through the substrate. More importantly, such technology could be used for laser-cutting the probes from the substrate into its desired final shape. Previous work has shown that laser machining is a viable option for both the ablation of material and the cutting of neural probes [173].

## 4. Discussion

Organic optoelectronic devices such as OLEDs and photodiode-based OPDs represent the next generation of neural optoprobes using innovative soft and flexible polymeric substrates and organic semiconducting materials. Among many biomedical applications, the use of these devices in optogenetic studies and for calcium and voltage imaging is of particular interest. OLEDs and organic photodiodes bring the advantages of spectral selectivity, ultra-small footprint, flexibility, biocompatibility, and multifunctionality, representing significant improvements on current optical techniques in the field. Additionally, OLEDs provide high optoelectronic performance, with sufficient optical power densities (1–1000 µW mm⁻²) to activate optogenetic opsins at low power consumption levels (in the milliwatt range). Organic photodiodes demonstrate adequate sensitivity
levels (5 pW mm⁻²) to map fluorescent signatures from evoked and spontaneous neuronal events. Although proof-of-concept studies are invaluable to set the foundations for this technology, more research efforts are needed to engineer implantable and fMRI-compatible organic neural probes that would revolutionize organic electronics and neuroscience.

While outside the scope of this review, optical studies in neuroscience might also benefit from other types of organic sensing devices, such as photoconductors-based OPDs (PC-OPDs) and phototransistors-based OPDs (PT-OPDs) [42]. PC-OPDs rely on the photoconductivity mechanism in organic materials to sense light and feature a simple device structure in which two electrodes are attached to the opposite ends of an organic semiconducting layer. Their excellent performance results from the photoconductive gain often associated with the photomultiplication effect. In contrast, the PT-OPDs employ a transistor structure to amplify the photocurrent and external quantum efficiency, which makes them excellent in detecting low-amplitude signals [174]. Hybrid inorganic-organic devices emitting or detecting light, such as devices based on perovskite, could also prove useful [175–177]. In this context, particular attention should go to two-dimensional (2D) van der Waals semiconductors (vdWSs) due to their tunable electrical and optical properties and whose incorporation into photodetectors could offer a solution to extended absorption under UV to near-infrared (NIR) light [178,179]. Notably, covalently bonded laminar assembly of vdWSs with polymer supporting substrates has been achieved through surface functionalization, producing structurally stable and electrically reliable vdWS-based flexible devices [180]. Furthermore, novel approaches to synthesizing these 2D semiconductors have recently marked a step forward in breaking the size growth limitation [181]. These advances foster the development of next-generation semiconductor technologies for flexible microelectronics in biomedical research.

It is worth mentioning that in the context of in vivo recordings, OLEDs and photodiode-based organic photodetectors might also synergize with other cutting-edge technologies, such as wireless power delivery systems [182,183]. Their small footprint, high efficiency, and low power consumption could reduce the size, weight, and overall complexity of wireless modules. Computing methodologies for signal processing, such as artificial intelligence or machine learning [184], could also benefit from high signal-to-noise signals recorded by organic photodiodes as well as from spatially selective stimulations using OLEDs.

**Author Contributions:** Conceptualization, P.S. and M.K. (Marcin Kielar); methodology, M.K. (Marcin Kielar) and M.K. (Matthew Kenna); validation, P.S., P.B., M.K. (Marcin Kielar) and M.K. (Matthew Kenna); writing—original draft preparation, M.K. (Marcin Kielar); writing—review and editing, M.K. (Marcin Kielar), M.K. (Matthew Kenna), P.S. and P.B.; visualization, M.K. (Marcin Kielar) and M.K. (Matthew Kenna); supervision, P.S. and P.B.; project administration, P.S.; funding acquisition, P.S., P.B. and M.K. (Marcin Kielar). All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Australian Research Council, grant number DP220102377; and by the National Agency for Research (Agence Nationale de la Recherche, ANR) and the University of Angers in France.

**Conflicts of Interest:** The authors declare no conflicts of interest.

## References

1. Stringer, C.; Pachitariu, M. Analysis Methods for Large-Scale Neuronal Recordings. *Science* **2024**, *386*, eadp7429. [CrossRef] [PubMed]

2. Breakspear, M. Dynamic Models of Large-Scale Brain Activity. *Nat. Neurosci.* **2017**, *20*, 340–352. [CrossRef] [PubMed]

3. Engel, T.A.; Steinmetz, N.A. New Perspectives on Dimensionality and Variability from Large-Scale Cortical Dynamics. *Curr. Opin. Neurobiol.* **2019**, *58*, 181–190. [CrossRef]
4. Kerr, C.C.; van Albada, S.J.; Neymotin, S.A.; Chadderdon, G.L.; Robinson, P.A.; Lytton, W.W. Cortical Information Flow in Parkinson's Disease: A Composite Network/Field Model. *Front. Comput. Neurosci.* **2013**, *7*, 45203. [CrossRef] [PubMed]

5. Adarsh, V.; Gangadharan, G.R.; Fiore, U.; Zanetti, P. Multimodal Classification of Alzheimer's Disease and Mild Cognitive Impairment Using Custom MKSCDDL Kernel over CNN with Transparent Decision-Making for Explainable Diagnosis. *Sci. Rep.* **2024**, *14*, 1774. [CrossRef]

6. Liu, M.; Cheng, D.; Yan, W. Classification of Alzheimer's Disease by Combination of Convolutional and Recurrent Neural Networks Using FDG-PET Images. *Front. Neuroinform.* **2018**, *12*, 312747. [CrossRef]

7. Vassanelli, S.; Mahmud, M. Trends and Challenges in Neuroengineering: Toward “Intelligent” Neuroprostheses through Brain-“brain Inspired Systems” Communication. *Front. Neurosci.* **2016**, *10*, 438. [CrossRef]

8. Won, S.M.; Cai, L.; Gutruf, P.; Rogers, J.A. Wireless and Battery-Free Technologies for Neuroengineering. *Nat. Biomed. Eng.* **2023**, *7*, 405-423. [CrossRef]

9. Fan, B.; Li, W. Miniaturized Optogenetic Neural Implants: A Review. *Lab Chip* **2015**, *15*, 3838-3855. [CrossRef]

10. Fekete, Z. Recent Advances in Silicon-Based Neural Microelectrodes and Microsystems: A Review. *Sens. Actuators B Chem.* **2015**, *215*, 300-315. [CrossRef]

11. Streich, L.; Boffi, J.C.; Wang, L.; Alhalaseh, K.; Barbieri, M.; Rehm, R.; Deivasigamani, S.; Gross, C.T.; Agarwal, A.; Prevedel, R. High-Resolution Structural and Functional Deep Brain Imaging Using Adaptive Optics Three-Photon Microscopy. *Nat. Methods* **2021**, *18*, 1253-1258. [CrossRef]

12. Ciccone, G.; Meloni, I.; Fernandez Lahore, R.G.; Vierock, J.; Reineke, S.; Kleemann, H.; Hegemann, P.; Leo, K.; Murawski, C. Tailoring Organic LEDs for Bidirectional Optogenetic Control via Dual-Color Switching. *Adv. Funct. Mater.* **2022**, *32*, 2110590. [CrossRef]

13. Kielar, M.; Gooch, H.; Xu, L.; Pandey, A.K.; Sah, P. Direct Detection of Neuronal Activity Using Organic Photodetectors. *ACS Photonics* **2021**, *8*, 228-237. [CrossRef]

14. Morton, A.; Murawski, C.; Deng, Y.; Keum, C.; Miles, G.B.; Tello, J.A.; Gather, M.C. Photostimulation for In Vitro Optogenetics with High-Power Blue Organic Light-Emitting Diodes. *Adv. Biosyst.* **2019**, *3*, 1800290. [CrossRef] [PubMed]

15. Kielar, M.; Marek, R.; Kenna, M.; Cole, C.M.; Xu, L.; Yambem, S.D.; Sah, P.; Pandey, A.K. Optogenetic Stimulation and Spatial Localization of Neurons Using a Multi-OLED Approach. *ACS Photonics* **2022**, *9*, 3279-3290. [CrossRef]

16. Lai, H.C.; Jan, L.Y. The Distribution and Targeting of Neuronal Voltage-Gated Ion Channels. *Nat. Rev. Neurosci.* **2006**, *7*, 548-562. [CrossRef]

17. Fletcher, A. Action Potential: Generation and Propagation. *Anaesth. Intensive Care Med.* **2011**, *12*, 258-262. [CrossRef]

18. Südhof, T.C. Towards an Understanding of Synapse Formation. *Neuron* **2018**, *100*, 276-293. [CrossRef]

19. Hyman, S.E. Neurotransmitters. *Curr. Biol.* **2005**, *15*, R154-R158. [CrossRef]

20. Alberts, B.; Johnson, A.; Lewis, J.; Raff, M.; Roberts, K.; Walter, P. *Molecular Biology of the Cell: Ion Channels and the Electrical Properties of Membranes*, 4th ed.; Garland Science: New York, NY, USA, 2002.

21. Kenna, M.; Marek, R.; Sah, P. Insights into the Encoding of Memories through the Circuitry of Fear. *Curr. Opin. Neurobiol.* **2023**, *80*, 102712. [CrossRef]

22. Huang, Z. Brief History and Development of Electrophysiological Recording Techniques in Neuroscience. In *Signal Processing in Neuroscience*; Springer: Singapore, 2016; pp. 1-10. [CrossRef]

23. Rey, H.G.; Pedreira, C.; Quian Quiroga, R. Past, Present and Future of Spike Sorting Techniques. *Brain Res. Bull.* **2015**, *119*, 106-117. [CrossRef] [PubMed]

24. Luan, L.; Robinson, J.T.; Aazhang, B.; Chi, T.; Yang, K.; Li, X.; Rathore, H.; Singer, A.; Yellapantula, S.; Fan, Y.; et al. Recent Advances in Electrical Neural Interface Engineering: Minimal Invasiveness, Longevity, and Scalability. *Neuron* **2020**, *108*, 302-321. [CrossRef]

25. Krauss, J.K.; Lipsman, N.; Aziz, T.; Boutet, A.; Brown, P.; Chang, J.W.; Davidson, B.; Grill, W.M.; Hariz, M.I.; Horn, A.; et al. Technology of Deep Brain Stimulation: Current Status and Future Directions. *Nat. Rev. Neurol.* **2020**, *17*, 75-87. [CrossRef]

26. Frey, J.; Cagle, J.; Johnson, K.A.; Wong, J.K.; Hilliard, J.D.; Butson, C.R.; Okun, M.S.; de Hemptinne, C. Past, Present, and Future of Deep Brain Stimulation: Hardware, Software, Imaging, Physiology and Novel Approaches. *Front. Neurol.* **2022**, *13*, 825178. [CrossRef] [PubMed]

27. Mosley, P.E.; Windels, F.; Morris, J.; Coyne, T.; Marsh, R.; Giorni, A.; Mohan, A.; Sachdev, P.; O'Leary, E.; Boschen, M.; et al. A Randomised, Double-Blind, Sham-Controlled Trial of Deep Brain Stimulation of the Bed Nucleus of the Stria Terminalis for Treatment-Resistant Obsessive-Compulsive Disorder. *Transl. Psychiatry* **2021**, *11*, 190. [CrossRef] [PubMed]

28. Sullivan, C.R.P.; Olsen, S.; Widge, A.S. Deep Brain Stimulation for Psychiatric Disorders: From Focal Brain Targets to Cognitive Networks. *Neuroimage* **2021**, *225*, 117515. [CrossRef]

29. Holtzheimer, P.E.; Mayberg, H.S. Deep Brain Stimulation for Psychiatric Disorders. *Annu. Rev. Neurosci.* **2011**, *34*, 289. [CrossRef]

30. Chen, T.W.; Wardill, T.J.; Sun, Y.; Pulver, S.R.; Renninger, S.L.; Baohan, A.; Schreiter, E.R.; Kerr, R.A.; Orger, M.B.; Jayaraman, V.; et al. Ultrasensitive Fluorescent Proteins for Imaging Neuronal Activity. *Nature* **2013**, *499*, 295-300. [CrossRef]
31. Zhang, F.; Aravanis, A.M.; Adamantidis, A. Circuit-Breakers: Optical Technologies for Probing Neural Signals and Systems. Nat. Rev. Neurosci. **2007**, *8*, 577–581. [CrossRef]

32. Simpson, E.H.; Akam, T.; Patriarchi, T.; Blanco-Pozo, M.; Burgeno, L.M.; Mohebi, A.; Cragg, S.J.; Walton, M.E. Lights, Fiber, Action! A Primer on in Vivo Fiber Photometry. Neuron **2024**, *112*, 718–739. [CrossRef]

33. Sridharan, A.; Rajan, S.D.; Muthuswamy, J. Long-Term Changes in the Material Properties of Brain Tissue at the Implant-Tissue Interface. J. Neural Eng. **2013**, *10*, 066001. [CrossRef]

34. Yang, H.H.; St-Pierre, F. Genetically Encoded Voltage Indicators: Opportunities and Challenges. J. Neurosci. **2016**, *36*, 9977–9989. [CrossRef] [PubMed]

35. Zhang, Z.; Lin, Y. Organic Semiconductors for Vacuum-Deposited Planar Heterojunction Solar Cells. ACS Omega **2020**, *5*, 24994–24999. [CrossRef]

36. Kim, H. Neural Correlates of Explicit and Implicit Memory at Encoding and Retrieval: A Unified Framework and Meta-Analysis of Functional Neuroimaging Studies. Biol. Psychol. **2019**, *145*, 96–111. [CrossRef] [PubMed]

37. Boutet, A.; Rashid, T.; Hancu, I.; Elias, G.J.B.; Gramer, R.M.; Germann, J.; Dimarzio, M.; Li, B.; Paramanandam, V.; Prasad, S.; et al. Functional MRI Safety and Artifacts during Deep Brain Stimulation: Experience in 102 Patients. Radiology **2019**, *293*, 174–183. [CrossRef]

38. Hargreaves, B.A.; Worters, P.W.; Pauly, K.B.; Pauly, J.M.; Koch, K.M.; Gold, G.E. Metal-Induced Artifacts in MRI. Am. J. Roentgenol. **2011**, *197*, 547–555. [CrossRef] [PubMed]

39. Chiang, C.K.; Fincher, C.R.; Park, Y.W.; Heeger, A.J.; Shirakawa, H.; Louis, E.J.; Gau, S.C.; MacDiarmid, A.G. Electrical Conductivity in Doped Polyacetylene. Phys. Rev. Lett. **1977**, *39*, 1098–1101. [CrossRef]

40. Chen, L.X. Organic Solar Cells: Recent Progress and Challenges. ACS Energy Lett. **2019**, *4*, 2537–2539. [CrossRef]

41. Hong, G.; Gan, X.; Leonhardt, C.; Zhang, Z.; Seibert, J.; Busch, J.M.; Bräse, S.; Hong, G.; Gan, X.; Leonhardt, C.; et al. A Brief History of OLEDs—Emitter Development and Industry Milestones. Adv. Mater. **2021**, *33*, 2005630. [CrossRef]

42. Ren, H.; Chen, J.D.; Li, Y.Q.; Tang, J.X. Recent Progress in Organic Photodetectors and Their Applications. Adv. Sci. **2021**, *8*, 2002418. [CrossRef]

43. Facchetti, A. Semiconductors for Organic Transistors. Mater. Today **2007**, *10*, 28–37. [CrossRef]

44. Naresh, V.; Lee, N. A Review on Biosensors and Recent Development of Nanostructured Materials-Enabled Biosensors. Sensors **2021**, *21*, 1109. [CrossRef] [PubMed]

45. Holzinger, M.; Goff, A.L.; Cosnier, S. Nanomaterials for Biosensing Applications: A Review. Front. Chem. **2014**, *2*, 108707. [CrossRef]

46. Saggar, S.; Sanderson, S.; Gedefaw, D.; Pan, X.; Philippa, B.; Andersson, M.R.; Lo, S.C.; Namdas, E.B. Toward Faster Organic Photodiodes: Tuning of Blend Composition Ratio. Adv. Funct. Mater. **2021**, *31*, 2010661. [CrossRef]

47. Fuentes-Hernandez, C.; Chou, W.F.; Khan, T.M.; Diniz, L.; Lukens, J.; Larrain, F.A.; Rodriguez-Toro, V.A.; Kippelen, B. Large-Area Low-Noise Flexible Organic Photodiodes for Detecting Faint Visible Light. Science **2020**, *370*, 698–701. [CrossRef] [PubMed]

48. Kielar, M.; Dhez, O.; Pecastaings, G.; Curutchet, A.; Hirsch, L. Long-Term Stable Organic Photodetectors with Ultra Low Dark Currents for High Detectivity Applications. Sci. Rep. **2016**, *6*, 39201. [CrossRef]

49. Fang, Y.; Armin, A.; Meredith, P.; Huang, J. Accurate Characterization of Next-Generation Thin-Film Photodetectors. Nat. Photonics **2019**, *13*, 1–4. [CrossRef]

50. Baeg, K.-J.; Binda, M.; Natali, D.; Caironi, M.; Noh, Y. Organic Light Detectors: Photodiodes and Phototransistors. Adv. Mater. **2013**, *25*, 4267–4295. [CrossRef]

51. Kim, I.K.; Jo, J.H.; Lee, B.; Choi, Y.J. Detectivity Analysis for Organic Photodetectors. Org. Electron. **2018**, *57*, 89–92. [CrossRef]

52. Ahn, J.; Lee, S.H.; Song, I.; Chidchob, P.; Kwon, Y.; Oh, J.H. Chiral Organic Semiconducting Materials for Next-Generation Optoelectronic Sensors. Device **2023**, *1*, 100176. [CrossRef]

53. Wang, Y.; Gong, Q.; Miao, Q. Structured and Functionalized Organic Semiconductors for Chemical and Biological Sensors Based on Organic Field Effect Transistors. Mater. Chem. Front. **2020**, *4*, 3505–3520. [CrossRef]

54. Borges-González, J.; Kousseff, C.J.; Nielsen, C.B. Organic Semiconductors for Biological Sensing. J. Mater. Chem. C **2019**, *7*, 1111–1130. [CrossRef]

55. Kook, G.; Lee, S.W.; Lee, H.C.; Cho, I.J.; Lee, H.J. Neural Probes for Chronic Applications. Micromachines **2016**, *7*, 179. [CrossRef]

56. Cellot, G.; Lagonegro, P.; Tarabella, G.; Scaini, D.; Fabbri, F.; Iannotta, S.; Prato, M.; Salviati, G.; Ballerini, L. PEDOT:PSS Interfaces Support the Development of Neuronal Synaptic Networks with Reduced Neuroglia Response in Vitro. Front. Neurosci. **2016**, *9*, 521. [CrossRef]

57. Dijk, G.; Rutz, A.L.; Malliaras, G.G. Stability of PEDOT:PSS-Coated Gold Electrodes in Cell Culture Conditions. Adv. Mater. Technol. **2020**, *5*, 1900662. [CrossRef]

58. Lan, Z.; Lau, Y.S.; Cai, L.; Han, J.; Suen, C.W.; Zhu, F. Dual-Band Organic Photodetectors for Dual-Channel Optical Communications. Laser Photon. Rev. **2022**, *16*, 2100602. [CrossRef]
59. Li, N.; Eedugurala, N.; Azoulay, J.D.; Ng, T.N. A Filterless Organic Photodetector Electrically Switchable between Visible and Infrared Detection. *Cell Rep. Phys. Sci.* **2022**, *3*, 100711. [CrossRef]

60. Cho, H.; Byun, C.-W.; Cho, N.S.; Han, J.-H.; Lee, H.; Choi, S.; Kwon, B.-H.; Lee, J.; Cho, H.; Byun, C.-W.; et al. Color-Tunable Organic Light-Emitting Diodes with Vertically Stacked Blue, Green, and Red Colors for Lighting and Display Applications. *Opt. Express* **2018**, *26*, 18351. [CrossRef]

61. Fröbel, M.; Schwab, T.; Kliem, M.; Hofmann, S.; Leo, K.; Gather, M.C. Get It White: Color-Tunable AC/DC OLEDs. *Light Sci. Appl.* **2015**, *4*, e247. [CrossRef]

62. Deisseroth, K. Optogenetics: 10 Years of Microbial Opsins in Neuroscience. *Nat. Neurosci.* **2015**, *18*, 1213–1225. [CrossRef]

63. Wang, Y.; Qiu, Y.; Ameri, S.K.; Jang, H.; Dai, Z.; Huang, Y.; Lu, N. Low-Cost, Micron-Thick, Tape-Free Electronic Tattoo Sensors with Minimized Motion and Sweat Artifacts. *npj Flex. Electron.* **2018**, *2*, 6. [CrossRef]

64. Murawski, C.; Archer, E. A Substrateless, Flexible, and Water-Resistant Organic Light-Emitting Diode. *Nat. Commun.* **2020**, *11*, 6250. [CrossRef]

65. Salatino, J.W.; Ludwig, K.A.; Kozai, T.D.Y.; Purcell, E.K. Glial Responses to Implanted Electrodes in the Brain. *Nat. Biomed. Eng.* **2017**, *1*, 862–877. [CrossRef] [PubMed]

66. Kim, D.; Yokota, T.; Suzuki, T.; Lee, S.; Woo, T.; Yukita, W.; Koizumi, M.; Tachibana, Y.; Yawo, H.; Onodera, H.; et al. Ultraflexible Organic Light-Emitting Diodes for Optogenetic Nerve Stimulation. *Proc. Natl. Acad. Sci. USA* **2020**, *117*, 21138–21146. [CrossRef]

67. Klapoetke, N.C.; Murata, Y.; Kim, S.S.; Pulver, S.R.; Birdsey-Benson, A.; Cho, Y.K.; Morimoto, T.K.; Chuong, A.S.; Carpenter, E.J.; Tian, Z.; et al. Independent Optical Excitation of Distinct Neural Populations. *Nat. Methods* **2014**, *11*, 338–346. [CrossRef] [PubMed]

68. Steude, A.; Witts, E.C.; Miles, G.B.; Gather, M.C. Arrays of Microscopic Organic LEDs for High-Resolution Optogenetics. *Sci. Adv.* **2016**, *2*, e1600061. [CrossRef]

69. Yuan, W.; Jin, Q.; Du, M.; Duan, L.; Zhang, Y. Tailoring Ultra-Narrowband Tetraborylated Multiple Resonance Emitter for High-Performance Blue OLED. *Adv. Mater.* **2024**, *36*, 2410096. [CrossRef] [PubMed]

70. Yin, C.; Xin, Y.; Huang, T.; Zhang, Q.; Duan, L.; Zhang, D. Ultra-Low Power-Consumption OLEDs via Phosphor-Assisted Thermally-Activated-Delayed-Fluorescence-Sensitized Narrowband Emission. *Nat. Commun.* **2025**, *16*, 30. [CrossRef]

71. Chen, R.; Liang, N.; Zhai, T. Dual-Color Emissive OLED with Orthogonal Polarization Modes. *Nat. Commun.* **2024**, *15*, 1331. [CrossRef]

72. Ciccone, G.; Weber, J.P.; Meloni, I.; Kleemann, H.; Leo, K.; Murawski, C. Multiplexed Optogenetics with Striped Organic LEDs. *Adv. Opt. Mater.* **2024**, *12*, 2301340. [CrossRef]

73. Lan, Z.; Lei, Y.; Chan, W.K.E.; Chen, S.; Luo, D.; Zhu, F. Near-Infrared and Visible Light Dual-Mode Organic Photodetectors. *Sci. Adv.* **2020**, *6*, eaaw8065. [CrossRef] [PubMed]

74. Wang, Y.; Siegmund, B.; Tang, Z.; Ma, Z.; Kublitski, J.; Xing, S.; Nikolis, V.C.; Ullbrich, S.; Li, Y.; Benduhn, J.; et al. Stacked Dual-Wavelength Near-Infrared Organic Photodetectors. *Adv. Opt. Mater.* **2021**, *9*, 2001784. [CrossRef]

75. Chiba, T.; Kumagai, D.; Udagawa, K.; Watanabe, Y.; Kido, J. Dual Mode OPV-OLED Device with Photovoltaic and Light-Emitting Functionalities. *Sci. Rep.* **2018**, *8*, 11472. [CrossRef] [PubMed]

76. Kielar, M.; Hamid, T.; Wu, L.; Windels, F.; Sah, P.; Pandey, A.K. Organic Optoelectronic Diodes as Tactile Sensors for Soft-Touch Applications. *ACS Appl. Mater. Interfaces* **2019**, *11*, 21775–21783. [CrossRef]

77. Hamid, T.; Kielar, M.; Yambem, S.D.; Pandey, A.K. Multifunctional Diode Operation of Tetracene Sensitized Polymer:Fullerene Heterojunctions with Simultaneous Electroluminescence in Visible and NIR Bands. *Adv. Electron. Mater.* **2021**, *7*, 2000824. [CrossRef]

78. Chen, H.; Huang, Y.; Zhang, R.; Mou, H.; Ding, J.; Zhou, J.; Wang, Z.; Li, H.; Chen, W.; Zhu, J.; et al. Organic Solar Cells with 20.82% Efficiency and High Tolerance of Active Layer Thickness through Crystallization Sequence Manipulation. *Nat. Mater.* **2025**, *24*, 444–453. [CrossRef]

79. Park, J.; Kim, K.; Kim, Y.; Kim, T.S.; Min, I.S.; Li, B.; Cho, Y.U.; Lee, C.; Lee, J.Y.; Gao, Y.; et al. A Wireless, Solar-Powered, Optoelectronic System for Spatial Restriction-Free Long-Term Optogenetic Neuromodulations. *Sci. Adv.* **2023**, *9*, eadi8918. [CrossRef]

80. Pranti, A.S.; Schander, A.; Bödecker, A.; Lang, W. PEDOT: PSS Coating on Gold Microelectrodes with Excellent Stability and High Charge Injection Capacity for Chronic Neural Interfaces. *Sens. Actuators B Chem.* **2018**, *275*, 382–393. [CrossRef]

81. Bianchi, M.; De Salvo, A.; Asplund, M.; Carli, S.; Di Lauro, M.; Schulze-Bonhage, A.; Stieglitz, T.; Fadiga, L.; Biscarini, F. Poly(3,4-Ethylenedioxythiophene)-Based Neural Interfaces for Recording and Stimulation: Fundamental Aspects and In Vivo Applications. *Adv. Sci.* **2022**, *9*, 2104701. [CrossRef]

82. Filho, G.; Júnior, C.; Spinelli, B.; Damasceno, I.; Fiuza, F.; Morya, E. All-Polymeric Electrode Based on PEDOT:PSS for In Vivo Neural Recording. *Biosensors* **2022**, *12*, 853. [CrossRef]

83. Zou, S.J.; Shen, Y.; Xie, F.M.; Chen, J.D.; Li, Y.Q.; Tang, J.X. Recent Advances in Organic Light-Emitting Diodes: Toward Smart Lighting and Displays. *Mater. Chem. Front.* **2020**, *4*, 788–820. [CrossRef]
84. Thejo Kalyani, N.; Dhoble, S.J. Organic Light Emitting Diodes: Energy Saving Lighting Technology—A Review. *Renew. Sustain. Energy Rev.* **2012**, *16*, 2696–2723. [CrossRef]

85. Wang, J.; Zhang, F.; Zhang, J.; Tang, W.; Tang, A.; Peng, H.; Xu, Z.; Teng, F.; Wang, Y. Key Issues and Recent Progress of High Efficient Organic Light-Emitting Diodes. *J. Photochem. Photobiol. C Photochem. Rev.* **2013**, *17*, 69–104. [CrossRef]

86. Shuai, Z.; Peng, Q. Organic Light-Emitting Diodes: Theoretical Understanding of Highly Efficient Materials and Development of Computational Methodology. *Natl. Sci. Rev.* **2017**, *4*, 224–239. [CrossRef]

87. Yadav, R.A.K.; Dubey, D.K.; Chen, S.Z.; Liang, T.W.; Jou, J.H. Role of Molecular Orbital Energy Levels in OLED Performance. *Sci. Rep.* **2020**, *10*, 9915. [CrossRef]

88. Riahi, M.; Yoshida, K.; King, L.G.; Samuel, I.D.W. In-Operando Investigation of Purcell Effect on Efficiency Roll-off in Top-Emitting Phosphorescent Organic Light Emitting Diodes. *Synth. Met.* **2025**, *312*, 117848. [CrossRef]

89. Kang, K.; Byeon, I.; Kim, Y.G.; Choi, J.-R.; Kim, D.; Kang, K.; Byeon, I.; Kim, D.; Kim, Y.G.; Choi, J. Nanostructures in Organic Light-Emitting Diodes: Principles and Recent Advances in the Light Extraction Strategy. *Laser Photon. Rev.* **2024**, *18*, 2400547. [CrossRef]

90. Steude, A.; Jahnel, M.; Thomschke, M.; Schober, M.; Gather, M.C. Controlling the Behavior of Single Live Cells with High Density Arrays of Microscopic OLEDs. *Adv. Mater.* **2015**, *27*, 7657–7661. [CrossRef]

91. Lee, Y.-K.; Bawolek, E.; Christen, J.B.; Smith, J.T.; O'Brien, B. Application of Flexible OLED Display Technology for Electro-Optical Stimulation and/or Silencing of Neural Activity. *J. Disp. Technol.* **2014**, *10*, 514–520. [CrossRef]

92. Smith, J.; Shah, A.; Lee, Y.K.; O'Brien, B.; Kullman, D.; Sridharan, A.; Muthuswamy, J.; Blain Christen, J. Optogenetic Neurostimulation of Auricular Vagus Using Flexible OLED Display Technology to Treat Chronic Inflammatory Disease and Mental Health Disorders. *Electron. Lett.* **2016**, *52*, 900–902. [CrossRef]

93. Hillebrandt, S.; Keum, C.; Deng, Y.; Chavas, J.; Galle, C.; Hardin, T.; Galluppi, F.; Gather, M.C. High Brightness, Highly Directional Organic Light-Emitting Diodes as Light Sources for Future Light-Amplifying Prosthetics in the Optogenetic Management of Vision Loss. *Adv. Opt. Mater.* **2022**, *11*, 2200877. [CrossRef]

94. Murawski, C.; Mischok, A.; Booth, J.; Kumar, J.D.; Archer, E.; Tropf, L.; Keum, C.M.; Deng, Y.L.; Yoshida, K.; Samuel, I.D.W.; et al. Narrowband Organic Light-Emitting Diodes for Fluorescence Microscopy and Calcium Imaging. *Adv. Mater.* **2019**, *31*, 1903599. [CrossRef] [PubMed]

95. Sridharan, A.; Shah, A.; Kumar, S.S.; Kyeh, J.; Smith, J.; Blain-Christen, J.; Muthuswamy, J. Optogenetic Modulation of Cortical Neurons Using Organic Light Emitting Diodes (OLEDs). *Biomed. Phys. Eng. Express* **2020**, *6*, 025003. [CrossRef] [PubMed]

96. Kielar, M.; Marek, R.; Gooch, H.; Cole, C.M.; Kenna, M.; Xu, L.; Yambem, S.D.; Pandey, A.K.; Sah, P. Stability, Reliability, and Performance of Organic Light-Emitting Diodes and Photodetectors in Optogenetic Studies. *Proc. SPIE* **2023**, *1266103*, 7. [CrossRef]

97. Matarèse, B.F.E.; Feyen, P.L.C.; de Mello, J.C.; Benfenati, F. Sub-Millisecend Control of Neuronal Firing by Organic Light-Emitting Diodes. *Front. Bioeng. Biotechnol.* **2019**, *7*, 483442. [CrossRef]

98. Ghezzi, D.; Antognazza, M.R.; Dal Maschio, M.; Lanzarini, E.; Benfenati, F.; Lanzani, G. A Hybrid Bioorganic Interface for Neuronal Photoactivation. *Nat. Commun.* **2011**, *2*, 166. [CrossRef]

99. Morton, A.; Murawski, C.; Pulver, S.R.; Gather, M.C. High-Brightness Organic Light-Emitting Diodes for Optogenetic Control of Drosophila Locomotor Behaviour. *Sci. Rep.* **2016**, *6*, 31117. [CrossRef]

100. Murawski, C.; Morton, A.; Samuel, I.D.W.; Pulver, S.R.; Gather, M.C. Organic Light-Emitting Diodes for Optogenetic Stimulation of Drosophila Larvae. In *Fourier Transform Spectroscopy; Proceedings, Light, Energy and the Environment; Optica Publishing Group: Washington, DC, USA, 2016; p. JW4A-9*. [CrossRef]

101. Rezaei-Mazinani, S.; Ivanov, A.I.; Proctor, C.M.; Gkoupidenis, P.; Bernard, C.; Malliaras, G.G.; Ismailova, E. Monitoring Intrinsic Optical Signals in Brain Tissue with Organic Photodetectors. *Adv. Mater. Technol.* **2018**, *3*, 1700333. [CrossRef]

102. Murawski, C.; Pulver, S.R.; Gather, M.C. Segment-Specific Optogenetic Stimulation in Drosophila Melanogaster with Linear Arrays of Organic Light-Emitting Diodes. *Nat. Commun.* **2020**, *11*, 6248. [CrossRef]

103. Taal, A.J.; Uguz, I.; Hillebrandt, S.; Moon, C.K.; Andino-Pavlovsky, V.; Choi, J.; Keum, C.; Deisseroth, K.; Gather, M.C.; Shepard, K.L. Optogenetic Stimulation Probes with Single-Neuron Resolution Based on Organic LEDs Monolithically Integrated on CMOS. *Nat. Electron.* **2023**, *6*, 669–679. [CrossRef]

104. Stoltzfus, D.M.; Donaghey, J.E.; Armin, A.; Shaw, P.E.; Burn, P.L.; Meredith, P. Charge Generation Pathways in Organic Solar Cells: Assessing the Contribution from the Electron Acceptor. *Chem. Rev.* **2016**, *116*, 12920–12955. [CrossRef]

105. Simone, G.; Dyson, M.J.; Weijtens, C.H.; Meskers, S.C.; Coehoorn, R.; Janssen, R.A.; Gelinck, G.H. On the Origin of Dark Current in Organic Photodiodes. *Adv. Opt. Mater.* **2020**, *8*, 1901568. [CrossRef]

106. Kielar, M.; Hamid, T.; Wiemer, M.; Windels, F.; Hirsch, L.; Sah, P.; Pandey, A.K. Light Detection in Open-Circuit Voltage Mode of Organic Photodetectors. *Adv. Funct. Mater.* **2020**, *30*, 1907964. [CrossRef]

107. Simone, G.; Di Carlo Rasi, D.; de Vries, X.; Heintges, G.H.; Meskers, S.C.; Janssen, R.A.; Gelinck, G.H. Near-Infrared Tandem Organic Photodiodes for Future Application in Artificial Retinal Implants. *Adv. Mater.* **2018**, *30*, 1804678. [CrossRef] [PubMed]
108. Zhang, Y.; Rózsa, M.; Liang, Y.; Bushey, D.; Wei, Z.; Zheng, J.; Reep, D.; Broussard, G.J.; Tsang, A.; Tsegaye, G.; et al. Fast and Sensitive GCaMP Calcium Indicators for Imaging Neural Populations. *Nature* **2023**, *615*, 884–891. [CrossRef] [PubMed]

109. Mano, O.; Creamer, M.S.; Matulis, C.A.; Salazar-Gatzimas, E.; Chen, J.; Zavatone-Veth, J.A.; Clark, D.A. Using Slow Frame Rate Imaging to Extract Fast Receptive Fields. *Nat. Commun.* **2019**, *10*, 4979. [CrossRef]

110. Baran, D.; Corzo, D.; Blazquez, G.T. Flexible Electronics: Status, Challenges and Opportunities. *Front. Electron.* **2020**, *1*, 594003. [CrossRef]

111. Lewis, J. Material Challenge for Flexible Organic Devices. *Mater. Today* **2006**, *9*, 38–45. [CrossRef]

112. Miranda, I.; Souza, A.; Sousa, P.; Ribeiro, J.; Castanheira, E.M.S.; Lima, R.; Minas, G. Properties and Applications of PDMS for Biomedical Engineering: A Review. *J. Funct. Biomater.* **2021**, *13*, 2. [CrossRef]

113. Szafran, K.; Jurak, M.; Mroczka, R.; Wiącek, A.E. Surface Properties of the Polyethylene Terephthalate (PET) Substrate Modified with the Phospholipid-Polypeptide-Antioxidant Films: Design of Functional Biocoatings. *Pharmaceutics* **2022**, *14*, 2815. [CrossRef]

114. Fonrodona, M.; Escarré, J.; Villar, F.; Soler, D.; Asensi, J.M.; Bertomeu, J.; Andreu, J. PEN as Substrate for New Solar Cell Technologies. *Sol. Energy Mater. Sol. Cells* **2005**, *89*, 37–47. [CrossRef]

115. Moss, T.; Greiner, A. Functionalization of Poly(Para-Xylylene)s—Opportunities and Challenges as Coating Material. *Adv. Mater. Interfaces* **2020**, *7*, 1901858. [CrossRef]

116. Vomero, M.; Ciarpella, F.; Zucchini, E.; Kirsch, M.; Fadiga, L.; Stieglitz, T.; Asplund, M. On the Longevity of Flexible Neural Interfaces: Establishing Biostability of Polyimide-Based Intracortical Implants. *Biomaterials* **2022**, *281*, 121372. [CrossRef] [PubMed]

117. Lavazza, J.; Contino, M.; Marano, C. Strain Rate, Temperature and Deformation State Effect on Ecoflex 00-50 Silicone Mechanical Behaviour. *Mech. Mater.* **2023**, *178*, 104560. [CrossRef]

118. Behl, M.; Lendlein, A. Shape-Memory Polymers. *Mater. Today* **2007**, *10*, 20–28. [CrossRef]

119. Cywar, R.M.; Rorrer, N.A.; Hoyt, C.B.; Beckham, G.T.; Chen, E.Y.X. Bio-Based Polymers with Performance-Advantaged Properties. *Nat. Rev. Mater.* **2021**, *7*, 83–103. [CrossRef]

120. McGlynn, E.; Nabaei, V.; Ren, E.; Galeote-Checa, G.; Das, R.; Curia, G.; Heidari, H. The Future of Neuroscience: Flexible and Wireless Implantable Neural Electronics. *Adv. Sci.* **2021**, *8*, 2002693. [CrossRef]

121. Zhao, H.; Liu, R.; Zhang, H.; Cao, P.; Liu, Z.; Li, Y. Research Progress on the Flexibility of an Implantable Neural Microelectrode. *Micromachines* **2022**, *13*, 386. [CrossRef]

122. Axpe, E.; Orive, G.; Franze, K.; Appel, E.A. Towards Brain-Tissue-like Biomaterials. *Nat. Commun.* **2020**, *11*, 3423. [CrossRef]

123. Weltman, A.; Yoo, J.; Meng, E. Flexible, Penetrating Brain Probes Enabled by Advances in Polymer Microfabrication. *Micromachines* **2016**, *7*, 180. [CrossRef]

124. Zhang, E.N.; Clément, J.P.; Alameri, A.; Ng, A.; Kennedy, T.E.; Juncker, D. Mechanically Matched Silicone Brain Implants Reduce Brain Foreign Body Response. *Adv. Mater. Technol.* **2021**, *6*, 2000909. [CrossRef]

125. Kim, H.; Gilmore, C.M.; Piqué, A.; Horwitz, J.S.; Mattoussi, H.; Murata, H.; Kafafi, Z.H.; Chrisey, D.B. Electrical, Optical, and Structural Properties of Indium–Tin–Oxide Thin Films for Organic Light-Emitting Devices. *J. Appl. Phys.* **1999**, *86*, 6451–6461. [CrossRef]

126. Ellmer, K. Past Achievements and Future Challenges in the Development of Optically Transparent Electrodes. *Nat. Photonics* **2012**, *6*, 809–817. [CrossRef]

127. Chen, Z.; Li, W.; Li, R.; Zhang, Y.; Xu, G.; Cheng, H. Fabrication of Highly Transparent and Conductive Indium-Tin Oxide Thin Films with a High Figure of Merit via Solution Processing. *Langmuir* **2013**, *29*, 13836–13842. [CrossRef]

128. Aleksandrova, M.; Kolev, G.; Cholakova, I.; Dobrikov, G.; Bodurov, G. Photolithography versus Lift off Process for Patterning of Sputtered Indium Tin Oxide for Flexible Displays. *Int. J. Thin Film. Sci. Technol.* **2013**, *2*, 67–75. [CrossRef]

129. Shim, H.; Jang, S.; Yu, C. High-Resolution Patterning of Organic Semiconductors toward Industrialization of Flexible Organic Electronics. *Matter* **2022**, *5*, 23–25. [CrossRef]

130. Chen, Z.; Cotterell, B.; Wang, W.; Guenther, E.; Chua, S.J. A Mechanical Assessment of Flexible Optoelectronic Devices. *Thin Solid Films* **2001**, *394*, 201–205. [CrossRef]

131. Fehse, K.; Walzer, K.; Leo, K.; Lövenich, W.; Elschner, A. Highly Conductive Polymer Anodes as Replacements for Inorganic Materials in High-Efficiency Organic Light-Emitting Diodes. *Adv. Mater.* **2007**, *19*, 441–444. [CrossRef]

132. Krautz, D.; Cheylan, S.; Ghosh, D.S.; Pruneri, V. Nickel as an Alternative Semitransparent Anode to Indium Tin Oxide for Polymer LED Applications. *Nanotechnology* **2009**, *20*, 275204. [CrossRef]

133. Hu, L.; Kim, H.S.; Lee, J.Y.; Peumans, P.; Cui, Y. Scalable Coating and Properties of Transparent, Flexible, Silver Nanowire Electrodes. *ACS Nano* **2010**, *4*, 2955–2963. [CrossRef]

134. Leem, D.-S.; Edwards, A.; Faist, M.; Nelson, J.; C Bradley, D.D.; de Mello, J.C.; Leem, D.; Edwards, A.; Faist, M.; Nelson, J.; et al. Efficient Organic Solar Cells with Solution-Processed Silver Nanowire Electrodes. *Adv. Mater.* **2011**, *23*, 4371–4375. [CrossRef] [PubMed]

135. Kim, S.; Yim, J.; Wang, X.; Bradley, D.D.C.; Lee, S.; DeMello, J.C. Spin- and Spray-Deposited Single-Walled Carbon-Nanotube Electrodes for Organic Solar Cells. *Adv. Funct. Mater.* **2010**, *20*, 2310–2316. [CrossRef]
136. Chang, H.; Wang, G.; Yang, A.; Tao, X.; Liu, X.; Shen, Y.; Zheng, Z. A Transparent, Flexible, Low-Temperature, and Solution-Processible Graphene Composite Electrode. *Adv. Funct. Mater.* **2010**, *20*, 2893–2902. [CrossRef]

137. Peng, Y.; Zhang, L.; Cheng, N.; Andrew, T.L. ITO-Free Transparent Organic Solar Cell with Distributed Bragg Reflector for Solar Harvesting Windows. *Energies* **2017**, *10*, 707. [CrossRef]

138. Galagan, Y.; Rubingh, J.E.J.; Andriessen, R.; Fan, C.C.; Blom, P.W.; Veenstra, S.C.; Kroon, J.M. ITO-Free Flexible Organic Solar Cells with Printed Current Collecting Grids. *Sol. Energy Mater. Sol. Cells* **2011**, *95*, 1339–1343. [CrossRef]

139. Choi, S.; Kippelen, B.; Potscavage, W.J. ITO-Free Large-Area Organic Solar Cells. *Opt. Express* **2010**, *18*, A458–A466. [CrossRef] [PubMed]

140. Hengge, M.; Livanov, K.; Zamoshchik, N.; Hermerschmidt, F.; List-Kratochvil, E.J.W. ITO-Free OLEDs Utilizing Inkjet-Printed and Low Temperature Plasma-Sintered Ag Electrodes. *Flex. Print. Electron.* **2021**, *6*, 015009. [CrossRef]

141. Baierl, D.; Fabel, B.; Lugli, P.; Scarpa, G. Efficient Indium-Tin-Oxide (ITO) Free Top-Absorbing Organic Photodetector with Highly Transparent Polymer Top Electrode. *Org. Electron.* **2011**, *12*, 1669–1673. [CrossRef]

142. Kinner, L.; Dimopoulos, T.; Ligorio, G.; List-Kratochvil, E.J.W.; Hermerschmidt, F. High Performance Organic Light-Emitting Diodes Employing ITO-Free and Flexible TiOx/Ag/Al:ZnO Electrodes. *RSC Adv.* **2021**, *11*, 17324–17331. [CrossRef]

143. Chilvery, A.; Das, S.; Guggilla, P.; Brantley, C.; Sunda-Meya, A. A Perspective on the Recent Progress in Solution-Processed Methods for Highly Efficient Perovskite Solar Cells. *Sci. Technol. Adv. Mater.* **2016**, *17*, 650–658. [CrossRef]

144. Aberra, A.S.; Peterchev, A.V.; Grill, W.M. Biophysically Realistic Neuron Models for Simulation of Cortical Stimulation. *J. Neural Eng.* **2018**, *15*, 066023. [CrossRef]

145. Hillebrandt, S.; Moon, C.-K.; Taal, A.J.; Overhauser, H.; Shepard, K.L.; Gather, M.C.; Hillebrandt, S.; Moon, C.-K.; Gather, M.C.; Taal, A.J.; et al. High-Density Integration of Ultrabright OLEDs on a Miniaturized Needle-Shaped CMOS Backplane. *Adv. Mater.* **2024**, *36*, 2300578. [CrossRef]

146. Li, J.; Ni, Y.; Zhao, X.; Wang, B.; Xue, C.; Bi, Z.; Zhang, C.; Dong, Y.; Tong, Y.; Tang, Q.; et al. Vertically Stacked Skin-like Active-Matrix Display with Ultrahigh Aperture Ratio. *Light Sci. Appl.* **2024**, *13*, 177. [CrossRef]

147. Hou, X.; Chen, S.; Tang, W.; Liang, J.; Ouyang, B.; Li, M.; Song, Y.; Shan, T.; Chen, C.C.; Too, P.; et al. Low-Temperature Solution-Processed All Organic Integration for Large-Area and Flexible High-Resolution Imaging. *IEEE J. Electron Devices Soc.* **2022**, *10*, 821–826. [CrossRef]

148. Dykstra, E.; Fralaide, M.; Zhang, Y.; Biswas, R.; Dennis Slafer, W.; Shinar, J.; Shinar, R. OLEDs on Planarized Light Outcoupling-Enhancing Structures in Plastic. *Org. Electron.* **2022**, *111*, 106648. [CrossRef]

149. Hauss, J.; Bocksrocker, T.; Riedel, B.; Lemmer, U.; Gerken, M. On the Interplay of Waveguide Modes and Leaky Modes in Corrugated OLEDs. *Opt. Express* **2011**, *19*, A851–A858. [CrossRef] [PubMed]

150. Park, C.Y.; Choi, B. Enhanced Light Extraction from Bottom Emission OLEDs by High Refractive Index Nanoparticle Scattering Layer. *Nanomaterials* **2019**, *9*, 1241. [CrossRef]

151. Li, W.; Li, D.; Dong, G.; Duan, L.; Sun, J.; Zhang, D.; Wang, L. High-Stability Organic Red-Light Photodetector for Narrowband Applications. *Laser Photon. Rev.* **2016**, *10*, 473–480. [CrossRef]

152. Shen, D.; Guan, Z.; Li, M.; Tsang, S.W.; Zhang, W.; Lo, M.F.; Lee, C.S. Trilayer Organic Narrowband Photodetector with Electrically-Switchable Spectral Range and Color Sensing Ability. *J. Mater. Chem. C* **2021**, *9*, 3814–3819. [CrossRef]

153. Xie, B.; Xie, R.; Zhang, K.; Yin, Q.; Hu, Z.; Yu, G.; Huang, F.; Cao, Y. Self-Filtering Narrowband High Performance Organic Photodetectors Enabled by Manipulating Localized Frenkel Exciton Dissociation. *Nat. Commun.* **2020**, *11*, 2871. [CrossRef]

154. Delaporte, P.; Karnakis, D.; Zergioti, I. Laser Processing of Flexible Organic Electronic Materials. In *Handbook of Flexible Organic Electronics: Materials, Manufacturing and Applications*; Woodhead Publishing: Sawston, UK, 2014; pp. 285–313. [CrossRef]

155. Teichler, A.; Perelaer, J.; Schubert, U.S. Inkjet Printing of Organic Electronics—Comparison of Deposition Techniques and State-of-the-Art Developments. *J. Mater. Chem. C* **2013**, *1*, 1910–1925. [CrossRef]

156. Park, J.U.; Hardy, M.; Kang, S.J.; Barton, K.; Adair, K.; Mukhopadhyay, D.K.; Lee, C.Y.; Strano, M.S.; Alleyne, A.G.; Georgiadis, J.G.; et al. High-Resolution Electrohydrodynamic Jet Printing. *Nat. Mater.* **2007**, *6*, 782–789. [CrossRef] [PubMed]

157. Sumaiya, S.; Kardel, K.; El-Shahat, A. Organic Solar Cell by Inkjet Printing—An Overview. *Technologies* **2017**, *5*, 53. [CrossRef]

158. Eggenhuisen, T.M.; Galagan, Y.; Biezemans, A.F.K.V.; Slaats, T.M.W.L.; Voorthuijzen, W.P.; Kommeren, S.; Shanmugam, S.; Teunissen, J.P.; Hadipour, A.; Verhees, W.J.H.; et al. High Efficiency, Fully Inkjet Printed Organic Solar Cells with Freedom of Design. *J. Mater. Chem. A* **2015**, *3*, 7255–7262. [CrossRef]

159. Sekitani, T.; Noguchi, Y.; Zschieschang, U.; Klauk, H.; Someya, T. Organic Transistors Manufactured Using Inkjet Technology with Subfemtoliter Accuracy. *Proc. Natl. Acad. Sci. USA* **2008**, *105*, 4976–4980. [CrossRef]

160. Pace, G.; Grimoldi, A.; Rengert, Z.; Bazan, G.C.; Natali, D.; Caironi, M. Inkjet Printed Organic Detectors with Flat Responsivity up to the NIR and Inherent UV Optical Filtering. *Synth. Met.* **2019**, *254*, 92–96. [CrossRef]

161. Azzellino, G.; Grimoldi, A.; Binda, M.; Caironi, M.; Natali, D.; Sampietro, M. Fully Inkjet-Printed Organic Photodetectors with High Quantum Yield. *Adv. Mater.* **2013**, *25*, 6829–6833. [CrossRef]
162. Amruth, C.; Luszczynska, B.; Szymanski, M.Z.; Ulanski, J.; Albrecht, K.; Yamamoto, K. Inkjet Printing of Thermally Activated Delayed Fluorescence (TADF) Dendrimer for OLEDs Applications. *Org. Electron.* **2019**, *74*, 218–227. [CrossRef]

163. Kant, C.; Mahmood, S.; Katiyar, M. Large-Area Inkjet-Printed OLEDs Patterns and Tiles Using Small Molecule Phosphorescent Dopant. *Adv. Mater. Technol.* **2023**, *8*, 2201514. [CrossRef]

164. Noh, Y.Y.; Zhao, N.; Caironi, M.; Sirringhaus, H. Downscaling of Self-Aligned, All-Printed Polymer Thin-Film Transistors. *Nat. Nanotechnol.* **2007**, *2*, 784–789. [CrossRef]

165. Singh, M.; Haverinen, H.M.; Dhagat, P.; Jabbour, G.E. Inkjet Printing-Process and Its Applications. *Adv. Mater.* **2010**, *22*, 673–685. [CrossRef] [PubMed]

166. Yokota, T.; Sekitani, T.; Kato, Y.; Kuribara, K.; Zschieschang, U.; Klauk, H.; Yamamoto, T.; Takimiya, K.; Kuwabara, H.; Ikeda, M.; et al. Low-Voltage Organic Transistor with Subfemtoliter Inkjet Source–Drain Contacts. *MRS Commun.* **2011**, *1*, 3–6. [CrossRef]

167. Yuk, H.; Lu, B.; Lin, S.; Qu, K.; Xu, J.; Luo, J.; Zhao, X. 3D Printing of Conducting Polymers. *Nat. Commun.* **2020**, *11*, 1604. [CrossRef] [PubMed]

168. Mele, A.; Giardini-Guidoni, A.; Teghil, R. Laser Ablation of Inorganic and Organic Materials. *J. Chem. Sci.* **1993**, *105*, 715–733. [CrossRef]

169. Srinivasan, R.; Braren, B. Ultraviolet Laser Ablation of Organic Polymers. *Chem. Rev.* **1989**, *89*, 1303–1316. [CrossRef]

170. Ravi-Kumar, S.; Lies, B.; Lyu, H.; Qin, H. Laser Ablation of Polymers: A Review. *Procedia Manuf.* **2019**, *34*, 316–327. [CrossRef]

171. Choi, J.; Yoo, Y.; Kim, H.J.; Lee, H.H.; Mottay, E.; Kling, R. Femtosecond Laser Based Manufacturing of Tailored Flexible Electronics for OLED and OPV Applications. *2019 Conf. Lasers Electro-Opt. CLEO 2019-Proc.* **2019**, *6*, 24427. [CrossRef]

172. Jipa, F.; Zamfirescu, M.; Velea, A.; Popescu, M.; Dabu, R. Femtosecond Laser Lithography in Organic and Non-Organic Materials. In *Updates in Advanced Lithography*; IntechOpen: London, UK, 2013; pp. 65–94. [CrossRef]

173. Shim, S.; Park, H.Y.; Choi, G.J.; Shin, H.C.; Kim, S.J. A Simply Fabricated Neural Probe by Laser Machining of a Thermally Laminated Gold Thin Film on Transparent Cyclic Olefin Polymer. *ACS Omega* **2019**, *4*, 2590–2595. [CrossRef]

174. Liu, J.; Gao, M.; Kim, J.; Zhou, Z.; Chung, D.S.; Yin, H.; Ye, L. Challenges and Recent Advances in Photodiodes-Based Organic Photodetectors. *Mater. Today* **2021**, *51*, 475–503. [CrossRef]

175. Kong, L.; Luo, Y.; Wu, Q.; Xiao, X.; Wang, Y.; Chen, G.; Zhang, J.; Wang, K.; Choy, W.C.H.; Zhao, Y.B.; et al. Efficient and Stable Hybrid Perovskite-Organic Light-Emitting Diodes with External Quantum Efficiency Exceeding 40 per Cent. *Light Sci. Appl.* **2024**, *13*, 138. [CrossRef] [PubMed]

176. Dou, L.; Yang, Y.M.; You, J.; Hong, Z.; Chang, W.H.; Li, G.; Yang, Y. Solution-Processed Hybrid Perovskite Photodetectors with High Detectivity. *Nat. Commun.* **2014**, *5*, 5404. [CrossRef] [PubMed]

177. Yoo, J.J.; Seo, G.; Chua, M.R.; Park, T.G.; Lu, Y.; Rotermund, F.; Kim, Y.K.; Moon, C.S.; Jeon, N.J.; Correa-Baena, J.P.; et al. Efficient Perovskite Solar Cells via Improved Carrier Management. *Nature* **2021**, *590*, 587–593. [CrossRef]

178. Dong, Y.; Zhao, C.; Wang, H.; Jiang, Y.; Fang, Y.; Wang, J.; Duan, S.; Fu, X.; Miao, J.; Hu, W. Van Der Waals Integration of Two-Dimensional Materials and Bulk Semiconductors for Infrared Photodetection Technology. *MRS Bull.* **2023**, *48*, 914–922. [CrossRef]

179. Mazaheri, A.; Lee, M.; Van Der Zant, H.S.J.; Frisenda, R.; Castellanos-Gomez, A. MoS2-on-Paper Optoelectronics: Drawing Photodetectors with van Der Waals Semiconductors beyond Graphite. *Nanoscale* **2020**, *12*, 19068–19074. [CrossRef]

180. Li, N.; Jabegu, T.; He, R.; Yun, S.; Ghosh, S.; Maraba, D.; Olunloyo, O.; Ma, H.; Okmi, A.; Xiao, K.; et al. Covalently-Bonded Laminar Assembly of Van Der Waals Semiconductors with Polymers: Toward High-Performance Flexible Devices. *Small* **2024**, *2310175*. [CrossRef]

181. Moon, D.; Lee, W.; Lim, C.; Kim, J.; Kim, J.; Jung, Y.; Choi, H.Y.; Choi, W.S.; Kim, H.; Baek, J.H.; et al. Hypotaxy of Wafer-Scale Single-Crystal Transition Metal Dichalcogenides. *Nature* **2025**, *638*, 957–964. [CrossRef]

182. Wang, S.; Li, L.; Zhang, S.; Jiang, Q.; Li, P.; Wang, C.; Xiao, R.; Li, X.M.; Song, J. Multifunctional Ultraflexible Neural Probe for Wireless Optogenetics and Electrophysiology. *Giant* **2024**, *18*, 100272. [CrossRef]

183. Yoon, Y.; Shin, H.; Byun, D.; Woo, J.; Cho, Y.; Choi, N.; Cho, I.J. Neural Probe System for Behavioral Neuropharmacology by Bi-Directional Wireless Drug Delivery and Electrophysiology in Socially Interacting Mice. *Nat. Commun.* **2022**, *13*, 5521. [CrossRef]

184. Badrulhisham, F.; Pogatzki-Zahn, E.; Segelcke, D.; Spisak, T.; Vollert, J. Machine Learning and Artificial Intelligence in Neuroscience: A Primer for Researchers. *Brain. Behav. Immun.* **2024**, *115*, 470–479. [CrossRef]

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