Optoelectronic neuromorphic systems have emerged as a promising hardware paradigm for next-generation artificial intelligence, combining the high bandwidth, parallelism, and wavelength selectivity of photonics with the adaptive plasticity of electronic materials. This mini-review provides a focused and critical overview of recent advances in material platforms, device architectures, and system-level implementations enabling light-driven neuromorphic computation. We comparatively analyze key photoresponsive materials—including halide perovskites, low-dimensional semiconductors, phase-change and oxide systems, and organic–inorganic hybrids—highlighting their underlying physical mechanisms such as photocarrier generation, charge trapping, ion migration, and excitonic effects. Particular emphasis is placed on device concepts, including optoelectronic synapses, neurons, and crossbar arrays, as well as their integration into in-sensor and hybrid photonic–electronic architectures for machine vision and real-time perception. A benchmarking analysis is presented to evaluate trade-offs in speed, energy consumption, retention, scalability, and stability across different material systems. Finally, we discuss key technological bottlenecks—including device variability, lack of standardized metrics, and integration challenges—and outline future research directions toward scalable, energy-efficient, and application-specific optoelectronic neuromorphic processors. Optoelectronic neuromorphic systems have rapidly advanced, merging photonics’ speed with electronics’ adaptability to emulate neural functions. This mini-review highlights recent progress in these systems, focusing on material platforms, device physics, and system architectures for AI applications. Researchers have developed optoelectronic synapses using materials like halide perovskites and two-dimensional semiconductors, each offering unique benefits such as spectral tunability and high responsivity. These materials enable devices to perform complex tasks like vision processing directly within hardware, reducing the need for extensive electronic processing. Significant findings include the development of perovskite-based systems that balance speed and energy efficiency, though challenges in stability and scalability remain. The review suggests that future advancements will require coordinated efforts in materials science and device engineering to overcome these challenges, paving the way for next-generation intelligent computing platforms."This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author." Optoelectronic neuromorphic systems integrate light-responsive materials with adaptive electronic functionalities to enable energy-efficient artificial intelligence. Diverse material platforms—including two-dimensional semiconductors, halide perovskites, oxide memristors, and hybrid architectures—exploit mechanisms such as photocarrier generation, charge trapping, ion migration, excitonic effects, and interfacial modulation to emulate synaptic plasticity and neuronal spiking. By combining sensing, memory, and computation within unified hardware, these devices enable in-sensor vision processing, real-time perception, and low-power neuromorphic computing, providing a promising route toward scalable next-generation intelligent optoelectronic systems.

Optoelectronic inhibitory synapses play critical roles in modulating neural activity and maintaining the balance between excitation and inhibition within artificial neural circuits. However, most reported devices fail to properly emulate depression-related synaptic functions under optical stimulation. In this work, we demonstrate an optoelectronic inhibitory synaptic device based on lead sulfide (PbS) nanocrystals capped with iodide ligands. Crucially, the devices operate in the visible range (RGB), essential for retina-inspired color sensing, demonstrating inhibitory postsynaptic current (IPSC) behavior under 450, 550 and 740 nm illumination. A low power consumption of similar to 40 nJ was also recorded under red light illumination. The system successfully emulates fundamental synaptic behaviors including paired-pulse depression (PPD), spiking-number-dependent plasticity (SNDP), and spiking-rate-dependent plasticity (SRDP), enabling biologically plausible visual processing.

Halide perovskite quantum dots (PQDs) possess outstanding optical properties for display technologies, but their commercial use is limited by surface defects and poor environmental stability. In this work, we introduce hydroxyl‐terminated polystyrene (PS–OH) as a bifunctional ligand incorporated directly during CsPbBr 3 PQD synthesis. The terminal ‐OH groups provide strong coordination to under‐coordinated Pb 2+ sites, suppressing nonradiative recombination, while the polystyrene backbone forms a protective hydrophobic shell. This dual action leads to substantially enhanced photoluminescence quantum yield and substantially improved resistance to moisture‐induced degradation. When used as a color‐conversion layer on top of blue LEDs, the PS–OH modified PQDs enable more efficient photon down‐conversion and improved device‐level performance. These results demonstrate that functionalized polymers, employed as in situ ligands, offer a powerful approach for stabilizing and enhancing brightness of perovskite quantum dots for advanced optoelectronic applications.

Formamidinium lead iodide (FAPbI3) perovskite light-emitting diodes (PeLEDs), showing record external quantum efficiencies (EQEs), are typically fabricated in an n-i-p structure through the deposition of a precursor solution rich in formamidinium iodide (FAI) on top of ZnO/ITO substrates. The ZnO electron transport layer is known to interact with the precursor solution during the fabrication, which can help to remove most of the excess FAI to ensure good stoichiometry and crystallinity as well as promote the formation of films with strong emission. However, residual FAI often remains on the perovskite surface, where it compromises device efficiency and stability. To mitigate this, we introduce here a thiol-functionalized tertiary ammonium halide, 2-diethylaminoethanethiol hydrochloride (DEAET), on top of FAPbI3. Beyond effectively regulating excess FAI, DEAET also acts as a passivation agent through synergistic hydrogen-bonding and electrostatic interactions. This dual functionality suppresses non-radiative pathways and promotes radiative recombination. The resulting DEAET-passivated LEDs exhibit increased EQE and an impressive 15-days air stability without encapsulation. The encouraging findings of this study lay the foundation for the utilization of thiol-based salts as efficient agents for interface engineering in FAPbI3 perovskite, that opens new possibilities for air-stable perovskite light-emitting diodes.

Two-dimensional (2D) Ruddlesden-Popper halide perovskites are promising candidates for flexible optoelectronic memories owing to their tunable composition, reduced power consumption, and intrinsic structural anisotropy. Yet, achieving long-term reliability and environmental stability in perovskite-based memristors remains a major challenge. Here, we demonstrate highly oriented (PEA)2MA4Pb5I16 thin films integrated into optoelectronic memristors, where interface optimization with softly deposited Ag nanoparticles (NPs) enhances device performance without damaging the active layer. The resulting forming-free ITO/Ag NPs/(PEA)2MA4Pb5I16/Pt NPs/ITO devices exhibited robust binary resistive switching, with a large memory window (∼105), excellent endurance (>1011 cycles), and a low SET voltage (∼0.2 V). The confined conducting filament (CF) formation, induced by the vertically aligned crystalline domains, combined with the energetically favorable atom extraction from Ag NPs, ensured stable and low power switching under both electrical and optical stimuli. Numerical simulations were further conducted, elucidating the origin of the enhanced switching behavior and the role of crystal orientation in mitigating perovskite degradation pathways. The device conductance can be also modulated by applying light pulses, yielding a low energy consumption (∼100 nJ per programming event at 740 nm), whereas multicolor perception was achieved using additional wavelengths (450 and 550 nm). A 10 × 10 flexible crossbar array was fabricated, demonstrating high device yield, reproducible performance under bending, and excellent environmental stability over an extended period. This work establishes (PEA)2MA4Pb5I16 as a benchmark material for reliable, low-power, and optically programmable memristors and provides a scalable strategy for next-generation perovskite-based optoelectronic architectures.

CuInS2 quantum dots (CIS-QDs) are promising nontoxic and air-stable materials that can be readily synthesized through a controllable heat-up reaction between metal cation precursors and a sulfur source, enabling tunable photoluminescence (PL) across the visible to near-infrared range. However, their broader application in light-emitting diodes (LEDs) is limited by inefficient radiative recombination and a photoluminescence quantum yield (PLQY) significantly below unity. To address this challenge, we introduce formamidinium acetate (FAAc) into the reactiona common additive in metal halide perovskite precursor solutions. FAAc modulates precursor chemistry by forming complexes that control the size and bandgap of the resulting nanocrystals, without significantly altering their crystal structure. We also find that FAAc regulates the stoichiometry, inducing substantial Cu-(I) deficiency and a corresponding decrease in the lattice work function. These effects, combined with the potential passivation of surface defects by nitrogen-containing byproducts of FAAc decomposition, contribute to a dramatic enhancement of PLQY, from 43% to 94%, and an increase in PL lifetime from 0.2 to 7.2 μs. Proof-of-concept LED devices incorporating FAAc-modified CIS-QDs exhibit bright red emission, demonstrating FAAc as an effective additive for engineering the electroluminescence of CIS-QDs. We propose that this strategy could be extended to other ternary quantum dots to enable high-performance optoelectronic applications.

Bi2Se3 is an essential thermoelectric semiconductor and a prototypical three-dimensional topological insulator. The as-grown single crystals exhibit hexagonal or reduced trigonal morphology and uniformly flat surfaces down to a few quintuple layers. In this study, we present a comparative analysis of Shubnikov-de Haas (SdH) oscillations and Hall transport measurements conducted under both Physical Property Measurement System (0-14 T) and pulsed high-field (up to 70 T) conditions, aiming to assess whether ultrahigh magnetic fields can enhance surface sensitivity and uncover nonclassical transport features. Across both regimes, we observe a single oscillation frequency and a trivial Berry phase with a 1/8 phase shift, consistent with bulk-dominated transport through a single three-dimensional Fermi pocket. Notably, a deviation from linear Hall resistance as the field strength exceeds 30 T at 1.3 K suggests that the system is entering the 3D bulk quantum limit, where additional transport channels-possibly surface-related-may begin to emerge. While clear topological surface-state signatures are not resolved, our results provide a detailed reference for evaluating the onset of nonclassical behavior in highly doped Bi2Se3. The comparative approach outlined here clarifies the limitations of field-only methods and highlights the necessity of bulk suppression techniques for accessing topological transport in similar systems.

In this work, aqueous nutrient solutions replicating bioreactor culture media for microalgae were analyzed using spontaneous Raman spectroscopy. Focusing on nitrate, sulfate, glucose, and phosphate, the study evaluated their potential for real-time monitoring in cell cultivations such as Chlorella vulgaris. Univariate analysis, based on Raman intensities of specific nutrient peaks, was conducted and compared to multivariate analysis results. Four multivariate calibration models were developed using partial least squares regression (PLSR), achieving high calibration and validation performance, with R2 values above 0.99 and low RMSECV, indicating strong calibration accuracy. The study also examined the limit of detection (LOD) for each nutrient, finding that LODs for nitrate, sulfate, and glucose reached levels relevant for algae bioreactors even without the application of enhanced Raman techniques. To further validate the PLS models, independent real bioreactor samples were analyzed, showing strong predictive accuracy (RP2: 0.9661-0.9892) and low RMSEP values. Additional testing with five samples collected over a Chlorella vulgaris cultivation run (day 0 to day 9) confirmed the models' robust performance under real bioprocess conditions. Limitations in practical applications, such as phosphate's relatively high LOD, were also identified. The results suggest that Raman spectroscopy, combined with multivariate analysis, could deliver precise and reliable detection of critical nutrients and their concentrations in bioreactor culture media. This potential of the Raman technique, along with insights into nutrient LODs, PLS model accuracy, and practical application challenges, provides a solid foundation for future research and development in industrial bioprocess monitoring.

Integrating multicolor perception with neuromorphic vision systems, capable of emulating the procedures of image detection, storage, and local processing, represents a significant advancement in artificial visual technologies. However, challenges related to data fusion, system complexity, and stability must be addressed to fully realize the potential of this technology. In this work, a low-dimensional/three-dimensional (LD/3D) halide perovskite heterostructure consisting of Ag/LD perovskitoid/3D CsFAMA/ITO is fabricated, demonstrating excellent stability for 2 months combined with the co-existence of two switching modes, namely volatile and non-volatile. The former mode is leveraged to construct the nodes of the reservoir computing architecture, where the fusion rate of the electrical and optical signals is examined to achieve maximum recognition accuracy of multicolor handwritten MNIST images (84%). An ultra-low power consumption of 400 fJ per synaptic weight change is also recorded during red light irradiation. By combining experiments with different top electrode materials and extensive Density Functional Theory calculations on metal atom diffusion and clustering in the materials of interest, key atomic scale processes are identified that underlie the switching behavior and lead to improved memory performance. The ability of the proposed device configuration to accurately carry out multimodal recognition tasks opens new possibilities for realizing biomimetic systems.

The development of artificial neural networks with biorealistic computing properties represents a frontier in the neuromorphic computing era. However, achieving compact and energy-efficient integration of silicon-based synapses and neurons remains challenging due to complexities in their electrical circuits. Herein, we fabricated a low power Ag/SiO2/FA2PbI4/Pt nanoparticles/ITO bilayer memristor with reconfigurable properties, exhibiting dual switching modes and neuromorphic functionalities. These effects were experimentally investigated through transient response and endurance measurements, while valuable insights were provided using a comprehensive numerical model. The SiO2/FA2PbI4 and FA2PbI4/Pt nanoparticle interfaces played a critical role in regulating ion migration, stabilizing filament dynamics and enhancing device reliability. A compact optoelectronic neuromorphic system was demonstrated by integrating synaptic and neuronal elements, enabling precise control of the firing activity. An ultralow power consumption (∼10 fJ/spike) was achieved, comparable to that of the human brain and state-of-the-art memristive technologies, thereby paving the way for energy-efficient optoelectronic computing platforms.

Herein we report on the synthesis, crystal structure, vibrational and optoelectronic properties as well as DFT calculations of the air stable lead free TMS3M2I9 (TMS = (CH3)3S; M = Bi, Sb) perovskite halides. These compounds were synthesized with high purity using solid-state and solution methods by reacting MI3 and (CH3)3SI. Hexagonal, P63mc (No. 186), crystal structures of both compounds were confirmed at 393 K by single crystal Xray Scattering and Rietveld analysis with a 0D network of [M2I9]3- dimer structure. TMS3M2I9 hexagonal undergoes structural phase transitions to the monoclinic crystal, twinning in the space group C121 (#5), at room temperature of 298 K. Investigation of lattice and molecular vibrations performed by Raman spectroscopy in the temperature range of - 190-180 degrees C indicated the presence of hexagonal and monoclinic phases and confirmed the robustness of the synthesized materials related to the high stability of the trimethyl sulfonium cation in the perovskite framework. Band gaps of 2.1 eV and 2.3 eV were experimentally determined for the Bi- and Sbcontaining perovskites, respectively. First-principles calculations were conducted to assess the energy band gap values, electronic structure, and density of states, yielding results consistent with the experimentally determined values. The new compounds were successfully incorporated in DSSCs, justifying their broad application potential.

The intriguing properties of two-dimensional (2D) materials render them attractive for energy efficient neuromorphic computations because their atomic scale thickness can alleviate the power requirements of the device. In parallel, their layered structure can be leveraged to optically program the device and further reduce power consumption. Along these lines, in this work, a forming free memory device consisting of a MoS _2 monolayer with dimensions of ∼100 μ m decorated with small (∼3 nm diameter) Pt nanoparticles (NPs) was fabricated. The impact of the Pt NPs’ surface density on the optoelectronic neuromorphic properties under ultraviolet irradiation ( λ = 390 nm) was systematically investigated. More specifically, the reference samples without Pt NPs exhibited only synaptic behavior, while the NPs-based one (surface density of ∼2 × 10 ^12 NPs cm ^−2 ) presented a neuron-like response. An elevated surface density (∼5 × 10 ^12 NPs cm ^−2 ) just reduced the frequency of the generated spikes. Various synaptic plasticity and neuronal coding schemes were experimentally demonstrated. The underlying origins of this behavior were attributed to band bending in the Pt NPs-MoS _2 interface, leading to a trapping effect of electrons on the metallic NPs, evidenced by photoluminescence quenching, followed by a detrapping process, demonstrated by the reduced firing rate when using the Pt layer with the higher surface density. This versatility of the devices was leveraged to simulate the behavior of a fully optoelectronic spiking neural network. Considering the low energy consumption per spike (∼400 pJ) during the experimental recorded neuromorphic properties, a dramatically reduced power consumption of ∼320 μ W was extracted during the pattern recognition of optical images. Our work provides valuable insights for emulating the artificial synaptic and neuronal behavior and paves the way for the development of next-generation and fully memristive artificial neural networks with a very small energy footprint.

Raman spectroscopy is a promising non-invasive technique not only for the rapid and accurate detection of colorectal cancer (CRC) but also for the identification of positive surgical margins. In this study, micro-Raman spectroscopy was used to explore biochemical differences in surgically resected intestinal segments, with a focus on boundary tumor zone. Spectral and statistical analyses, including Partial least squares discriminant analysis (PLS-DA), were performed to identify significant molecular signatures and distinguish different tissue types. Our findings suggest that boundary tumor zone contain a mix of cancerous and normal cells, complicating the discrimination of these regions. Despite this challenge, we achieved a classification accuracy of 82 % for tumor margin differentiation from normal tissue along with identifying several biochemically significant spectroscopic differences. Rapid Raman measurements using a portable system, taken from resected tissues immediately after surgery, further demonstrated the technique's ability to differentiate cancerous from healthy tissues with 97 % accuracy, 98 % sensitivity, and 96 % specificity, underscoring the potential of Raman spectroscopy for real-time clinical applications in CRC surgery.

Bis(trifluoromethane)sulfonimide (TFSI) treatment results in near-unity photoluminescence quantum yields in monolayer transition-metal dichalcogenides, such as MoS2, due to passivation of native defects. Surprisingly, this simple post-treatment process has never been tested in the case of metal halide perovskites which suffer from limited radiative recombination due to charge carrier trapping. Here, we adopt this strategy and treat methylammonium lead iodide perovskite films with TFSI solutions. By employing photoluminescence spectroscopy, the appearance of brighter films proves a net passivation effect, while chemical analysis explains that this is due to strong interactions between S 00000000 00000000 00000000 00000000 11111111 00000000 11111111 00000000 00000000 00000000 O groups of TFSI and under-coordinated Pb2+. A simultaneous passivation of iodide vacancies also leads to a reduction of n-doping at the perovskite surface and thus better hole extraction through spiro-MeOTAD which is deposited on top. These two effects combined (chemical passivation and de-doping) result in enhanced stabilized efficiencies for the as-fabricated n-i-p solar cells. The findings pave the way for the use of TFSI-based solutions to improve the performance of perovskite optoelectronic devices.

Calcific aortic valve stenosis (CAVS), characterized by calcium deposition in the aortic valve in a multiannual process, is associated with high mortality and morbidity. To understand phenomena at its early stages, reliable animal models are needed. Here, we used a critically revised high-fat vitamin D2 diet rabbit model to unveil the earliest in vivo-derived mechanisms linked to CAVS progression. We modeled the inflammation-calcification temporal pattern seen in human disease and investigated molecular changes before inflammation. Coupling comprehensive multiomics and vibrational spectroscopy revealed that among the many procedures involved, mechanotransduction, peroxisome activation, DNA damage-response, autophagy, phospholipid signaling, native ECM proteins upregulation, protein cross-linking and self-folding, are the most relevant driving mechanisms. Activation of Complement 3 receptor, Immunoglobulin J and TLR6 were the earliest signs of inflammation. Among several identified key genes were AXIN2, FOS, and JUNB. Among 10 identified miRNAs, miR-21-5p and miR-204-5p dominated fundamental cellular processes, phenotypic transition, inflammatory modulation, and were validated in human samples. The enzymatic biomineralization process mediated by TNAP was complemented by V-type proton ATPase overexpression, and the substitution of Mg-pyrophosphate with Ca-pyrophosphate. These data extend our understanding on CAVS progression, facilitate the refinement of pathophysiological hypotheses and provide a basis for novel pharmaceutical therapy investigations.

Raman spectroscopy has emerged as a powerful tool in medical, biochemical, and biological research with high specificity, sensitivity, and spatial and temporal resolution. Recent advanced Raman systems, such as portable Raman systems and fiber-optic probes, provide the potential for accurate in vivo discrimination between healthy and cancerous tissues. In our study, a portable Raman probe spectrometer was tested in immunosuppressed mice for the in vivo localization of colorectal cancer malignancies from normal tissue margins. The acquired Raman spectra were preprocessed, and principal component analysis (PCA) was performed to facilitate discrimination between malignant and normal tissues and to highlight their biochemical differences using loading plots. A transfer learning model based on a one-dimensional convolutional neural network (1D-CNN) was employed for the Raman spectra data to assess the classification accuracy of Raman spectra in live animals. The 1D-CNN model yielded an 89.9% accuracy and 91.4% precision in tissue classification. Our results contribute to the field of Raman spectroscopy in cancer diagnosis, highlighting its promising role within clinical applications.

Advanced Raman spectroscopy (RS) systems have gained new interest in the field of medicine as an emerging tool for in vivo tissue discrimination. The coupling of RS with artificial intelligence (AI) algorithms has given a boost to RS to analyze spectral data in real time with high specificity and sensitivity. However, limitations are still encountered due to the large amount of clinical data which are required for the pre-training process of AI algorithms. In this study, human healthy and cancerous colon specimens were surgically resected from different sites of the ascending colon and analyzed by RS. Two transfer learning models, the one-dimensional convolutional neural network (1D-CNN) and the 1D–ResNet transfer learning (1D-ResNet) network, were developed and evaluated using a Raman open database for the pre-training process which consisted of spectra of pathogen bacteria. According to the results, both models achieved high accuracy of 88% for healthy/cancerous tissue discrimination by overcoming the limitation of the collection of a large number of spectra for the pre-training process. This gives a boost to RS as an adjuvant tool for real-time biopsy and surgery guidance.

Raman spectroscopy (RS) techniques are attracting attention in the medical field as a promising tool for real-time biochemical analyses. The integration of artificial intelligence (AI) algorithms with RS has greatly enhanced its ability to accurately classify spectral data in vivo. This combination has opened up new possibilities for precise and efficient analysis in medical applications. In this study, healthy and cancerous specimens from 22 patients who underwent open colorectal surgery were collected. By using these spectral data, we investigate an optimal preprocessing pipeline for statistical analysis using AI techniques. This exploration entails proposing preprocessing methods and algorithms to enhance classification outcomes. The research encompasses a thorough ablation study comparing machine learning and deep learning algorithms toward the advancement of the clinical applicability of RS. The results indicate substantial accuracy improvements using techniques like baseline correction, L2 normalization, filtering, and PCA, yielding an overall accuracy enhancement of 15.8%. In comparing various algorithms, machine learning models, such as XGBoost and Random Forest, demonstrate effectiveness in classifying both normal and abnormal tissues. Similarly, deep learning models, such as 1D-Resnet and particularly the 1D-CNN model, exhibit superior performance in classifying abnormal cases. This research contributes valuable insights into the integration of AI in medical diagnostics and expands the potential of RS methods for achieving accurate malignancy classification.

Heterostructured photocatalytic materials in the form of photonic crystals have been attracting attention for their unique light harvesting ability that can be ideally combined with judicious compositional modifications toward the development of visible light-activated (VLA) photonic catalysts, though practical environmental applications, such as the degradation of pharmaceutical emerging contaminants, have been rarely reported. Herein, heterostructured MoS2-TiO2 inverse opal films are introduced as highly active immobilized photocatalysts for the VLA degradation of tetracycline and ciprofloxacin broad-spectrum antibiotics as well as salicylic acid. A single-step co-assembly method was implemented for the challenging incorporation of MoS2 nanosheets into the nanocrystalline inverse opal walls. Compositional tuning and photonic band gap engineering of the MoS2-TiO2 photonic films showed that integration of low amounts of MoS2 nanosheets in the inverse opal framework maintains intact the periodic macropore structure and enhances the available surface area, resulting in efficient VLA antibiotic degradation far beyond the performance of benchmark TiO2 films. The combination of broadband MoS2 visible light absorption and photonic-assisted light trapping together with the enhanced charge separation that enables the generation of reactive oxygen species via firm interfacial coupling between MoS2 nanosheets and TiO2 nanoparticles is concluded as a competent approach for pharmaceutical abatement in water bodies.