A Z-scheme electron transfer mechanism, coupled with plasmonic effects, is highly desirable for achieving enhanced photocatalytic performance. Graphitic carbon nitride (g-C3N4)-based Z-scheme photocatalytic systems have been widely reported due to their cost-effectiveness, photo-chemical stability, and environmental sustainability. In this study, we report the fabrication of a novel Z-scheme heterojunction photocatalyst, Agdecorated FeCo2O4/g-C3N4 (Ag@FCO-PCN), synthesized via an integrated approach involving automoisturization, polycondensation, ultrasonication, and photoreduction techniques. Electron paramagnetic resonance (EPR) analysis was employed to investigate atomic vacancies in pure FeCo2O4 and g-C3N4, which play a crucial role in enhancing charge carrier separation. The synthesized photocatalysts were extensively characterized using a suite of optical and physicochemical techniques. The photocatalytic performance of the ternary Ag@FCO-PCN nanocomposite was evaluated through aqueous phase degradation of methylene blue (MB), used as a model organic pollutant. Under visible light irradiation and in the presence of potassium persulfate (PS), the ternary composite exhibited a photo-Fenton-like degradation mechanism, achieving nearly 99 % MB removal within 3 h. Among the tested materials, Ag@FeCo2O4/g-C3N4 demonstrated the highest charge separation efficiency compared to pristine g-C3N4, FeCo2O4, and the binary FeCo2O4/g-C3N4 composite. Scavenger experiments confirmed the generation of reactive species including SO4 center dot-, h+, O2 center dot-, and OH center dot, with SO4 center dot- playing a dominant role in the degradation pathway, thereby supporting the proposed Z-scheme mechanism. The ternary composite was easily recovered using magnetic separation and exhibited excellent cyclic stability and reusability, as confirmed by post-reaction characterizations. This study presents new insights into the development of stable, efficient, and reusable metal nanoparticle-decorated metal oxide-based photo Fenton like heterogeneous catalysts for advanced photocatalytic applications.

The demand for high-mobility, energy-efficient oxide-based transistors is rapidly increasing to meet the performance and scaling requirements of next-generation electronics. However, intrinsic defect states, interfacial scattering, and carrier localization in oxide semiconductors severely limit their potential, leading to degraded transport characteristics and poor operational stability. Overcoming these fundamental limitations is essential for enabling high-speed, low-power electronic devices. In this study, we demonstrate a quasi-two-dimensional electron gas (quasi-2DEG) transistor, enabled by atomic layer deposition of an ultrathin Al2O3/In2O3 heterostructure on a SiO2/Si substrate at a low thermal budget below 200 degrees C. Electrical measurements demonstrate a high apparent field effect mobility of 91 cm(2)/(V & centerdot;s), an ultra-low off state current (similar to 10(-15) A), an on/off ratio exceeding 10(12), and a subthreshold swing of similar to 100 mV/dec, ensuring both performance and energy efficiency. Additionally, the device exhibits a positive threshold voltage (V-th> 0 V) and near-zero hysteresis, highlighting its reliability for practical applications. The top Al2O3 layer not only facilitates 2DEG formation but also serves as an effective passivation layer, further enhancing device longevity and stability. These results establish oxide-based 2DEG transistors as a transformative platform, offering a viable route toward high-performance, low-power electronics with broad implications for next-generation semiconductor technology.

ABSTRACT Always‐on acoustic recognition is becoming essential for intelligent vehicles, robotics, and embedded sensor nodes, yet conventional audio pipelines remain energy‐intensive because they rely on continuous sampling, digital feature extraction, and computation of heavy inference. Materials that encode temporal sound features directly in hardware can enable privacy‐preserving on‐device intelligence with reduced complexity. Here we demonstrate device‐level temporal encoding for edge sound recognition using a ferroelectric oxide heterostructure that merges leaky integration, facilitation, and short‐term relaxation within a single two‐terminal device. The W/HZO/ZnO/W device exhibits polarization‐controlled transport coupled to oxygen vacancy dynamics, yielding a pronounced polarity‐dependent response with memory‐like behavior in one bias regime and resistive operation in the opposite regime. Under pulse stimulation, the device shows progressive current accumulation followed by gradual decay, enabling paired pulse facilitation and short‐term memory without an external reset. Integrated with a microcontroller that converts audio waveforms into pulse trains, distinct spoken commands generate reproducible transient current fingerprints that serve as separable physical features for recognition. Complementing the measured device encoding, network‐level simulations using a compact multi‐branch spiking architecture indicate the classification achievable when this event‐coded representation feeds a trained spiking network, pointing toward event‐driven inference with lower computational load than dense digital baselines.

Sensors are the universal interface of intelligent systems, yet they generate large volumes and mostly unnecessary data streams, much of it irrelevant to the eventual classification. Conventional architectures transmit these data at full resolution to multilayer perceptron and convolutional transformer classifiers. These are accurate, but they require task-specific training and are memory-traffic-intensive, resulting in substantial bandwidth, latency, and energy challenges before digital inference. This motivates a universal near-sensor computing framework. To address this, we introduce the electrical parallel-resistive input summation module (E-PRISM), a memristive near-sensor architecture that performs analog subset coding, composing many inputs into a single readout with 2(10) resolvable states in a single parallel measurement. This universal and scalable primitive reduces raw sensor bandwidth by nearly an order of magnitude, lowers per-frame energy by similar to 20 & times;, and decreases latency by more than two orders of magnitude relative to MLP pipelines. As a stand-alone classifier, E-PRISM is effective or low-dimensional streams, achieving similar to 95% on noisy 10-bit pattern recognition, similar to 88% on 2D shape classification, and similar to 99% on motion-trajectory tasks without digital post-processing. For higher-dimensional inputs, coupling E-PRISM to a lightweight Kolmogorov-Arnold network yields a hybrid that consistently exceeds 95% accuracy across domains including 3D object recognition, wavelength discrimination, sensor fusion, and M-of-N safety logic.

Accurate color sensing is essential for applications ranging from materials analysis to autonomous vision, yet compact systems that can classify color directly at the device level without bulky optics or computationally intensive post-processing remain limited. Here, a machine learning-free, self-powered retinomorphic pyro-photodetector is demonstrated for direct electrical wavelength encoding from 365 to 940 nm. The multiterminal Ag/ZnO/n-Si/Ag device uses electrostatically balanced built-in potentials to convert incident wavelength into distinct photo and pyroelectric current fingerprints, enabling direct in-sensor spectral discrimination without spectral reconstruction and external neural processing. The device achieves wavelength decoding with less than 3 nm accuracy and a pyroelectric current rise-time of ∼46 µs. At the system level, direct in-sensor encoding enables end-to-end wavelength classification with 300 ms latency while reducing downstream energy and computational complexity. The encoded electrical readout enables accurate color classification and is further applied to plant health and pigment-state sensing, as well as quantification of 0% to 40% water adulteration in milk, curd, coconut water, and salt and sugar solutions, with classification accuracy above 92%. This work establishes a portable retinomorphic platform for real-time, energy-efficient, and high-precision spectral sensing, providing a general route toward direct in-sensor color recognition and wavelength classification.

Ferroelectric materials are gaining significant attention as next-generation semiconductor devices due to their unique properties. In this study, we developed HfO2–TiO2 (HTO) nanolaminates using PEALD method and investigated their film structure and ferroelectric performance. GIXRD analysis revealed the presence of the HTO orthorhombic phase, TiO2 anatase phase, and β-W phase in the nanolaminate films after annealing. Electrical characterization revealed a leakage current density of 9.45 × 10–7 A/cm2 at applied voltage of 1 V, which is close to the current industry standard for gate oxides. The P–V hysteresis loops exhibited a maximum remanent polarization of 4.65 μC/cm2, underscoring the tunable ferroelectric and electrical properties of HTO nanolaminates achieved by carefully controlling the deposition sequence and post-deposition annealing. It is found that the C–V measurements indicated a dielectric constant (k) ranging from 32 to 33 over a frequency range of 10 kHz to 1 MHz, suggesting its viability as a high-k dielectric material in advanced semiconductor application. Additionally, the endurance tests showed remarkable stability, withstanding up to 107 cycles. This study optimizes the process parameters for fabricating ferroelectric HTO thin films using PEALD and highlights their potential applications in ferroelectric-based devices.

Reconfiguring differential capacitance (DC = dC/dV) holds significant promise for the advancement of energy-efficient and multifunctional electronic components. Typical passive components like resistors or conventional capacitors lack the ability to exhibit cumulative charge behavior, making them unsuitable for advanced computing and data processing tasks. In this study, we introduce a nanodomain HfZrO2 ferroelectric device capable of modulating DC values from positive to negative, a feature enabled by its intrinsic and randomly oriented ferroelectric polarization. The device demonstrates dynamic multilevel hysteresis loop openings in capacitance-voltage characteristics, confirmed through advanced characterization techniques, including vector piezoresponse force microscopy and transmission electron microscopy. Using an Op-Amp integrator circuit, we derived cumulative charge (SumQ) from capacitance data with unprecedented control and predictability, achieving high linearity (>99 %) and distinct charge levels, a feat unattainable in typical resistors or capacitors. Furthermore, the SumQ data was successfully employed in machine learning (ML) models to classify human presence and absence based on WiFi received signal strength indicator signals. This application underscores the potential of our device in enabling advanced ML-driven electronic systems and security applications. These results establish our device as an innovation, bridging the gap between physical electronic behavior and computational applications, and paving the way for next-generation high-performance electronic systems.

The rapid evolution of real-time, energy-efficient sensing technologies is paramount for innovations in fields such as 3D imaging, gesture recognition, and human-machine interaction. However, conventional photodetectors are limited by their slow response to sudden changes in light and high-power consumption during continuous monitoring, while event sensors excel in detecting instantaneous changes but are ineffective at capturing gradual intensity shifts. To address these critical limitations, we present a ‘hybrid’ near-infrared (NIR) Au/Ga2O3/n-Si/Au photodetector, designed to simultaneously detect both instantaneous events and gradual light intensity variations. Our single-pixel NIR photodetector leverages capacitance changes for rapid event detection and photocurrent generation at the junction to monitor continuous light variations. This dual-functionality architecture allows for real-time adaptability in dynamic and static environments. We further demonstrate its capability in real-time z-distance sensing, integrating a deep neural network for precise depth measurements. Additionally, the hybrid single-pixel photodetector facilitates real-time sign language recognition, translating gestures into word, thereby offering a new paradigm in human-machine interaction. By bridging the gap between traditional and event-based sensors, this hybrid NIR device not only meets the demands of next-generation sensing technologies but also redefines the potential for innovation in the fields, such as in object classification, gesture recognition, and beyond.

Event-based vision sensors offer sparse, low-latency alternatives to frame-based imaging, but their lack of embedded memory and static scene awareness limits use in intelligent systems. Most designs capture only transient changes and rely on external processors for classification and motion prediction, lacking the temporal continuity and energy efficiency needed for real-time, context-aware decision-making. Here, we report a neuromorphic photodetector that integrates voltage-controlled static sensing, event detection, and tunable shortterm memory (STM) within a single pixel. By combining a photoactive silicon layer with a ferroelectric HfZrO2 stack, the device enables bias-dependent transitions between self-powered event spikes (-73 mu s), steadystate photocurrent, and programmable STM-like decay responses. By coupling the sensor array to a fieldprogrammable gate array that performs on-chip learning and inference, the system intrinsically encodes realtime temporal dynamics to directly classify spatiotemporal patterns-such as gestures, logic sequences, and Morse code-with over 93 % accuracy, while also enabling real-time motion prediction of dynamic objects. The resulting architecture reduces power consumption by over 1000 x and boosts inference speed by more than 200 x compared to conventional event sensors with software-based neural networks, while STM elevates prediction accuracy from 20 % to over 80 % in dynamic position tracking tasks. This unified sensor-processor platform offers a scalable route toward compact, adaptive, and low-power neuromorphic vision systems.

Traditional photodetectors are often limited to specific tasks, such as continuous illumination sensing or event detection, relying on distinct architectures for functionalities like static and dynamic pattern recognition. Achieving seamless integration of temporal processing, event-driven adaptability, and static pattern recognition within a unified architecture remains a significant challenge. These limitations are further intensified by environmental variations, such as fluctuating lighting and obscured conditions, which undermine their reliability in real-world applications. Here, we present a reconfigurable and adaptive single-pixel photodetector capable of transitioning seamlessly between photodetector, synaptic, and retinomorphic modes by altering its operating conditions. Leveraging quantum-well-inspired charge trapping mechanisms, the device achieves ultrafast transient detection, cumulative signal integration, and robust adaptability, mimicking key functionalities of biological vision. Experimental results, supported by simulations, demonstrate real-time adaptive vision, multi-object tracking, and enhanced pattern recognition under diverse environments, ranging from intense sunlight to obscured conditions. The device transcends conventional sensing by enabling object classification through machine learning, achieving over 94 % accuracy using multidimensional metrics, even for objects with similar shapes and sizes. This multifunctional photodetection platform addresses critical challenges in next-generation sensing technologies by combining adaptability, high-speed response, and intelligent classification, paving the way for transformative applications in autonomous vision, neuromorphic computing, and intelligent imaging systems.

Electrolyte-gated semiconductor devices are the building blocks for next-generation optoelectronics due to their memory effect and slow ion kinetics, mimicking synapses. Halide perovskites have memory effects, mixed electronic and ionic conductivity, and optical responses, which are extremely promising for this application. However, most high-performance halide perovskites are unstable in liquid electrolytes due to solvent intercalation. We have stabilized the Ruddlesden-Popper 2D perovskites by introducing an ion-transporting membrane separator between the thin film and a quasi-solid-state gel electrolyte interface. Here, we demonstrate an electrolyte-gated three-terminal device that operates as a switchable OR, AND, and a universal NOR gate, with one input being electrical and the other being optical, based on negative, zero, and positive gate voltages, respectively. We also demonstrated all electrical XOR gates, electrical and optical NOT, and BUFFER gates using the same. Overall, this work will open new opportunities for halide perovskites and contribute to a deeper understanding of their photophysical properties.

The increasing demand for adaptive and energy-efficient systems highlights the limitations of conventional devices, which are often constrained by rigid and deterministic behaviors. In contrast, nature, with its inherent randomness and variability, presents an opportunity to harness this intrinsic disorder to advance functional devices beyond traditional paradigms. However, the effective utilization of intrinsic randomness in physical devices for adaptive, multilevel data processing and beyond has yet to be fully demonstrated. By leveraging Shannon entropy as a quantifiable measure of randomness, we demonstrate how ferroelectric In2O3/HfO2 thin-film transistors (TFTs) can exhibit adaptive, multilevel dynamic behavior, essential for applications in secure systems and neuromorphic computing. The observed multilevel dynamics are attributed to the reorientation of ferroelectric polarization and charge trapping effects, as confirmed by local probe force microscopy. This entropy-driven mechanism enables dynamic functionalities, including secure noise-based authentication, random data encoding/obfuscation, and nociceptor-like responses such as hyperalgesia and allodynia, offering low-energy operation (similar to 2 nJ read) operation. Our findings establish Shannon entropy as a foundational metric, linking physical randomness with ferroelectric properties to create robust, energy-efficient, and adaptable devices for next-generation secure and neuromorphic architectures.

Despite significant advancements in high-speed photodetection, existing ultrafast photodetectors remain constrained by fundamental limitations in responsivity, dynamic range, and signal integrity, particularly for applications requiring secure communication and adaptive processing. An ultrafast photodetector that captures optical transients on nanosecond timescales, far surpassing the approximate to mu s speed limitations of conventional photosensors is presented. Achieving a 61 ns response time (33 ns halfwidth) via a coplanar Schottky-capacitive design, this device leverages instantaneous photo-induced capacitance modulation to generate transient current spikes, effectively bypassing RC time-constant limitations. The resulting transient detection mode offers a large linear dynamic range (>93 dB) and a 6000% enhanced sensitivity compared to conventional steady-state photocurrent operation. This ultrafast speed and sensitivity are harnessed for secure high-speed data transmission and logic processing via an electro-optical modulation scheme that ensures reliable, tamper-resistant information encoding. Furthermore, the photodetector's nonlinear, bias-tunable photoresponse captures distinct material-dependent optical signatures, allowing machine learning classification of metals, insulators, and semiconductors with over 82% accuracy. By integrating ultrafast optical detection with secure communication and logic processing capabilities, this photodetector platform represents a transformative solution for next-generation robotics, automation, intelligent sensing, and high-security materials characterization.

Artificial intelligence (AI) advancements are driving the need for highly parallel and energy-efficient computing analogous to the human brain and visual system. Inspired by the human brain, resistive random-access memories (ReRAMs) have recently emerged as an essential component of the intelligent circuitry architecture for developing high-performance neuromorphic computing systems. This occurs due to their fast switching with ultralow power consumption, high ON/OFF ratio, excellent data retention, good endurance, and even great possibilities for altering resistance analogous to their biological counterparts for neuromorphic computing applications. Additionally, with the advantages of photoelectric dual modulation of resistive switching, ReRAMs allow optically inspired artificial neural networks and reconfigurable logic operations, promoting innovative in-memory computing technology for neuromorphic computing and image recognition tasks. Optoelectronic neuromorphic computing architectured ReRAMs can simulate neural functionalities, such as light-triggered long-term/short-term plasticity. They can be used in intelligent robotics and bionic neurological optoelectronic systems. Metal oxide (MOx)–polymer hybrid nanocomposites can be beneficial as an active layer of the bistable metal–insulator–metal ReRAM devices, which hold promise for developing high-performance memory technology. This review explores the state of the art for developing memory storage, advancement in materials, and switching mechanisms for selecting the appropriate materials as active layers of ReRAMs to boost the ON/OFF ratio, flexibility, and memory density while lowering programming voltage. Furthermore, material design cum-synthesis strategies that greatly influence the overall performance of MOx–polymer hybrid nanocomposite ReRAMs and their performances are highlighted. Additionally, the recent progress of multifunctional optoelectronic MOx–polymer hybrid composites-based ReRAMs are explored as artificial synapses for neural networks to emulate neuromorphic visualization and memorize information. Finally, the challenges, limitations, and future outlooks of the fabrication of MOx–polymer hybrid composite ReRAMs over the conventional von Neumann computing systems are discussed.

In this study, the effect of annealing and substrate conditions on the ferroelectricity of undoped hafnium oxide (HfO2) was analyzed. Hafnium oxide was deposited on various substrates such as platinum, titanium nitride, and silicon (Pt, TiN, Si) through RF magnetron sputtering. Annealing was performed in a nitrogen atmosphere at temperatures ranging from 400 to 600 °C, and the process lasted anywhere from 1 to 30 min. As a result, it was confirmed that the orthorhombic phase, the main cause of ferroelectricity, was dominant after a post-anneal at 600 °C for 30 min. Additionally, it was observed that interface mixing between hafnium oxide and the substrate may degrade ferroelectricity. Accordingly, the highest remanent polarization, measured at 14.24 μC/cm2, was observed with the Pt electrode. This finding was further corroborated by piezo force microscopy and endurance tests, with the results being significant compared to previously reported values. This analysis demonstrates that optimizing substrate and annealing conditions, rather than doping, can enhance the ferroelectricity of hafnium oxide, laying the foundation for the future development of ferroelectric-based transistors.

Neuromorphic computing is a potential approach for imitating massive parallel processing capabilities of a bio-synapse. To date, memristors have emerged as the most appropriate device for designing artificial synapses for this purpose due to their excellent analog switching capacities with high endurance and retention. However, to build an operational neuromorphic platform capable of processing high-density information, memristive synapses with nanoscale footprint are important, albeit with device size scaled down, retaining analog plasticity and low power requirement often become a challenge. This paper demonstrates site-selective self-assembly of Au nanoparticles on a patterned TiOx layer formed as a result of ion-induced self-organization, resulting in site-specific resistive switching and emulation of bio-synaptic behavior (e.g., potentiation, depression, spike rate-dependent and spike timing-dependent plasticity, paired pulse facilitation, and post tetanic potentiation) at nanoscale. The use of local probe-based methods enables nanoscale probing on the anisotropic films. With the help of various microscopic and spectroscopic analytical tools, the observed results are attributed to defect migration and self-assembly of implanted Au atoms on self-organized TiO(x )surfaces. By leveraging the site-selective evolution of gold-nanostructures, the functionalized TiOx surface holds significant potential in a multitude of fields for developing cutting-edge neuromorphic computing platforms and Au-based biosensors with high-density integration.

Nontrivial topological polar textures in ferroelectric materials, including vortices, skyrmions, and others, have the potential to develop ultrafast, high-density, reliable multilevel memory storage and conceptually innovative processing units, even beyond the limit of binary storage of 180° aligned polar materials. However, the realization of switchable polar textures at room temperature in ferroelectric materials integrated directly into silicon using a straightforward large area fabrication technique and effectively utilizing it to design multilevel programable memory and processing units has not yet been demonstrated. Here, utilizing vector piezoresponse force and conductive atomic force microscopy, microscopic evidence of the electric field switchable polar nanotexture is provided at room temperature in HfO2 -ZrO2 nanolaminates grown directly onto silicon using an atomic layer deposition technique. Additionally, a two-terminal Au/nanolaminates/Si ferroelectric tunnel junction is designed, which shows ultrafast (≈83 ns) nonvolatile multilevel current switching with high on/off ratio (>106 ), long-term durability (>4000 s), and giant tunnel electroresistance (108 %). Furthermore, 14 Boolean logic operations are tested utilizing a single device as a proof-of-concept for reconfigurable logic-in-memory processing. The results offer a potential approach to "processing with polar textures" and addressing the challenges of developing high-performance multilevel in-memory processing technology by virtue of its fundamentally distinct mechanism of operation.

Unlike the structure‐specific piezoelectric effect, flexoelectricity is a universal phenomenon that can offer a wide range of energy‐efficient, cost‐effective, mechano‐opto‐electro‐coupled applications. Even though the flexoelectric effect has been extensively studied at nanoscale, a fundamental, yet unresolved, the issue is how it can be exploited at larger scales for potential applications. Herein, the long‐range (>millimeter) stimulated and regulated impact of the localized inhomogeneous strain‐induced flexoelectric potential on centrosymmetric metal/titanium oxide heterojunction with nanoscale precision (≈5.8 nm) is demonstrated. The noticed phenomenon is attributed to the long‐range interaction between flexoelectric and build‐in potentials, which is further utilized to develop mechanically regulated (enhancement > 10 4 %), self‐powered (i.e., 0 V), ultrafast (>10 million bits per second), and broadband (λ = 365–1720 nm) pyro‐photosensors having high responsivity (≈1.18 mA W −1 ). As prospective applications, proof‐of‐concept ultrafast night movement monitors (>720 km h −1 ), high‐performing stationery, and dynamic obstacle sensors with possible impact alerts are developed. These findings lay the groundwork for the micro‐to‐millimeter‐range flexo‐opto‐electrical coupling in centrosymmetric materials, which can have a wide variety of practical applications.