Photonic neural networks have garnered significant attention in recent years due to their ultra-high computational speed, broad bandwidth, and parallel processing capabilities. However, compared to conventional electronic nonlinear activation function (NAF), progress on efficient and easily implementable optical nonlinear activation function (ONAF) was barely reported. To address this issue, we proposed a programmable, low-loss ONAF device based on a silicon micro-ring resonator capped with the Antimony selenide (Sb₂Se₃) thin films, and with indium tin oxide (ITO) used as the microheater. Leveraging our self-developed phase-transformation kinetic and optical models, we successfully simulated the phase-transition behavior of Sb₂Se₃ and three different ONAFs—ELU, ReLU, and radial basis function (RBF) were achieved according to discernible optical responses of proposed devices under different phase-change extents. Classification results from the Fashion MNIST dataset demonstrated that these ONAFs can be considered as appropriate substitutes for traditional NAF. This indicated the bright prospect of the proposed device for nonlinear activation function in future photonic neural networks.

The traditional von Neumann architecture has demonstrated inefficiencies in parallel computing and adaptive learning, rendering it incapable of meeting the growing demand for efficient and high-speed computing. Neuromorphic computing with significant advantages such as high parallelism and ultra-low power consumption is regarded as a promising pathway to overcome the limitations of conventional computers and achieve the next-generation artificial intelligence. Among various neuromorphic devices, the artificial synapses based on electrolyte-gated transistors stand out due to their low energy consumption, multimodal sensing/recording capabilities, and multifunctional integration. Moreover, the emerging optoelectronic neuromorphic devices which combine the strengths of photonics and electronics have demonstrated substantial potential in the neuromorphic computing field. Therefore, this article reviews recent advancements in electrolyte-gated optoelectronic neuromorphic transistors. First, it provides an overview of artificial optoelectronic synapses and neurons, discussing aspects such as device structures, operating mechanisms, and neuromorphic functionalities. Next, the potential applications of optoelectronic synapses in different areas such as artificial visual system, pain system, and tactile perception systems are elaborated. Finally, the current challenges are summarized, and future directions for their developments are proposed.

In this data explosion era, ensuring the secure storage, access, and transmission of information is imperative, encompassing all aspects ranging from safeguarding personal devices to formulating national information security strategies. Leveraging the potential offered by dual-type carriers for transportation and employing optical modulation techniques to develop high reconfigurable ambipolar optoelectronic transistors enables effective implementation of information destruction after reading, thereby guaranteeing data security. In this study, a reconfigurable ambipolar optoelectronic synaptic transistor based on poly (3-hexylthiophene) (P3HT) and poly [[N,N-bis(2-octyldodecyl)-napthalene-1,4,5,8-bis(dicarboximide)-2,6-diyl]-alt-5,5′-(2,2′-bithiophene)] (N2200) blend film was fabricated through solution-processed method. The resulting transistor exhibited a relatively large ON/OFF ratio of 10³ in both n- and p-type regions, and tunable photoconductivity after light illumination, particularly with green light. The photo-generated carriers could be effectively trapped under the gate bias, indicating its potential application in mimicking synaptic behaviors. Furthermore, the synaptic plasticity, including volatile/non−volatile and excitatory/inhibitory characteristics, could be finely modulated by electrical and optical stimuli. These optoelectronic reconfigurable properties enable the realization of information light assisted burn after reading. This study not only offers valuable insights for the advancement of high-performance ambipolar organic optoelectronic synaptic transistors but also presents innovative ideas for the future information security access systems.

Optoelectronic memristor is generating growing research interest for high efficient computing and sensing-memory applications. In this work, an optoelectronic memristor with Au/a-C:Te/Pt structure is developed. Synaptic functions, i.e., excitatory post-synaptic current and pair-pulse facilitation are successfully mimicked with the memristor under electrical and optical stimulations. More importantly, the device exhibited distinguishable response currents by adjusting 4-bit input electrical/optical signals. A multi-mode reservoir computing (RC) system is constructed with the optoelectronic memristors to emulate human tactile-visual fusion recognition and an accuracy of 98.7% is achieved. The optoelectronic memristor provides potential for developing multi-mode RC system.

Recently, for developing neuromorphic visual systems, adaptive optoelectronic devices become one of the main research directions and attract extensive focus to achieve optoelectronic transistors with high performances and flexible functionalities. In this review, based on a description of the biological adaptive functions that are favorable for dynamically perceiving, filtering, and processing information in the varying environment, we summarize the representative strategies for achieving these adaptabilities in optoelectronic transistors, including the adaptation for detecting information, adaptive synaptic weight change, and history-dependent plasticity. Moreover, the key points of the corresponding strategies are comprehensively discussed. And the applications of these adaptive optoelectronic transistors, including the adaptive color detection, signal filtering, extending the response range of light intensity, and improve learning efficiency, are also illustrated separately. Lastly, the challenges faced in developing adaptive optoelectronic transistor for artificial vision system are discussed. The description of biological adaptive functions and the corresponding inspired neuromorphic devices are expected to provide insights for the design and application of next-generation artificial visual systems.

The rapid industrial growth and increasing population have led to significant pollution and deterioration of the natural atmospheric environment. Major atmospheric pollutants include NO₂ and CO₂. Hence, it is imperative to develop NO₂ and CO₂ sensors for ambient conditions, that can be used in indoor air quality monitoring, breath analysis, food spoilage detection, etc. In the present study, two thin film nanocomposite (nickel oxide-graphene and nickel oxide-silver nanowires) gas sensors are fabricated using direct ink writing. The nano-composites are investigated for their structural, optical, and electrical properties. Later the nano-composite is deposited on the interdigitated electrode (IDE) pattern to form NO₂ and CO₂ sensors. The deposited films are then exposed to NO₂ and CO₂ gases separately and their response and recovery times are determined using a custom-built gas sensing setup. Nickel oxide-graphene provides a good response time and recovery time of 10 and 9 s, respectively for NO₂, due to the higher electron affinity of graphene towards NO₂. Nickel oxide-silver nanowire nano-composite is suited for CO₂ gas because silver is an excellent electrocatalyst for CO₂ by giving response and recovery times of 11 s each. This is the first report showcasing NiO nano-composites for NO₂ and CO₂ sensing at room temperature.

Exploring electrode materials with larger capacity, higher power density and longer cycle life was critical for developing advanced flexible lithium-ion batteries (LIBs). Herein, we used a controlled two-step method including electrospraying followed with calcination treatment by CVD furnace to design novel electrodes of Si/Si_x/C and Sn/C microrods array consisting of nanospheres on flexible carbon cloth substrate (denoted as Si/Si_x/C@CC, Sn/C@CC). Microrods composed of cumulated nanospheres (the diameter was approximately 120 nm) had a mean diameter of approximately 1.5 µm and a length of around 4.0 µm, distributing uniformly along the entire woven carbon fibers. Both of Si/Si/Si_x/C@CC and Sn/C@CC products were synthesized as binder-free anodes for Li-ion battery with the features of high reversible capacity and excellent cycling. Especially Si/Si_x/C electrode exhibited high specific capacity of about 1750 mA∙h∙g⁻¹ at 0.5 A∙g⁻¹ and excellent cycling ability even after 1050 cycles with a capacity of 1388 mA∙h∙g⁻¹. Highly flexible Si/Si_x/C@CC//LiCoO₂ batteries based on liquid and solid electrolytes were also fabricated, exhibiting high flexibility, excellent electrical stability and potential applications in flexible wearable electronics.

With the advancement of artificial intelligence, optic in-sensing reservoir computing based on emerging semiconductor devices is high desirable for real-time analog signal processing. Here, we disclose a flexible optomemristor based on C₂₇H₃₀O₁₅/FeO_x heterostructure that presents a highly sensitive to the light stimuli and artificial optic synaptic features such as short- and long-term plasticity (STP and LTP), enabling the developed optomemristor to implement complex analogy signal processing through building a real-physical dynamic-based in-sensing reservoir computing algorithm and yielding an accuracy of 94.88% for speech recognition. The charge trapping and detrapping mediated by the optic active layer of C₂₇H₃₀O₁₅ that is extracted from the lotus flower is response for the positive photoconductance memory in the prepared optomemristor. This work provides a feasible organic−inorganic heterostructure as well as an optic in-sensing vision computing for an advanced optic computing system in future complex signal processing.

We demonstrate a bipolar graphene/F₁₆CuPc synaptic transistor (GFST) with matched p-type and n-type bipolar properties, which emulates multiplexed neurotransmission of the release of two excitatory neurotransmitters in graphene and F₁₆CuPc channels, separately. This process facilitates fast-switching plasticity by altering charge carriers in the separated channels. The complementary neural network for image recognition of Fashion-MNIST dataset was constructed using the matched relative amplitude and plasticity properties of the GFST dominated by holes or electrons to improve the weight regulation and recognition accuracy, achieving a pattern recognition accuracy of 83.23%. These results provide new insights to the construction of future neuromorphic systems.

Artificial skin should embody a softly functional film that is capable of self-powering, healing and sensing with neuromorphic processing. However, the pursuit of a bionic skin that combines high flexibility, self-healability, and zero-powered photosynaptic functionality remains elusive. In this study, we report a self-powered and self-healable neuromorphic vision skin, featuring silver nanoparticle-doped ionogel heterostructure as photoacceptor. The localized surface plasmon resonance induced by light in the nanoparticles triggers temperature fluctuations within the heterojunction, facilitating ion migration for visual sensing with synaptic behaviors. The abundant reversible hydrogen bonds in the ionogel endow the skin with remarkable mechanical flexibility and self-healing properties. We assembled a neuromorphic visual skin equipped with a 5 × 5 photosynapse array, capable of sensing and memorizing diverse light patterns.

In the era of Metaverse and virtual reality (VR)/augmented reality (AR), capturing finger motion and force interactions is crucial for immersive human-machine interfaces. This study introduces a flexible electronic skin for the index finger, addressing coupled perception of both state and process in dynamic tactile sensing. The device integrates resistive and giant magnetoelastic sensors, enabling detection of surface pressure and finger joint bending. This e-skin identifies three phases of finger action: bending state, dynamic normal force and tangential force (sweeping). The system comprises resistive carbon nanotubes (CNT)/polydimethylsiloxane (PDMS) films for bending sensing and magnetoelastic sensors (NdFeB particles, EcoFlex, and flexible coils) for pressure detection. The inward bending resistive sensor, based on self-assembled microstructures, exhibits directional specificity with a response time under 120 ms and bending sensitivity from 0° to 120°. The magnetoelastic sensors demonstrate specific responses to frequency and deformation magnitude, as well as sensitivity to surface roughness during sliding and material hardness. The system’s capability is demonstrated through tactile-based bread type and condition recognition, achieving 92% accuracy. This intelligent patch shows broad potential in enhancing interactions across various fields, from VR/AR interfaces and medical diagnostics to smart manufacturing and industrial automation.

Multimodal sensor fusion can make full use of the advantages of various sensors, make up for the shortcomings of a single sensor, achieve information verification or information security through information redundancy, and improve the reliability and safety of the system. Artificial intelligence (AI), referring to the simulation of human intelligence in machines that are programmed to think and learn like humans, represents a pivotal frontier in modern scientific research. With the continuous development and promotion of AI technology in Sensor 4.0 age, multimodal sensor fusion is becoming more and more intelligent and automated, and is expected to go further in the future. With this context, this review article takes a comprehensive look at the recent progress on AI-enhanced multimodal sensors and their integrated devices and systems. Based on the concept and principle of sensor technologies and AI algorithms, the theoretical underpinnings, technological breakthroughs, and pragmatic applications of AI-enhanced multimodal sensors in various fields such as robotics, healthcare, and environmental monitoring are highlighted. Through a comparative study of the dual/tri-modal sensors with and without using AI technologies (especially machine learning and deep learning), AI-enhanced multimodal sensors highlight the potential of AI to improve sensor performance, data processing, and decision-making capabilities. Furthermore, the review analyzes the challenges and opportunities afforded by AI-enhanced multimodal sensors, and offers a prospective outlook on the forthcoming advancements.

Implantable temperature sensors are revolutionizing physiological monitoring and playing a crucial role in diagnostics, therapeutics, and life sciences research. This review classifies the materials used in these sensors into three categories: metal-based, inorganic semiconductor, and organic semiconductor materials. Metal-based materials are widely used in medical and industrial applications due to their linearity, stability, and reliability. Inorganic semiconductors provide rapid response times and high miniaturization potential, making them promising for biomedical and environmental monitoring. Organic semiconductors offer high sensitivity and ease of processing, enabling the development of flexible and stretchable sensors. This review analyzes recent studies for each material type, covering design principles, performance characteristics, and applications, highlighting key advantages and challenges regarding miniaturization, sensitivity, response time, and biocompatibility. Furthermore, critical performance parameters of implantable temperature sensors based on different material types are summarized, providing valuable references for future sensor design and optimization. The future development of implantable temperature sensors is discussed, focusing on improving biocompatibility, long-term stability, and multifunctional integration. These advancements are expected to expand the application potential of implantable sensors in telemedicine and dynamic physiological monitoring.

Photodetectors with weak-light detection capabilities play an indispensable role in various crucial fields such as health monitors, imaging, optical communication, and etc. Nevertheless, the detection of weak light signals is often severely interfered by multiple factors such as background light, dark noise and circuit noise, making it difficult to accurately capture signals. While traditional technologies like silicon photomultiplier tubes excel in sensitivity, their high cost and inherent fragility restrict their widespread application. Against this background, perovskite materials have rapidly emerged as a research focus in the field of photodetection due to their simple preparation processes and exceptional optoelectronic properties. Not only are the preparation processes of perovskite materials straightforward and cost-effective, but more importantly, they can be flexibly integrated into flexible and stretchable substrates. This characteristic significantly compensates for the shortcomings of traditional rigid electronic devices in specific application scenarios, opening up entirely new possibilities for photodetection technology. Herein, recent advances in perovskite light detection technology are reviewed. Firstly, the chemical and physical properties of perovskite materials are discussed, highlighting their remarkable advantages in weak-light detection. Subsequently, the review systematically organizes various preparation techniques of perovskite materials and analyses their advantages in different application scenarios. Meanwhile, from the two core dimensions of performance improvement and light absorption enhancement, the key strategies of improving the performance of perovskite weak-light photodetectors are explored. Finally, the review concludes with a brief summary and a discussion on the potential challenges that may arise in the further development of perovskite devices.

With the rapid development of the internet of things (IoT) and wearable electronics, the role of flexible sensors is becoming increasingly irreplaceable, due to their ability to process and convert information acquisition. Two-dimensional (2D) materials have been widely welcomed by researchers as sensitive layers, which broadens the range and application of flexible sensors due to the advantages of their large specific surface area, tunable energy bands, controllable thickness at the atomic level, stable mechanical properties, and excellent optoelectronic properties. This review focuses on five different types of 2D materials for monitoring pressure, humidity, sound, gas, and so on, to realize the recognition and conversion of human body and environmental signals. Meanwhile, the main problems and possible solutions of flexible sensors based on 2D materials as sensitive layers are summarized.

With the rapid development of artificial intelligence (AI) technology, the demand for high-performance and energy-efficient computing is increasingly growing. The limitations of the traditional von Neumann computing architecture have prompted researchers to explore neuromorphic computing as a solution. Neuromorphic computing mimics the working principles of the human brain, characterized by high efficiency, low energy consumption, and strong fault tolerance, providing a hardware foundation for the development of new generation AI technology. Artificial neurons and synapses are the two core components of neuromorphic computing systems. Artificial perception is a crucial aspect of neuromorphic computing, where artificial sensory neurons play an irreplaceable role thus becoming a frontier and hot topic of research. This work reviews recent advances in artificial sensory neurons and their applications. First, biological sensory neurons are briefly described. Then, different types of artificial neurons, such as transistor neurons and memristive neurons, are discussed in detail, focusing on their device structures and working mechanisms. Next, the research progress of artificial sensory neurons and their applications in artificial perception systems is systematically elaborated, covering various sensory types, including vision, touch, hearing, taste, and smell. Finally, challenges faced by artificial sensory neurons at both device and system levels are summarized.

The rapid advancement of information technology has heightened interest in complementary devices and circuits. Conventional p-type semiconductors often lack sufficient electrical performance, thus prompting the search for new materials with high hole mobility and long-term stability. Elemental tellurium (Te), featuring a one-dimensional chiral atomic structure, has emerged as a promising candidate due to its narrow bandgap, high hole mobility, and versatility in industrial applications, particularly in electronics and renewable energy. This review highlights recent progress in Te nanostructures and related devices, focusing on synthesis methods, including vapor deposition and hydrothermal synthesis, which produce Te nanowires, nanorods, and other nanostructures. Critical applications in photodetectors, gas sensors, and energy harvesting devices are discussed, with a special emphasis on their role within the internet of things (IoT) framework, a rapidly growing field that is reshaping our technological landscape. The prospects and potential applications of Te-based technologies are also highlighted.

Organic electrochemical transistors have emerged as a solution for artificial synapses that mimic the neural functions of the brain structure, holding great potentials to break the bottleneck of von Neumann architectures. However, current artificial synapses rely primarily on electrical signals, and little attention has been paid to the vital role of neurotransmitter-mediated artificial synapses. Dopamine is a key neurotransmitter associated with emotion regulation and cognitive processes that needs to be monitored in real time to advance the development of disease diagnostics and neuroscience. To provide insights into the development of artificial synapses with neurotransmitter involvement, this review proposes three steps towards future biomimic and bioinspired neuromorphic systems. We first summarize OECT-based dopamine detection devices, and then review advances in neurotransmitter-mediated artificial synapses and resultant advanced neuromorphic systems. Finally, by exploring the challenges and opportunities related to such neuromorphic systems, we provide a perspective on the future development of biomimetic and bioinspired neuromorphic systems.

Heart rate variability (HRV) that can reflect the dynamic balance between the sympathetic nervous and parasympathetic nervous of human autonomic nervous system (ANS) has attracted considerable attention. However, traditional electrocardiogram (ECG) devices for HRV analysis are bulky, and hard wires are needed to attach measuring electrodes to the chest, resulting in the poor wearable experience during the long-term measurement. Compared with that, wearable electronics enabling continuously cardiac signals monitoring and HRV assessment provide a desirable and promising approach for helping subjects determine sleeping issues, cardiovascular diseases, or other threats to physical and mental well-being. Until now, significant progress and advances have been achieved in wearable electronics for HRV monitoring and applications for predicting human physical and mental well-being. In this review, the latest progress in the integration of wearable electronics and HRV analysis as well as practical applications in assessment of human physical and mental health are included. The commonly used methods and physiological signals for HRV analysis are briefly summarized. Furthermore, we highlighted the research on wearable electronics concerning HRV assessment and diverse applications such as stress estimation, drowsiness detection, etc. Lastly, the current limitations of the integrated wearable HRV system are concluded, and possible solutions in such a research direction are outlined.

Infrared optoelectronic sensing is the core of many critical applications such as night vision, health and medication, military, space exploration, etc. Further including mechanical flexibility as a new dimension enables novel features of adaptability and conformability, promising for developing next-generation optoelectronic sensory applications toward reduced size, weight, price, power consumption, and enhanced performance (SWaP³). However, in this emerging research frontier, challenges persist in simultaneously achieving high infrared response and good mechanical deformability in devices and integrated systems. Therefore, we perform a comprehensive review of the design strategies and insights of flexible infrared optoelectronic sensors, including the fundamentals of infrared photodetectors, selection of materials and device architectures, fabrication techniques and design strategies, and the discussion of architectural and functional integration towards applications in wearable optoelectronics and advanced image sensing. Finally, this article offers insights into future directions to practically realize the ultra-high performance and smart sensors enabled by infrared-sensitive materials, covering challenges in materials development and device micro-/nanofabrication. Benchmarks for scaling these techniques across fabrication, performance, and integration are presented, alongside perspectives on potential applications in medication and health, biomimetic vision, and neuromorphic sensory systems, etc.

Flexible photodetectors have garnered significant attention by virtue of their potential applications in environmental monitoring, wearable healthcare, imaging sensing, and portable optical communications. Perovskites stand out as particularly promising materials for photodetectors, offering exceptional optoelectronic properties, tunable band gaps, low-temperature solution processing, and notable mechanical flexibility. In this review, we explore the latest progress in flexible perovskite photodetectors, emphasizing the strategies developed for photoactive materials and device structures to enhance optoelectronic performance and stability. Additionally, we discuss typical applications of these devices and offer insights into future directions and potential applications.

In recent years, research focusing on synaptic device based on phototransistors has provided a new method for associative learning and neuromorphic computing. A TiO₂/AlGaN/GaN heterostructure-based synaptic phototransistor is fabricated and measured, integrating a TiO₂ nanolayer gate and a two-dimensional electron gas (2DEG) channel to mimic the synaptic weight and the synaptic cleft, respectively. The maximum drain to source current is 10 nA, while the device is driven at a reverse bias not exceeding −2.5 V. A excitatory postsynaptic current (EPSC) of 200 nA can be triggered by a 365 nm UVA light spike with the duration of 1 s at light intensity of 1.35 μW∙cm⁻². Multiple synaptic neuromorphic functions, including EPSC, short-term/long-term plasticity (STP/LTP) and paried-pulse facilitation (PPF), are effectively mimicked by our GaN-based heterostructure synaptic device. In the typical Pavlov’s dog experiment, we demonstrate that the device can achieve "retraining" process to extend memory time through enhancing the intensity of synaptic weight, which is similar to the working mechanism of human brain.

The hierarchical and coordinated processing of visual information by the brain demonstrates its superior ability to minimize energy consumption and maximize signal transmission efficiency. Therefore, it is crucial to develop artificial visual synapses that integrate optical sensing and synaptic functions. This study fully leverages the excellent photoresponsivity properties of the PM6 : Y6 system to construct a vertical photo-tunable organic memristor and conducts in-depth research on its resistive switching performance, photodetection capability, and simulation of photo-synaptic behavior, showcasing its excellent performance in processing visual information and simulating neuromorphic behaviors. The device achieves stable and gradual resistance change, successfully simulating voltage-controlled long-term potentiation/depression (LTP/LTD), and exhibits various photo-electric synergistic regulation of synaptic plasticity. Moreover, the device has successfully simulated the image perception and recognition functions of the human visual nervous system. The non-volatile Au/PM6 : Y6/ITO memristor is used as an artificial synapse and neuron modeling, building a hierarchical coordinated processing SLP-CNN cascade neural network for visual image recognition training, its linear tunable photoconductivity characteristic serves as the weight update of the network, achieving a recognition accuracy of up to 93.4%. Compared with the single-layer visual target recognition model, this scheme has improved the recognition accuracy by 19.2%.

Synaptic nano-devices have powerful capabilities in logic, memory and learning, making them essential components for constructing brain-like neuromorphic computing systems. Here, we have successfully developed and demonstrated a synaptic nano-device based on Ga₂O₃ nanowires with low energy consumption. Under 255 nm light stimulation, the biomimetic synaptic nano-device can stimulate various functionalities of biological synapses, including pulse facilitation, peak time-dependent plasticity and memory learning ability. It is found that the artificial synaptic device based on Ga₂O₃ nanowires can achieve an excellent "learning−forgetting−relearning" functionality. The transition from short-term memory to long-term memory and retention of the memory level after the stepwise learning can attribute to the great relearning functionality of Ga₂O₃ nanowires. Furthermore, the energy consumption of the synaptic nano-device can be lower than 2.39 × 10^‒11 J for a synaptic event. Moreover, our device demonstrates exceptional stability in long-term stimulation and storage. In the application of neural morphological computation, the accuracy of digit recognition exceeds 90% after 12 training sessions, indicating the strong learning capability of the cognitive system composed of this synaptic nano-device. Therefore, our work paves an effective way for advancing hardware-based neural morphological computation and artificial intelligence systems requiring low power consumption.

Memristors have a synapse-like two-terminal structure and electrical properties, which are widely used in the construction of artificial synapses. However, compared to inorganic materials, organic materials are rarely used for artificial spiking synapses due to their relatively poor memrisitve performance. Here, for the first time, we present an organic memristor based on an electropolymerized dopamine-based memristive layer. This polydopamine-based memristor demonstrates the improvements in key performance, including a low threshold voltage of 0.3 V, a thin thickness of 16 nm, and a high parasitic capacitance of about 1 μF∙mm⁻². By leveraging these properties in combination with its stable threshold switching behavior, we construct a capacitor-free and low-power artificial spiking neuron capable of outputting the oscillation voltage, whose spiking frequency increases with the increase of current stimulation analogous to a biological neuron. The experimental results indicate that our artificial spiking neuron holds potential for applications in neuromorphic computing and systems.

Lead chalcohalides (PbYX, X = Cl, Br, I; Y = S, Se) is an extension of the classic Pb chalcogenides (PbY). Constructing the heterogeneous integration with PbYX and PbY material systems makes it possible to achieve significantly improved optoelectronic performance. In this work, we studied the effect of introducing halogen precursors on the structure of classical PbS nanocrystals (NCs) during the synthesis process and realized the preparation of PbS/Pb₃S₂X₂ core/shell structure for the first time. The core/shell structure can effectively improve their optical properties. Furthermore, our approach enables the synthesis of Pb₃S₂Br₂ that had not yet been reported. Our results not only provide valuable insights into the heterogeneous integration of PbYX and PbY materials to elevate material properties but also provide an effective method for further expanding the preparation of PbYX material systems.