Search Results (3,469)

Quantum dots (QDs) are semiconductor nanocrystals with unique photophysical properties, rendering them promising for applications in biomedical imaging, neuroscience, and various industrial sectors. However, the rapid expansion of their production and application inevitably leads to the release of QDs into the environment throughout their life cycle, classifying them as an emerging class of contaminants of concern. Their potential neurotoxicity not only represents a major bottleneck obstructing their clinical translation but also poses environmental and health risks that warrant serious attention. This review summarizes recent advances in the neurotoxicity of QDs, with a focus on their adverse effects on the central and peripheral nervous systems. It indicates that the mechanisms of QD neurotoxicity involve a complex network comprising oxidative stress, metabolic reprogramming, neuroinflammation, and multiple cell death pathways. Notably, the peripheral nervous system is highlighted as an early-warning target, and the significant risks associated with long-term, low-dose environmental exposure are emphasized. Full article

Rapid detection of heavy metals is vital for monitoring surface water contamination and preventing environmental and health risks. Traditional detection methods for metals such as lead and copper often require sophisticated, costly instrumentation, limiting their use in routine analyses. To address this challenge, we developed a cost-effective fluorescence-based approach using semiconductor quantum dots (QDs) as nanosensors for metal ion detection. The QDs were synthesized directly in aqueous medium through a reflux-assisted process employing cadmium precursors, selenium, thioglycolic acid (TGA), and branched polyethyleneimine (PEI, Mw ~25,000) as stabilizing agents. Structural analysis revealed nanoparticles with diameters below 5 nm, spherical morphology, and a zinc blende (face-centered cubic) crystalline structure. Optical characterization by UV–Vis, photoluminescence (PL), and FTIR spectroscopy confirmed effective surface functionalization and strong quantum confinement. PEI-capped QDs exhibited enhanced colloidal stability and showed pronounced fluorescence quenching in the presence of Pb²⁺ ions, indicating high sensitivity and selectivity toward lead. Both TGA- and PEI-capped QDs also demonstrated moderate responses to Co²⁺ but negligible interaction with Sn²⁺, confirming ion-specific detection. Overall, this study demonstrates that surface-engineered QDs constitute a simple, accessible platform for selective detection of toxic metals, with promising applications in environmental monitoring and water quality assessment. Full article

Two-dimensional black phosphorus (2D BP) has emerged as one of the most promising two-dimensional semiconductors for next-generation micro and nanoelectronics beyond Moore’s Law. It is distinguished by its unique combination of a layer dependent direct bandgap, broadband photoresponse, and pronounced in-plane anisotropy, addressing key intrinsic limitations that have hindered the widespread application of graphene and conventional transition metal dichalcogenides (TMDCs). This review provides a systematic and comprehensive overview of recent advances in the controllable fabrication of 2D BP and its applications in transistors and photodetectors. We first elucidate its crystal lattice structure and fundamental physical properties, then categorize and summarize synthesis strategies based on production scale ranging from small scale methods (e.g., mechanical exfoliation and solution based exfoliation) to large scale methods (e.g., Chemical Vapor Deposition (CVD) and Pulsed Laser Deposition (PLD)), with a particular focus on recent advances in high-speed field-effect transistors and broadband photodetectors. In summary, the key to achieving large-scale controllable synthesis lies in addressing the challenges of high-temperature oxidation of black phosphorus and the uncontrollable diffusion of phosphorus sources. In the future, industrial applications are expected to be realized through CVD based regulation of phosphorus sources, low-temperature growth by PLD, and deep integration with silicon-based processes. Full article

High-sensitivity detection of formaldehyde is critically important for environmental protection and public health. Zinc oxide (ZnO) is a widely used core material for chemiresistive gas sensors; however, its conventional low-index facets suffer from a limited number of active sites, creating a bottleneck for further sensitivity enhancement. To overcome this limitation, this study pioneers the application of the highly reactive ZnO $\left[20\overline{2}1\right]$ high-index polar surface for formaldehyde detection. By leveraging its unique stepped atomic configuration and unprecedented density of coordination-unsaturated active sites, we systematically investigate the formaldehyde adsorption behavior and the underlying sensing mechanism using first-principles calculations based on density functional theory (DFT). The pristine ZnO $\left[20\overline{2}1\right]$ surface exhibits intrinsic metallic character. At a coverage of 1 monolayer (ML), the most stable G1 configuration achieves an adsorption energy of −1.54 eV per CH₂O molecule. Within a 2 × 1 supercell, formaldehyde adopts both associative and dissociative adsorption modes. At a lower coverage, formaldehyde forms a stable bidentate structure through dual C–O and Zn–O bonding interactions. Electronic structure analysis reveals significant orbital hybridization and interfacial charge redistribution upon adsorption. Notably, associative adsorption opens a bandgap of 0.04 eV at the Fermi level, inducing a metal-to-semiconductor transition. In contrast, dissociative adsorption results in pronounced n-type doping, thereby elucidating the microscopic origin of the resistivity decrease observed in ZnO-based sensors. Overall, this work highlights the structural advantages of high-index facets and demonstrates for the first time the superior formaldehyde adsorption capability of the ZnO $\left[20\overline{2}1\right]$ facet, providing robust theoretical guidance for the rational design of next-generation, high-performance gas-sensing materials. Full article

In this study, charge-trapping memory (CTM) transistors were developed using indium gallium zinc oxide (IGZO) as the oxide semiconductor channel and high-k HfO₂ as the charge-trapping layer, aiming for next-generation nonvolatile memory applications. To evaluate the impact of plasma exposure on film quality and device performance, HfO₂ thin films were deposited via atomic layer deposition (ALD) using both direct plasma (DP) and remote plasma (RP) modes. Post-deposition annealing (PDA) was applied to the IGZO and HfO₂ layers, with experiments conducted at various annealing temperatures to enhance the interfacial stability between the HfO₂ layer and the IGZO channel. Electrical characterization results demonstrated that the RP-processed devices exhibited a wider memory window, reduced gate leakage current, and improved threshold voltage stability compared with the DP-processed devices. Thermal treatment effectively reduced the interfacial defect density and enhanced the crystallinity at the dielectric–channel interface. These findings underscore that the selection of the plasma process and annealing conditions is critical in determining the electrical characteristics and reliability of oxide semiconductor-based CTM devices. Full article

As the requirements in fields such as automobile, high-frequency signal generation, and medical applications, the resolution of time-to-digital converters (TDCs) has been pushed to the picosecond and sub-picosecond levels. In this study, a Vernier TDC was investigated with a time resolution less than the signal transition in the circuit. As generally happens in TDCs or Analog-to-Digital Converters (ADCs), bubble errors are found to degrade the resolution of their output codes. The bubble errors are usually attributed to non-idealities and mismatches in the circuits. Since the input time difference in high-resolution TDCs is much smaller than the signal transition time with the existence of bubble errors, it is an issue to determine the corresponding thermometer code from the output bit string of interleaved 0 s and 1 s. In our exploration, a Xilinx FPGA was employed to implement a Vernier Delay Line (VDL) for the TDC. In this timing-sensitive design, the timing difference between the two paths mainly comes from the interconnects rather than the Look-Up Table (LUT) devices. Timing constraints and regular placement were also imposed in addition to the simple Register Transfer Level (RTL) codes. Since the nature of uncertainty, a statistical model was proposed to analyze the output bit patterns. Three methods were employed to determine the output thermometer code. The first would count the total number of 1 s in the output. The second is to detect the position of the last 1. And the third is to detect the first 0 in the output bit string. The obtained results showed that these three methods were almost equivalent in the statistical outputs. The time resolution of our FPGA-based VDL can be around 5 ps in our measurement. According to our model, the transition time in the FPGA circuit was estimated as 100 ps. This result is reasonable for a chip made of 28 nm Complementary Metal-Oxide-Semiconductor (CMOS) technology. For the study of the linearity of our VDL, its differential nonlinearity (DNL) was less than ±2 LSB. The code-density-like analysis also shows the nonlinearity of this VDL. It was also found that the methods detecting the last 1 and the first 0 were sensitive to bit failures. In summary, for this study, it is confirmed that the three conversion methods are equivalent, and we found that detecting the last 1 or the first 0 was sensitive to bit defects or mismatches. Full article

The present study investigates the application of electric resistive heating to photovoltaic (PV) panels, aimed at enabling their subsequent thermo-mechanical delamination. The key process parameters—namely current magnitude and applied voltage—required for direct electro-resistive heating are identified, and the process is experimentally demonstrated under laboratory conditions. The electric resistive heating of a composite photovoltaic panel, consisting of a solar cell layer (crystalline silicon, c-Si, with a metallic grid), a backsheet, and a glass layer, is analyzed in detail using a virtual model of a single-crystal silicon solar cell implemented as coupled electric-thermal analysis. The temperature dependence of the electrical resistance of the solar cell layer is experimentally measured, and exponential relationships are derived and subsequently incorporated into the numerical model. The virtual model results are validated, demonstrating that, for a given geometry and configuration of the conductive metallic grid (busbars and fingers), the electrical resistance of the semiconductor layer containing the p–n junction governs the temperature achieved during electro-resistive heating as a function of the applied current. Furthermore, results for the terminal current and voltage, current density in the busbars and fingers, electric field intensity, and the resulting temperature within the semiconductor layer of the single-crystal silicon solar cell are presented and analyzed. Full article

This article introduces the current status of the 0.18-micron CMOS MEMS foundry service platform provided by the Taiwan Semiconductor Research Institute (TSRI), extensively covering the CMOS MEMS components that it has supported in development and fabrication. It also attempts to expand the foundry service scope to the broader categories of capacitive sensors and electrostatic actuators. On the one hand, for fabless MEMS component designers, TSRI currently directly allows the design of two types of components: flow sensors with uniformly perforated membranes and actuators with comb-shaped interdigital electrodes. This service also includes tape-out and wire bonding packaging procedures, following procedures similar to those used by general IC designers. On the other hand, this article specifically presents a clear and feasible approach for MEMS designers equipped with simple wet-etching facilities and a clear and feasible approach to develop further CMOS MEMS components such as capacitive pressure sensors, accelerometers, micro mirrors, and scratch drive actuators with minimal post-processing and chip packaging steps. This work provides a practical CMOS-MEMS design and post-processing guideline for extending the current TSRI foundry platform toward capacitive sensing and electrostatic actuation applications with minimal additional fabrication complexity. Full article

The linear and third-order nonlinear optical (NLO) properties of a water-soluble perylenediimide derivative, N,N′-di(2-(trimethylammonium iodide) ethylene) perylenediimide (TAIPDI), doped with semiconductor nanoparticles (NPs), were systematically investigated under femtosecond laser excitation. ZnO and TiO₂ NPs were synthesized using a pulsed laser ablation technique. Nanocomposite systems were prepared by incorporating different concentrations of ZnO and TiO₂ NPs into the TAIPDI dye solution. The optical properties were characterized using UV–visible absorption spectroscopy together with open- and closed-aperture Z-scan measurements at 800 nm. Linear absorption measurements revealed concentration-dependent modifications in the optical band gap, indicating electronic interaction between the dye molecules and the semiconductor NPs. Open-aperture Z-scan results demonstrated strong nonlinear absorption (NLA) behavior dominated by two-photon absorption and excited-state absorption processes. Closed-aperture measurements showed a negative nonlinear refractive (NLR) index, corresponding to self-defocusing behavior. Both the NLA coefficient and the NLR index increased with increasing NP concentration, resulting in a significant enhancement of the third-order nonlinear susceptibility of the nanocomposite systems. In addition, optical limiting measurements revealed a pronounced reduction in the limiting threshold with increasing nanoparticle concentration, demonstrating improved laser attenuation capability. These findings indicate that ZnO@TAIPDI and TiO₂@TAIPDI nanocomposites are promising candidates for applications in optical limiting, all-optical switching, and advanced photonic devices. Full article

The rapid growth of artificial intelligence (AI) workloads is reshaping semiconductor design across architecture, interconnect, memory hierarchy, packaging, timing, and design automation. Rather than converging on a single hardware solution, the field is expanding into a heterogeneous ecosystem that includes data-center graphics processing units (GPUs), edge neural processing units (NPUs), and application-specific integrated circuits (ASICs), field-programmable gate array (FPGA)-based and hybrid AI system-on-chip (SoC) platforms, chiplet-enabled systems, and emerging beyond-conventional-silicon approaches such as photonic, neuromorphic, and analog in-memory processors. This paper presents a comprehensive review of AI-on-chip systems from a cross-layer perspective. It examines AI chip architectures and hardware platforms, network-on-chip (NoC) designs for AI communication patterns, and algorithm–hardware co-design methods for model acceleration, including compression, quantization, and sparsity-aware optimization. It also reviews clocking, synchronization, and clock-domain-crossing (CDC) challenges in large heterogeneous systems and chiplets, as well as manufacturing, advanced packaging, and reliability issues, including two-and-a-half-dimensional (2.5D) and three-dimensional (3D) integration, thermal and mechanical constraints, assembly quality, and long-term yield considerations. In parallel, the paper surveys the growing role of AI in chip design itself, covering machine-learning-assisted analysis, Bayesian and reinforcement-learning-based optimization, and the emerging use of large language models (LLMs) and AI agents for register-transfer level (RTL) generation, design-space exploration, and autonomous electronic design automation (EDA) workflows. Finally, it discusses beyond-silicon AI chip directions and the broader economic and industry context shaping cloud, on-premises, and edge deployment. By integrating these topics into a unified framework, this review highlights the key technological drivers, system-level tradeoffs, and future research directions that will define next-generation scalable, reliable, and energy-efficient AI-on-chip systems. Full article

Bismuth tungstate (Bi₂WO₆) is a typical bismuth-based visible-light-responsive semiconductor photocatalyst that has attracted significant attention in the fields of environment remediation and energy conversion. In this paper, to address the issues of high photogenerated carrier recombination rate and limited visible-light-response range of Bi₂WO₆, various modification strategies are highlighted, including morphology control, element doping, heterojunction construction, carbon material compositing, and coupling with functional materials such as metal–organic frameworks (MOFs), covalent organic frameworks (COFs), or conductive polymers. Furthermore, the structure–activity relationships are discussed. On this basis, the latest application progress of Bi₂WO₆-based photocatalysts in fields such as pollutant degradation, antibacterial activity, and energy conversion and storage is summarized. Finally, prospects are put forward regarding the existing shortcomings and future development directions in the application of Bi₂WO₆-based photocatalysts, aiming to provide a systematic theoretical reference for the design and application of high-performance Bi₂WO₆-based photocatalysts. Full article

Passively mode-locked lasers, as essential tools for generating ultrashort pulses, have found widespread applications in industrial manufacturing, optical communications, biomedical imaging, and fundamental scientific research. Saturable absorbers serve as the key components governing the performance of such laser systems. Conventional saturable absorber materials, including semiconductor saturable absorber mirrors, carbon nanotubes, and graphene, however, suffer from inherent limitations in operational wavelength range, damage threshold, and environmental stability. In recent years, two-dimensional transition metal carbides and nitrides, known as MXenes, have emerged as a promising class of materials to address these challenges. Their unique metallic conductivity, broadband saturable absorption, ultrafast carrier dynamics, excellent thermal management capability, and versatile chemical tunability offer unprecedented opportunities for advanced saturable absorber applications. This review systematically summarizes the recent progress of MXene-based saturable absorbers, with an emphasis on their distinctive advantages in extending the mode-locked wavelength range, enhancing output pulse stability, and increasing the optical damage threshold. Furthermore, strategies for performance optimization through surface terminal group engineering, defect modulation, and heterostructure design are discussed in depth. Finally, the future prospects and key challenges toward industrial implementation of MXenes in ultrafast photonics are outlined, aiming to stimulate further advancements in high-performance ultrafast laser technology. Full article

Tellurium (Te), an attractive p-type van der Waals semiconductor, has been considered a promising candidate in electrical applications due to its unique one-dimensional chiral atomic-helical-chain structure, tunable bandgap, and ultrahigh hole mobility. This review summarizes recent advances in the controlled synthesis of Te semiconductor nanostructures, including one-dimensional tellurium nanowires and two-dimensional tellurene in the form of nanosheets and thin films. We further highlight emerging electrical applications of Te in field-effect transistors, logic circuits, photodetectors, memristors, and artificial synapse devices. Finally, current challenges and future opportunities for the commercialization of Te-based electronic and optoelectronic devices, particularly for neuromorphic and in-sensor computing systems, are discussed. Full article

The emergence and the growing influence of contaminants in wastewater has driven the development of advanced and efficient treatment technologies. Catalysts based on biochar have become a promising material because of their cheapness, adjustable physicochemical characteristics, and environmental compatibility. This study comprehensively reviews recent developments in biochar-based catalytic processes to treat wastewater with an emphasis on AOPs and photocatalysis. The main categories of catalysts including metal-loaded biochar, heteroatom-doped biochar, biochar-supported semiconductor composites, and magnetic biochar are extensively discussed with regard to their synthesis, structure, and performance in the elimination of organic, emerging, and heavy metal contaminants. Emphasis is placed on catalytic reactions, radical (•OH, SO₄•⁻) and non-radical (singlet oxygen and electron transfer) reactions, as well as the effect of functional groups on the surface, defects, and electronic features in the control of activity. Engineered biochar has a better performance in charge separation, reactive species generation, and synergistic interactions between adsorption and degradation. Nevertheless, there are issues such as heterogeneity in biochar properties, insufficient understanding of structure–activity interactions, catalyst stability, and the absence of studies of biochar under real wastewater conditions. The future perspectives focus on rational catalyst design, integration of processes, and scaling up to practical applications. Overall, biochar-based catalysts have emerged as a sustainable platform for advanced wastewater treatment, but additional studies are needed to enable their large-scale use. Full article

Laser powder bed fusion (L-PBF) has emerged as a core metal additive manufacturing technology for high-end sectors, including aerospace and medical device manufacturing. However, melting anomalies that occur during fabrication accumulate layer by layer, leading to degraded surface quality and impaired mechanical performance of as-built components—a critical bottleneck limiting their large-scale industrial adoption. Accurate and robust layer-wise melting quality recognition remains a challenge due to the complex surface morphologies induced by such melting anomalies. This study presents a machine learning-enabled in situ monitoring approach for layer-wise melting quality identification in L-PBF. By systematically varying laser power and scanning speed, 24 parameter combinations were designed to fabricate specimens with three distinct melting states: over-melting (OM), lack of fusion (LOF), and normal melting. A high-resolution complementary meta–oxide–semiconductor (CMOS) camera was used to capture layer-wise surface images of the specimens, and following abnormal layer filtering and manual validation, a high-quality dataset comprising 5110 layer-wise images was constructed. Two mainstream machine learning approaches were systematically evaluated and optimized for melting quality classification: a support vector machine (SVM) model leveraging handcrafted gray-level co-occurrence matrix (GLCM) texture features achieved a classification accuracy of 96.77%, while a convolutional neural network (CNN) model with end-to-end feature learning directly from raw images attained a superior accuracy of 98.14%. In terms of computational efficiency, the CNN model exhibited a faster inference speed with a per-layer inference time of just 0.036 s, nearly half that of the SVM model (0.068 s per layer). Most critically, the CNN model completely eliminated fatal cross-class misclassification between OM and LOF—an error mode common in the SVM model that would trigger erroneous process corrective actions in practical industrial applications. The findings demonstrate that image-based machine learning provides a reliable technical foundation for intelligent in situ monitoring of the L-PBF process. With its high accuracy, strong robustness, and superior computational efficiency, the CNN model can effectively support on-site operational decision-making, reduce material and time losses, and enhance process stability in industrial settings, thus exhibiting significant potential for practical engineering deployment. Full article