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Direct ink writing (DIW) of high-temperature thin-film sensors holds significant potential for monitoring extreme environments. However, existing high-temperature inks face a trade-off between cost and performance. This study proposes a SiCN/RuO2/TiB2 composite ceramic ink. The added TiB2, after annealing in a high-temperature atmospheric environment, forms B2O3 glass, which synergizes with the SiO2 glass phase formed from the SiCN precursor to effectively encapsulate RuO2 particles. This enhances the film's density and adhesion to the substrate, preventing RuO2 volatilization at high temperatures. Additionally, the high conductivity of TiB2 improves the film's overall conductivity. Test results indicate that the SiCN/RuO2/TiB2 film exhibits high linearity from room temperature to 900 °C, high stability (resistance drift rate of 0.1%/h at 800 °C), and high conductivity (4410 S/m). As a proof of concept, temperature sensors and a heat flux sensor were successfully fabricated on a metallic hemispherical surface. Performance tests in extreme environments using high-power lasers and flame guns verified that the conformal thin-film sensor can accurately measure spherical temperature and heat flux, with a heat flux sensor response time of 53 ms. In conclusion, the SiCN/RuO2/TiB2 composite ceramic ink developed in this study offers a high-performance and cost-effective solution for high-temperature conformal thin-film sensors in extreme environments. [ABSTRACT FROM AUTHOR]

Cryptococcus is a family of strongly infectious pathogens that results in a wide variety of symptoms, particularly threatening the patients undergoing the immune‐deficiency or medical treatment. Rapidly identifying Cryptococcus subtypes and accurately quantifying their contents remain urgent needs for infection control and timely therapy. However, traditional detection techniques heavily rely on expensive, specialized instruments, significantly compromising their applicability for large‐scale population screening. In this work, we report a portable microwell array chip platform integrated with a deep learning‐based image recognition program, which enables rapid, precise quantification of the specific subtypes of Cryptococcus. The platform features four zones of microwell arrays preloaded with the subtype‐targeted CRISPR–Cas12a system that avoid dependence on slow, instrumental‐mediated target amplification, achieving rapid (10 min), high specificity for identifying the sequence of Cryptococcus. The deep learning‐based image recognition program utilizing segment anything model (SAM) significantly enhances automation and accuracy in identifying target concentrations, which eventually achieves ultra‐low limit of detection (0.5 pM) by personal smartphones. This platform can be further customized to adapt to various scenarios in clinical settings. [ABSTRACT FROM AUTHOR]

Conformal thin film sensing represents a cutting-edge technology capable of precisely measuring complex surface temperature fields under extreme conditions. However, fabricating high-temperature-resistant conformal thin film thermocouple arrays remains challenging. This study reports a method for manufacturing conformal thin film thermocouple arrays on metal spherical surfaces using a printable paste composed of silicates and Ag. Specifically, the use of silicate glass phases enhances the high-temperature performance of the silver printable paste, enabling the silver ink coatings to withstand temperatures up to 947 °C and survive over 25 h at 900 °C. The thermocouples, connected to Pt thin films, exhibited a Seebeck coefficient of approximately 17 μV/°C. As a proof of concept, an array of six Ag/Pt thin film thermocouples was successfully fabricated on a metal spherical surface. Compared to traditional wire-type thermocouples, the conformal thin film thermocouple arrays more accurately reflect temperature variations at different points on a spherical surface. The Ag/Pt conformal thin film thermocouple arrays hold promise for monitoring temperature fields in harsh environments, such as aerospace and nuclear energy applications. [ABSTRACT FROM AUTHOR]

Strain measurements are vital in engineering trials, testing, and scientific research. In the process of signal acquisition, baseline drift has a significant impact on the accuracy and validity of data. Traditional solutions, such as discrete wavelet transform and empirical mode decomposition, cannot be used in real-time systems. To solve this problem, this paper proposes a Transformer-based model to eliminate the drift in the signal. A self-attentive mechanism is utilized in the encoder of the model to learn the interrelationships between the components of the input signal, and captures the key features. Then, the decoder generates a corrected signal. Meanwhile, a high-precision strain acquisition system is constructed. The experiments tested the model's ability to remove drift from simulated voltage signals with and without Gaussian noise. The results demonstrated that the transformer model excels at eliminating signal baseline drift. Additionally, the performance of the model was investigated under different temperature conditions and with different levels of force applied by the electronic universal testing machine to produce strain. The experimental results indicate that the Transformer model can largely eliminate drift in dynamic signals l and has great potential for practical applications. [ABSTRACT FROM AUTHOR]

The Cu2ZnSn(S,Se)4 (CZTSSe) material is considered a promising semiconductor material for commercial photovoltaic applications due to its high theoretical efficiency, high absorption coefficient, tunable direct bandgap, high element abundance, and low production cost. However, the efficiency of solution-processed CZTSSe thin-film solar cells still falls short of their theoretical efficiency limit (∼31.0%) and that of their predecessor copper indium gallium selenide (CIGS) cells. The relatively low device efficiency is mainly due to bulk and interface defects generated during the preparation of the absorber layer. For achieving a high-quality CZTSSe absorber layer via solution processing, the properties of the solvent are essential. To further improve the efficiency of CZTSSe cells prepared by solution methods, this review provides a detailed summary of the current research status on the selection of solvents and solutes or precursor materials, regulation of bulk and interface defects, optimization of various functional layers, and design of device structures in the preparation of high-efficiency CZTSSe by six solution methods based on different solvent systems (hydrazine, 1,2-ethylenediamine and 1,2-ethanedithiol, dimethyl sulfoxide, N,N-dimethylformamide, 2-methoxyethanol, and thioglycolic acid–ammonia systems). Finally, this article discusses new insights and perspectives for achieving high-efficiency CZTSSe thin-film solar cells. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) technology provides a sustainable solution to meet the increasing energy demands from society. Silver sulfide (Ag2S), a direct bandgap PV material, is considered a promising semiconductor due to its excellent optical and electrical properties, including high theoretical efficiency (∼30%), tunable bandgap (Eg = 0.9–1.1 eV), high thermodynamic stability, low toxicity, abundant elemental availability, and low fabrication cost. Despite efforts to improve the photovoltaic conversion efficiency of Ag2S devices through various approaches such as film deposition techniques, device structures, and interface optimization, the quality of the absorber layer, structural defects, and non-ideal charge separation at the interfaces remain obstacles for further performance enhancement. Therefore, it is crucial to gain a deeper understanding of the fabrication methods, optoelectronic properties, and efficiency loss mechanisms of Ag2S solar cells to achieve rapid advancements in device performance. This review summarizes the current research status on the fabrication methods, device structure selection, design, and optimization of Ag2S thin films. Finally, insights into achieving high-efficiency Ag2S devices by improving the crystallinity of the absorber layer and reducing interface defects are discussed. [ABSTRACT FROM AUTHOR]

The pavement surface will inevitably develop cracks, subsidence, pits, ruts, and other defects due to the effects of traffic and the environment. In road engineering, vibration-based pavement condition monitoring has been widely adopted. Due to differences in vehicle dynamics, acquisition equipment, and road quality, the application of these techniques to the identification of minor pavement damage is limited. With the widespread adoption of the inertial navigation system (INS) in autonomous vehicles, INS-based pavement evaluation has emerged as a promising new technique. In this paper, the acceleration sensor, gyroscope, GPS, and other INS-integrated devices were used to collect data on vehicle attitude changes. For the detection of pavement apparent millimetre disease, a new method utilising INS data and machine learning was proposed. The method analyses the original vibration signal in time domain, determines the degree of sensing parameter influence, and extracts the index that can characterise the signal change. Multiple machine learning recognition models have been built to effectively classify road conditions, with the best-performing model achieving an F1 score of 99.61% and precision of 99.33%. The recall rate, accuracy rate, and F1 score of disease height classification were all above 0.7 on a macro and micro scale. [ABSTRACT FROM AUTHOR]

A strong polarization electric field (PEF) in III‐nitride semiconductors has adverse effects on the performance of III‐N light‐emitting diodes (LEDs). However, to reduce the PEF of III‐N LEDs based on the polar plane (c‐plane) remains a big challenge. Here, a weak PEF blue LED on c‐plane GaN with an InGaN/AlGaN superlattices (SLs) quantum barrier and InGaN quantum well grown by metal‐organic chemical vapor deposition is proposed. The InGaN/SLs multiple quantum wells (MQWs) show a significant reduction of PEF compared with traditional InGaN/GaN MQWs. A low electric field of 0.5 MV cm−1 is obtained, which is confirmed by power‐dependent photoluminescence (PL) and time‐resolved PL measurements. The proposed LEDs with InGaN/AlGaN SLs barriers present a low efficiency droop, a great wavelength stability, and a high −3 dB E‐O modulation bandwidth up to 1.01 GHz. This work demonstrates the role of InGaN/AlGaN SLs barriers in reducing the PEF of III‐N LEDs and provides a feasible technical route to achieve the weak PEF LEDs on c‐plane, which can be expanded and applied to the preparation of high‐performance, long wavelength, and deep UV III‐N LEDs. [ABSTRACT FROM AUTHOR]

We present a novel neural surface reconstruction method called NeuralRoom for reconstructing room-sized indoor scenes directly from a set of 2D images. Recently, implicit neural representations have become a promising way to reconstruct surfaces from multiview images due to their high-quality results and simplicity. However, implicit neural representations usually cannot reconstruct indoor scenes well because they suffer severe shape-radiance ambiguity. We assume that the indoor scene consists of texture-rich and flat texture-less regions. In texture-rich regions, the multiview stereo can obtain accurate results. In the flat area, normal estimation networks usually obtain a good normal estimation. Based on the above observations, we reduce the possible spatial variation range of implicit neural surfaces by reliable geometric priors to alleviate shape-radiance ambiguity. Specifically, we use multiview stereo results to limit the NeuralRoom optimization space and then use reliable geometric priors to guide NeuralRoom training. Then the NeuralRoom would produce a neural scene representation that can render an image consistent with the input training images. In addition, we propose a smoothing method called perturbation-residual restrictions to improve the accuracy and completeness of the flat region, which assumes that the sampling points in a local surface should have the same normal and similar distance to the observation center. Experiments on the ScanNet dataset show that our method can reconstruct the texture-less area of indoor scenes while maintaining the accuracy of detail. We also apply NeuralRoom to more advanced multiview reconstruction algorithms and significantly improve their reconstruction quality. [ABSTRACT FROM AUTHOR]

Nitrogen-polar (N-polar) III-nitride has natural advantages in the fabrication of electronic devices. In this work, unintentionally doped N-polar GaN films were grown on sapphire substrates by metalorganic chemical vapor deposition. By optimizing the growth parameters including growth temperature, pressure, and V/III ratio, N-polar GaN with a relatively high sheet resistivity of 3.6 × 105 Ω/sq was achieved. The secondary ion mass spectroscopy and x-ray diffraction measurement results demonstrate that the increased carbon impurity concentration is primarily responsible for the high resistivity. Moreover, N-polar GaN/Al0.25Ga0.75N heterojunctions were deposited on the high-resistivity N-polar GaN template. An extremely high-density two-dimensional electron gas of up to 1.12 × 1013 cm−2 was realized at the interface between the Al0.25Ga0.75N and the GaN. It is reasonably believed that the experimental results obtained here are beneficial for the development of high-performance N-polar GaN-based electronic devices. [ABSTRACT FROM AUTHOR]