Triboelectric Nanogenerator Made with Stretchable, Antibacterial Hydrogel Electrodes for Biomechanical Sensing.

Triboelectric nanogenerators (TENGs) have attracted widespread attention as a promising candidate for energy harvesting due to their flexibility and high power density. To meet diverse application scenarios, a highly stretchable (349%), conductive (1.87 S m-1), and antibacterial electrode composed of carbon quantum dots/LiCl/agar-polyacrylamide (CQDs/LiCl/agar-PAAm) dual-network (DN) hydrogel is developed for wearable TENGs. Notably, the concentration of agar alters the pore spacing and pore size of the DN hydrogel, thereby impacting the network cross-linking density and the migration of conductive ions (Li+ and Cl-). This variation further affects the mechanical strength and conductivity of the hydrogel electrode, thus modulating the mechanical stability and electrical output performance of the TENGs. With the optimal agar content, the tensile strength and conductivity of the hydrogel electrode increase by 211 and 719%, respectively. This enhancement ensures the stable output of TENGs during continuous operation (6000 cycles), with open-circuit voltage, short-circuit current, and transferred charge increasing by 200, 530, and 155%, respectively. Additionally, doping with CQDs enables the hydrogel electrode to effectively inhibit the Gram-negative bacterium Escherichia coli. Finally, the TENGs are utilized as a self-power smart ring for efficient and concise information transmission via Morse code. Consequently, this study introduces a creative approach for designing and implementing multifunctional, flexible wearable devices.

High-Throughput Sequencing Analysis Revealed a Preference for Animal-Based Food in Purple Sea Urchins.

Sea urchins play an important role in marine ecosystems. Owing to limitations in previous research methods, there has been insufficient understanding of the food sources and ecological functional value of purple sea urchins, leading to considerable controversy regarding their functional positioning. We focused on Daya Bay as the research area, utilizing stable isotope technology and high-throughput sequencing of 16S rDNA and 18S rDNA to analyze sea urchins and their potential food sources in stone and algae areas. The results showed that the δ13C range of purple sea urchins in the stone area is -11.42~-8.17‱, and the δ15N range is 9.15~10.31‱. However, in the algal area, the δ13C range is -13.97~-12.44‱, and the δ15N range is 8.75~10.14‱. There was a significant difference in δ13C between the two areas (p < 0.05), but there was no significant difference in δ15N (p > 0.05). The main food source for purple sea urchins in both areas is sediment. The sequencing results of 18S rDNA revealed that, in the algal area, the highest proportion in the sea urchin gut was Molluska (57.37%). In the stone area, the highest proportion was Arthropoda (76.71%). The sequencing results of 16S rDNA revealed that, in the algal area, Bacteroidetes was the dominant group in the sea urchin gut (28.87%), whereas, in the stone area, Proteobacteria was the dominant group (37.83%). Diversity detection revealed a significant difference in the number of gut microbes and eukaryotes between the stone and algal areas (p < 0.05). The results revealed that the main food source of purple sea urchins in both areas is sediment, but the organic nutritional value is greater in the algal area, and the richness of microbiota and eukaryotes in the gut of purple sea urchins in the stone area is greater. These results indicated that purple sea urchins are likely omnivores and that the area where they occur impacts their growth and development. The results of this study provide a theoretical basis for the restoration of wild purple sea urchin resources and the selection of areas for restocking and release.

High-Performance W-Doped Bi0.5Sb1.5Te3 Flexible Thermoelectric Films and Generators.

Bi-Sb-Te-based thermoelectric materials have the best room-temperature thermoelectric properties, but their inherent brittleness and rigidity limit their application in the wearable field. In this study, W-doped p-type Bi0.5Sb1.5Te3 (W-BST) thin films were prepared using magnetron sputtering on polyimide substrates to create thermoelectric generators (TEGs). Bending tests showed that the thin film has excellent flexibility and mechanical durability, meeting the flexible requirements of wearable devices. W doping can significantly increase the carrier concentration, Seebeck coefficient, and electrical conductivity of BST thin films. At 300 K, the power factor of the W-BST film is 2.25 times higher than that of the undoped film, reaching 13.75 µW cm-1 K-2. First-principles calculations showed that W doping introduces significant impurity peaks in the bandgap, in which W d electrons remarkably hybridize with the Sb and Te p electrons, leading to an improved electrical conductivity of BST films. Furthermore, W doping significantly reduces the work function of BST films, thereby improving the carrier mobility. A TEG module fabricated from four layers of W-BST thin films achieved a maximum output power density of 6.91 mW cm-2 at a temperature difference of 60 K. Application tests showed that the flexible TEG module could power a portable clock using the temperature difference between body temperature and room temperature. At a medium temperature of 439 K, the assembled TEG module can provide a stable output voltage of 1.51 V to power a LED. This study demonstrates the feasibility of combining inorganic thermoelectric materials with flexible substrates to create high-performance flexible TEGs.

End-to-End Diverse Metasurface Design and Evaluation Using an Invertible Neural Network.

Employing deep learning models to design high-performance metasurfaces has garnered significant attention due to its potential benefits in terms of accuracy and efficiency. A deep learning-based metasurface design framework typically comprises a forward prediction path for predicting optical responses and a backward retrieval path for generating geometrical configurations. In the forward design path, a specific geometrical configuration corresponds to a unique optical response. However, in the inverse design path, a single performance metric can correspond to multiple potential designs. This one-to-many mapping poses a significant challenge for deep learning models and can potentially impede their performance. Although representing the inverse path as a probabilistic distribution is a widely adopted method for tackling this problem, accurately capturing the posterior distribution to encompass all potential solutions remains an ongoing challenge. Furthermore, in most pioneering works, the forward and backward paths are captured using separate models. However, the knowledge acquired from the forward path does not contribute to the training of the backward model. This separation of models adds complexity to the system and can hinder the overall efficiency and effectiveness of the design framework. Here, we utilized an invertible neural network (INN) to simultaneously model both the forward and inverse process. Unlike other frameworks, INN focuses on the forward process and implicitly captures a probabilistic model for the inverse process. Given a specific optical response, the INN enables the recovery of the complete posterior over the parameter space. This capability allows for the generation of novel designs that are not present in the training data. Through the integration of the INN with the angular spectrum method, we have developed an efficient and automated end-to-end metasurface design and evaluation framework. This novel approach eliminates the need for human intervention and significantly speeds up the design process. Utilizing this advanced framework, we have effectively designed high-efficiency metalenses and dual-polarization metasurface holograms. This approach extends beyond dielectric metasurface design, serving as a general method for modeling optical inverse design problems in diverse optical fields.

A wearable 3D pressure sensor based on electrostatic self-assembly MXene/chitosan sponge and insulating PVP spacer.

It has been shown that flexible pressure sensors may be used in many different contexts, including human-machine interaction, intelligent robots, and health monitoring. In this work, we create a 3D sponge piezoresistive pressure sensor using MXene, chitosan, polyurethane sponge, and polyvinyl pyrrolidone (MXene/CS/PU sponge/PVP), with the well-conductive MXene nanosheet serving as the force sensitive material. In particular, the mechanical strength and endurance of the sensor are enhanced by electrostatic self-assembly between the negatively charged MXene nanosheets and the positively charged CS/PU composite sponge skeleton. The insulating PVP nanowires (PVP-NWs) also decreases the device's initial current, increasing the sensor's sensitivity. These characteristics allow the pressure sensor to simultaneously have a high sensitivity (50.27 kPa-1for pressure below 7 kPa and 13.3 kPa-1for pressure between 7 and 16 kPa), a quick response time (160 ms), a short recovery time (130 ms), and excellent cycling stability (5000 cycles). Moreover, the sensor exhibits a waterproof performance, where the force-sensitive layer still works normally after cleaning. In practice, the sensor could detect a variety of human actions as well as the distribution of spatial pressure due to the above superior device performance.

Plasmon-Enhanced Photocatalytic CO2 Reduction for Higher-Order Hydrocarbon Generation Using Plasmonic Nano-Finger Arrays.

The carbon dioxide reduction reaction (CO2RR) is a promising method to both reduce greenhouse gas carbon dioxide (CO2) concentrations and provide an alternative to fossil fuel by converting water and CO2 into high-energy-density chemicals. Nevertheless, the CO2RR suffers from high chemical reaction barriers and low selectivity. Here we demonstrate that 4 nm gap plasmonic nano-finger arrays provide a reliable and repeatable plasmon-resonant photocatalyst for multiple-electrons reactions: the CO2RR to generate higher-order hydrocarbons. Electromagnetics simulation shows that hot spots with 10,000 light intensity enhancement can be achieved using nano-gap fingers under a resonant wavelength of 638 nm. From cryogenic 1H-NMR spectra, formic acid and acetic acid productions are observed with a nano-fingers array sample. After 1 h laser irradiation, we only observe the generation of formic acid in the liquid solution. While increasing the laser irradiation period, we observe both formic and acetic acid in the liquid solution. We also observe that laser irradiation at different wavelengths significantly affected the generation of formic acid and acetic acid. The ratio, 2.29, of the product concentration generated at the resonant wavelength 638 nm and the non-resonant wavelength 405 nm is close to the ratio, 4.93, of the generated hot electrons inside the TiO2 layer at different wavelengths from the electromagnetics simulation. This shows that product generation is related to the strength of localized electric fields.

Ultrafast Early Warning of Heart Attacks through Plasmon-Enhanced Raman Spectroscopy using Collapsible Nanofingers and Machine Learning.

As the leading cause of death, heart attacks result in millions of deaths annually, with no end in sight. Early intervention is the only strategy for rescuing lives threatened by heart disease. However, the detection time of the fastest heart-attack detection system is >15 min, which is too long considering the rapid passage of life. In this study, a machine learning (ML)-driven system with a simple process, low-cost, short detection time (only 10 s), and high precision is developed. By utilizing a functionalized nanofinger structure, even a trace amount of biomarker leaked before a heart attack can be captured. Additionally, enhanced Raman profiles are constructed for predictive analytics. Five ML models are developed to harness the useful characteristics of each Raman spectrum and provide early warnings of heart attacks with >98% accuracy. Through the strategic combination of nanofingers and ML algorithms, the proposed warning system accurately provides alerts on silent heart-attack attempts seconds ahead of actual attacks.

Alloying Pt into Ni partially amorphous for promoted alkaline hydrogen production.

Aiming at the sluggish water dissociation step in alkaline hydrogen evolution reaction (HER), the platinum-nickel alloy material (PtNi10/C) featuring unique crystalline/amorphous structure supported on carbon black is deliberately designed and fabricated via a reversely rapid co-precipitation and mild thermal reduction strategy. Electrochemical results show that only 66 mV of overpotential is needed for PtNi10/C to drive a current density of 10 mA cm-2 at a lower platinum loading (8.3 µgPt cm-2 geo), which is much lower than that of other catalysts with a single metal source(S-Ni/C and S-Pt/C) and even the commercial Pt/C catalyst (20 wt%). The target catalyst also exhibits smaller tafel slope value (16.73 mV dec-1) and electrochemical impedance value, enabling a fast kinetics rate for water dissociation. Partial crystallization facilitates moderate adsorption of intermediates, while the high-valence Ni(II) and Pt(II) species serve as pivotal driving force for the kinetic dissociation of water. The unique microstructure of PtNi10/C shows a remarkable advantage toward HER in alkaline but acidic medium. In addition, other transition metal-based catalysts following the similar protocol are also fabricated and present varying degrees of HER performance. Hence, the facile and rapid co-precipitation/thermal reduction strategy proposed in this study provides some guidelines for designing high-efficiency alkaline HER catalysts.

Hot Electron-Driven Photocatalysis Using Sub-5 nm Gap Plasmonic Nanofinger Arrays.

Semiconductor photocatalysis has received increasing attention because of its potential to address problems related to the energy crisis and environmental issues. However, conventional semiconductor photocatalysts, such as TiO2 and ZnO, can only be activated by ultraviolet light due to their wide band gap. To extend the light absorption into the visible range, the localized surface plasmon resonance (LSPR) effect of noble metal nanoparticles (NPs) has been widely used. Noble metal NPs can couple incident visible light energy to strong LSPR, and the nonradiative decay of LSPR generates nonthermal hot carriers that can be injected into adjacent semiconductor material to enhance its photocatalytic activity. Here we demonstrate that nanoimprint-defined gap plasmonic nanofinger arrays can function as visible light-driven plasmonic photocatalysts. The sub-5 nm gaps between pairs of collapsed nanofingers can support ultra-strong plasmon resonance and thus boost the population of hot carriers. The semiconductor material is exactly placed at the hot spots, providing an efficient pathway for hot carrier injection from plasmonic metal to catalytic materials. This nanostructure thus exhibits high plasmon-enhanced photocatalytic activity under visible light. The hot carrier injection mechanism of this platform was systematically investigated. The plasmonic enhancement factor was calculated using the finite-difference time-domain (FDTD) method and was consistent with the measured improvement of the photocatalytic activity. This platform, benefiting from the precise controllable geometry, provides a deeper understanding of the mechanism of plasmonic photocatalysis.

Beam stability of buried-heterostructure quantum cascade lasers employing HVPE regrowth.

Measurements of beam stability for mid-infrared (IR)-emitting quantum cascade lasers (QCLs) are important for applications that require the beam to travel through air to remote targets, such as free-space communication links. We report beam-quality measurement results of narrow-ridge, 4.6 µm-emitting buried-heterostructure (BH) QCLs fabricated using ICP etching and HVPE regrowth. Beam-quality measurements under QCW operation exhibit M2 < 1.2 up to 1 W for ∼5 µm-wide ridges. 5 µm-wide devices display some small degree of centroid motion with increasing output power (< 0.125 mrad), which corresponds to a targeting error of ∼1.25 cm over a distance of 100 m.

Probing the Mechanisms of Strong Fluorescence Enhancement in Plasmonic Nanogaps with Sub-nanometer Precision.

Plasmon-enhanced fluorescence is demonstrated in the vicinity of metal surfaces due to strong local field enhancement. Meanwhile, fluorescence quenching is observed as the spacing between fluorophore molecules and the adjacent metal is reduced below a threshold of a few nanometers. Here, we introduce a technology, placing the fluorophore molecules in plasmonic hotspots between pairs of collapsible nanofingers with tunable gap sizes at sub-nanometer precision. Optimal gap sizes with maximum plasmon enhanced fluorescence are experimentally identified for different dielectric spacer materials. The ultrastrong local field enhancement enables simultaneous detection and characterization of sharp Raman fingerprints in the fluorescence spectra. This platform thus enables in situ monitoring of competing excitation enhancement and emission quenching processes. We systematically investigate the mechanisms behind fluorescence quenching. A quantum mechanical model is developed which explains the experimental data and will guide the future design of plasmon enhanced spectroscopy applications.