Self-powered visualized tactile-acoustic sensor for accurate artificial perception with high brightness and record-low detection limit.

The growth of the Internet of Things has focused attention on visualized sensors as a key technology. However, it remains challenging to achieve high sensing accuracy and self-power ability. Here, we propose a self-powered visualized tactile-acoustic sensor (SVTAS) based on an elaborated triboelectrification-induced electroluminescence (TIEL) unit.

Study on the enhancement of low carbon-to-nitrogen ratio urban wastewater pollutant removal efficiency by adding sulfur electron acceptors.

The effective elimination of nitrogen and phosphorus in urban sewage treatment was always hindered by the deficiency of organic carbon in the low C/N ratio wastewater. To overcome this organic-dependent barrier and investigate community changes after sulfur electron addition. In this study, we conducted a simulated urban wastewater treatment plant (WWTP) bioreactor by using sodium sulfate as an electron acceptor to explore the removal efficiency of characteristic pollutants before and after the addition of sulfur electron acceptor.

DFT mechanistic insights into the formation of the metal-dioxygen complex [Co(12-TMC)O] using HO as an [O] unit source.

The reaction of [M(L)] with HO as an [O] unit source and NEt as a base is a widely used biomimetic transition metal-peroxo and -superoxo complex [M(L)O] synthesis method, but the mechanism and accurate stoichiometry of the synthesis remain elusive. In this study, we performed DFT calculations to deeply understand the mechanism, using the synthesis of the cobalt-peroxo complex [Co(12-TMC)O] (12-TMC = (1,4,7,10-tetramethyl-1,4,7,10-tetraazacyclododecane)) from the reaction of [Co(12-TMC)] and HO in the presence of NEt as an example. The study found that cobalt-peroxo complex formation proceeds three stages: (Stage I) the conversion of [Co(12-TMC)] and HO to [Co(12-TMC)OH] and OOH˙ radical, (Stage II) the coordination of OOH˙ to [Co(12-TMC)] to give [Co(12-TMC)OOH], followed by deprotonation with NEt, affording [Co(12-TMC)O], and (Stage III) the transformation of [Co(12-TMC)OH] which is generated in Stage I to [Co(12-TMC)O].

Heat-Excitation-Based Triboelectric Charge Promotion Strategy.

The surface charge decay is observed at high temperatures due to thermionic emission, which, however, may not be the only mechanism contributing to the surface charge variation. Here, a triboelectric charge promotion strategy due to the heat-excitation effect of hot electrons near the fermi level is demonstrated, while the final charge is determined by the balance between thermionic emission and the heat-excitation effect. It is demonstrated that metals with lower work function exhibit a better heat excitation capability, and polymers with lower fluorine content in molecule chains further boost the charge output, where metal/Kapton pairs demonstrated a charge promotion of over 2 times at the temperature of 383 K with good durability during 90 min measurement.

Transition Metal-Gallium Intermetallic Compounds with Tailored Active Site Configurations for Electrochemical Ammonia Synthesis.

Gallium (Ga) with a low melting point can serve as a unique metallic solvent in the synthesis of intermetallic compounds (IMCs). The negative formation enthalpy of transition metal-Ga IMCs endows them with high catalytic stability. Meanwhile, their tunable crystal structures offer the possibility to tailor the configurations of active sites to meet the requirements for specific catalytic applications.

Benchmarking Mendelian randomization methods for causal inference using genome-wide association study summary statistics.

Mendelian randomization (MR), which utilizes genetic variants as instrumental variables (IVs), has gained popularity as a method for causal inference between phenotypes using genetic data. While efforts have been made to relax IV assumptions and develop new methods for causal inference in the presence of invalid IVs due to confounding, the reliability of MR methods in real-world applications remains uncertain. Instead of using simulated datasets, we conducted a benchmark study evaluating 16 two-sample summary-level MR methods using real-world genetic datasets to provide guidelines for the best practices.

Biomimetic bimodal haptic perception using triboelectric effect.

Multimodal haptic perception is essential for enhancing perceptual experiences in augmented reality applications. To date, several artificial tactile interfaces have been developed to perceive pressure and precontact signals, while simultaneously detecting object type and softness with quantified modulus still remains challenging. Here, inspired by the campaniform sensilla on insect antennae, we proposed a hemispherical bimodal intelligent tactile sensor (BITS) array using the triboelectric effect.

Reversibly-bonded microfluidic devices for stable cell culture and rapid, gentle cell extraction.

Microfluidic chips have emerged as significant tools in cell culture due to their capacity for supporting cells to adopt more physiologically relevant morphologies in 3D compared with traditional cell culture in 2D. Currently, irreversible bonding methods, where chips cannot be detached from their substrates without destroying the structure, are commonly used in fabrication, making it challenging to conduct further analysis on cells that have been cultured on-chip. Although some reversible bonding techniques have been developed, they are either restricted to certain materials such as glass, or require complex processing procedures.

Effect of temperature and humidity on mechanical properties and constitutive modeling of pressure-sensitive adhesives.

Pressure-sensitive adhesives (PSAs) are crucial for the structural and functional integrity of flexible displays. Investigating the intricate mechanical properties of PSAs can help enhance product quality and performance. This study conducts systematic mechanical tests, including uniaxial tensile, compression, planar shear, and stress relaxation, on PSAs at temperatures ranging from - 25 to 85 ℃ and relative humidity levels from 0 to 90%.

Dissecting PM sensor capabilities: A combined experimental and theoretical study on particle sizing and physicochemical properties.

Recent advancements in particulate matter (PM) optical measurement technology have enhanced the characterization of particle size distributions (PSDs) across various temporal and spatial scales, offering a more detailed analysis than traditional PM mass concentration monitoring. This study employs field experiments, laboratory tests, and model simulations to evaluate the influence of physicochemical characteristics of particulate matter (PM) on the performance of a compact, multi-channel PM sizing sensor. The sensor is integrated within a mini air station (MAS) designed to detect particles across 52 channels.

Investigating the temporal dynamics of motor vehicle collision density patterns in urban road networks - A case study of New York.

Introduction: Motor vehicle collisions are a leading source of mortality and injury on urban highways. From a temporal perspective, the determination of a road segment as being collision-prone over time can fluctuate dramatically, making it difficult for transportation agencies to propose traffic interventions. However, there has been limited research to identify and characterize collision-prone road segments with varying collision density patterns over time.

A Compact-Sized Fully Self-Powered Wireless Flowmeter Based on Triboelectric Discharge.

Flow sensing exhibits significant potential for monitoring, controlling, and optimizing processes in industries, resource management, and environmental protection. However, achieving wireless real-time and omnidirectional sensing of gas/liquid flow on a simple, self-contained device without external power support has remained a formidable challenge. In this study, a compact-sized, fully self-powered wireless sensing flowmeter (CSWF) is introduced with a small size diameter of down to less than 50 mm, which can transmit real-time and omnidirectional wireless signals, as driven by a rotating triboelectric nanogenerator (R-TENG).

Breaking Highly Ordered PtPbBi Intermetallic with Disordered Amorphous Phase for Boosting Electrocatalytic Hydrogen Evolution and Alcohol Oxidation.

Constructing amorphous/intermetallic (A/IMC) heterophase structures by breaking the highly ordered IMC phase with disordered amorphous phase is an effective way to improve the electrocatalytic performance of noble metal-based IMC electrocatalysts because of the optimized electronic structure and abundant heterophase boundaries as active sites. In this study, we report the synthesis of ultrathin A/IMC PtPbBi nanosheets (NSs) for boosting hydrogen evolution reaction (HER) and alcohol oxidation reactions. The resulting A/IMC PtPbBi NSs exhibit a remarkably low overpotential of only 25 mV at 10 mA cm for the HER in an acidic electrolyte, together with outstanding stability for 100 h.

High-Throughput and Scalable Exfoliation of Large-Sized Ultrathin 2D Materials by Ball-Milling in Supercritical Carbon Dioxide.

The 2D materials exhibit numerous technological applications, but their scalable production is a core challenge. Herein, ball milling exfoliation in supercritical carbon dioxide (scCO) and polystyrene (PS) is demonstrated to completely exfoliate hexagonal boron nitride nanosheets (BNNSs), graphene, molybdenum disulfide (MoS), and tungsten disulfide (WS). The exfoliation yield of 91%, 93%, 92%, and 92% and average aspect ratios of 743, 565, 564, and 502 for BNNSs, graphene, MoS, and WS respectively, are achieved.

Spatiotemporal investigation of near-surface CH and factors influencing CH over South, East, and Southeast Asia.

Methane (CH) is the second most abundant greenhouse gas after CO, which plays the most important role in global and regional climate change. To explore the long-term spatiotemporal variations of near-surface CH, datasets were extracted from Greenhouse gases Observing SATellite (GOSAT), and the Copernicus Atmospheric Monitoring Service (CAMS) reanalyzed datasets from June 2009 to September 2020 over South, East, and Southeast Asia. The accuracy of near-surface CH from GOSAT and CAMS was verified against surface observatory stations available in the study region to confirm both dataset applicability and results showed significant correlations.

Non-mitotic proliferation of malignant cancer cells revealed through live-cell imaging of primary and cell-line cultures.

Introduction: Anti-mitosis has been a key strategy of anti-cancer therapies, targeting at a fundamental property of cancer cells, their non-controllable proliferation due to overactive mitotic divisions. For improved anti-cancer therapies, it is important to find out whether cancer cells can proliferate independent of mitosis and become resistant to anti-mitotic agents.

Results: In this study, live-cell imaging was applied to both primary-cultures of tumor cells, and immortalized cancer cell lines, to detect aberrant proliferations.

Detecting Abnormality of Battery Lifetime from First-Cycle Data Using Few-Shot Learning.

The service life of large battery packs can be significantly influenced by only one or two abnormal cells with faster aging rates. However, the early-stage identification of lifetime abnormality is challenging due to the low abnormal rate and imperceptible initial performance deviations. This work proposes a lifetime abnormality detection method for batteries based on few-shot learning and using only the first-cycle aging data.

XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias.

Fine-mapping prioritizes risk variants identified by genome-wide association studies (GWASs), serving as a critical step to uncover biological mechanisms underlying complex traits. However, several major challenges still remain for existing fine-mapping methods. First, the strong linkage disequilibrium among variants can limit the statistical power and resolution of fine-mapping.

Passive Internet of Events Enabled by Broadly Compatible Self-Powered Visualized Platform Toward Real-Time Surveillance.

Surveillance is an intricate challenge worldwide especially in those complicated environments such as nuclear plants, banks, crowded areas, barns, etc. Deploying self-powered wireless sensor nodes can increase the system's event detection capabilities by collecting environmental changes, while the incompatibility among components (energy harvesters, sensors, and wireless modules) limits their application. Here, a broadly compatible self-powered visualized platform (SPVP) is reported to construct a passive internet of events (IoE) network for surveillance systems.

Removal of Cr (VI) and microbial community analysis in PCB wastewater treatment based on the BESI® process.

The treatment efficiency of Chromium (Cr)-containing Printed Circuit Board (PCB) wastewater is significantly hampered by the limited physiological activity of microorganisms when activated sludge is applied. In this study, the biodegradation and electron transfer based on sulfur metabolism in the integrated (BESI®) process use sulfur as the electron acceptor to achieve sulfate reduction and sulfide oxidation, leading to efficient removal of Cr. The concentrations of total Cr and Cr(VI) in the effluent were reduced to 0.

Gel transformation as a general strategy for fabrication of highly porous multiscale MOF architectures.

The structure and chemistry of metal-organic frameworks or MOFs dictate their properties and functionalities. However, their architecture and form are essential for facilitating the transport of molecules, the flow of electrons, the conduction of heat, the transmission of light, and the propagation of force, which are vital in many applications. This work explores the transformation of inorganic gels into MOFs as a general strategy to construct complex porous MOF architectures at nano, micro, and millimeter length scales.

Predicting battery impedance spectra from 10-second pulse tests under 10 Hz sampling rate.

Onboard measuring the electrochemical impedance spectroscopy (EIS) for lithium-ion batteries is a long-standing issue that limits the technologies such as portable electronics and electric vehicles. Challenges arise from not only the high sampling rate required by the Shannon Sampling Theorem but also the sophisticated real-life battery-using profiles. We here propose a fast and accurate EIS predicting system by combining the fractional-order electric circuit model-a highly nonlinear model with clear physical meanings-with a median-filtered neural network machine learning.