Generating a spin current naturally increases the magnetic dissipation of its source, and this unavoidable rise in dissipation presents a substantial obstacle to achieving high-efficiency and low-loss spintronics applications. Despite substantial efforts to reduce dissipation, its positive correlation with spin current output remains an insurmountable barrier. Here we demonstrate a counterintuitive phenomenon: the effective damping of an artificial ferrimagnet, which quantifies the dissipation in its dynamics, is negatively correlated with the spin current output. In other words, higher output equals lower dissipation. To explain this unexpected result, we propose a complex mechanism in which inter-magnet pumping can counter dissipation in the presence of spin current output, transforming the usual increase in dissipation into a decrease. Along with this, we observe an improvement in spin current output efficiency, quantified through the effective spin-mixing conductance. These findings revise the current understanding of spin dynamics and could provide insights useful for developing high-efficiency, low-loss spintronic devices.

Hexavalent chromium (Cr(VI)) is a toxic and carcinogenic pollutant that poses a serious health risk to humans and other living organisms. Single-atom catalysts (SACs) have promising prospects in environmental pollutants (Cr(VI)) detection fields, featuring atomic-level active sites and high atom utilization. However, SACs may face inevitable aggregation during the synthesis or reaction processes. The catalytic efficiency of SACs is limited in complex multi-step reactions due to the simplex active site, which hinders the practical application of SACs. Herein, we confine platinum single atoms (Pt SAs) that are isolated by cerium oxide nanoclusters (CeOx) on reduced graphene oxide (Pt/CeOx/RGO) via controlling the moderately oxidation state (calcination temperature 600 ℃), achieving record-breaking ultralow overpotential (0.279 V) for Cr(VI) electrocatalytic reduction. X-ray absorption fine structure spectra indicate that Pt-O bonds of Pt/CeOx/RGO were stretched after absorbing with Cr(VI), which facilitates strong interaction between Pt SAs with Cr(VI). Pt/CeOx/RGO also exhibits remarkable selectivity for Cr(VI) against common interferents (Hg(II), As(III), Cd(II), Pb(II)) and organic interferents (hydrazine, 2,2'-(Ethylenedioxy) diethanethiol, urea, and hydroquinone). Density functional theory calculations reveal that Pt and CeOx exert a dual-site synergistic tandem effect, which enables Pt SAs and CeOx NCs to achieve efficient catalytic reduction through cooperation and division of reaction sites, significantly lowering the Cr(VI) reduction energy barriers. Herein, the demonstrated tandem catalyst architecture not only advances the frontier of SACs but also opens a promising avenue for developing advanced sensing platforms toward environmental monitoring, remediation, and beyond.

The recent identification of α-MnTe as a candidate unconventional magnet combining altermagnetism and anomalous Hall response has attracted considerable interest, particularly for its potential application in magnetic random-access memory. Here, we report the epitaxial growth of centimeter-scale α-MnTe thin films on InP(111)B substrates via molecular beam epitaxy (MBE). We construct a MnTe phase diagram that provides clear guidance for stabilizing the pure α-MnTe phase, revealing that it is favored under high Te/Mn flux ratios and elevated growth temperatures, as determined through X-Ray diffraction (XRD) analysis. Remarkably, as-grown α-MnTe films exhibit an oscillatory X-ray Magnetic Circular Dichroism (XMCD) signal originating from the net contribution of two adjacent Mn sublattices. Furthermore, they exhibit a pronounced anomalous Hall effect (AHE) even as the net magnetic moment approaches zero. These findings provide compelling evidence for the intrinsic bulk altermagnetism and the associated AHE in MBE-grown α-MnTe thin films.

The recent identification of α-MnTe as a candidate altermagnet has attracted considerable interest, particularly for its potential application in magnetic random-access memory. However, the development of high-quality thin films - essential for practical implementation - has remained limited. Here, we report the epitaxial growth of centimeter-scale α-MnTe thin films on InP(111) substrates via molecular beam epitaxy (MBE). Through X-ray diffraction (XRD) analysis, we construct a MnTe phase diagram that provides clear guidance for stabilizing the pure α-MnTe phase, revealing that it is favored under high Te/Mn flux ratios and elevated growth temperatures. Cross-sectional electron microscopy confirms an atomically sharp film-substrate interface, consistent with a layer-by-layer epitaxial growth mode. Remarkably, these high-quality α-MnTe films exhibit a pronounced anomalous Hall effect (AHE) originating from Berry curvature, despite a net magnetic moment approaching zero - a signature of robust altermagnetic character. Our work establishes a viable route for synthesizing wafer-scale α-MnTe thin films and highlights their promise for altermagnet-based spintronics and magnetic sensing.

The sensitivity of low-dimensional superconductors to fluctuations gives rise to emergent behaviors beyond the conventional Bardeen-Cooper-Schrieffer framework. Anisotropy is one such manifestation often linked to spatially modulated electronic responses and unconventional pairing mechanisms. Pronounced in-plane anisotropy recently reported at KTaO3-based oxide interfaces has been interpreted as indicative of a stripelike superconducting texture, yet its microscopic origin and formation pathway remain unresolved. Here, we show that disorder in MgO/KTaO3(111) heterostructures broadens the superconducting transition and reveals transport signatures suggesting a percolative evolution from localized superconducting coherence to a stripelike texture. The stripe width extracted from vortex-dynamical responses is comparable to the spin precession length, suggesting a self-organized modulation influenced by spin-orbit coupling and reduced lattice symmetry. These results highlight disorder as a tuning parameter for superconductivity in two-dimensional quantum materials.

Designing a highly sensitive electrochemical sensing interface and elucidating the mechanism of enhanced electrochemical signals from the perspective of electronic structure are crucial for accurate detection of heavy metal ions (HIMIs). In this study, a composite transition metal sulfide Co9S8@MoS2 heterostructure was developed for the high-sensitivity detection of Pb(II) in Chinese herbal medicine, which achieved a high sensitivity of 103.3 +/- 0.5 mu A mu M- 1 and a detection limit of 0.01 mu M. Moreover, the fundamental reason for the excellent detection of Pb(II) was investigated by X-ray photoelectron spectroscopy (XPS) and density functional theory (DFT). The results indicated that the recombination of the heterojunction not only catalyzes the activation of the Co site in Co9S8 but also promotes S as a medium in the electron channel for increased charge transfer. In addition, the proposed method displayed anti-interference ability with outstanding stability and reproducibility in the presence of other HMIs. Importantly, the highly sensitive detection of Pb(II) has also been inherited in herbal Rehmannia glutinosa samples, demonstrating its potential for practical application in the detection of heavy metal ions in Chinese herbal medicine. This work provides essential theoretical guidance for constructing high-performance electrochemically sensitive interfaces from electronic structure-controlled sensing materials.

Electrochemical techniques have emerged as promising approaches for on-site detection and long-term monitoring of metal ions in environmental and biological samples, owing to their rapid response, high sensitivity, and portability. Many efforts have focused on employing various nanomaterials as electrode modifiers to enhance their sensing performance and exploring the detection mechanism. However, most studies remain limited to comparing pre- and post-reaction states of electrode interfaces, paying insufficient attention to the dynamic interfacial processes and real-time structural evolution at solid–solid and solid–liquid interfaces during electroanalysis. A deeper understanding and exploration of solid–solid and solid–liquid interface reaction characteristics, influenced by various factors, including electric fields, environmental conditions, surface state of electrodes, adsorbate species, electrolyte component, and pH value, is essential for purposefully designing highly efficient sensing interfaces. This review highlights recent advances in probing solid–solid and solid–liquid interfacial characteristics and reaction dynamics via in-situ techniques, dynamics simulations, DFT calculations, and machine learning.

Simultaneous quantification of multiple heavy metal ions remains a significant challenge in electrochemical methods, as complex high-throughput data from signal interference cannot be accurately analyzed through individual expertise and calibration curves. In this study, machine learning techniques were introduced to co-detect Cd(II) and Cu(II), with their electrochemical interference mechanisms explored on highly active Co2P/CoP heterostructures. The random forest (RF) model initially identified key feature variables in response currents, which were subsequently input into the convolutional neural network (CNN) to uncover the relationship between electrochemical signals and ion concentrations, demonstrating excellent reliability with R2 values of 0.996 for both Cd(II) and Cu(II). The root mean square error (RMSE) values for Cd(II) and Cu(II) were 0.0177 and 0.0206 μM, respectively, indicating high predictive accuracy. The experiments and theory calculations revealed that Cu(II) preferentially bonded with P sites over Cd(II). Enhanced electron transfer from Co to P atoms and weakened Cu-P bonds facilitated Cu(II) reduction and desorption from Co2P/CoP, thereby boosting electrochemical signals, while Cd(II) signals were inhibited due to active site loss. Herein, the integration of machine learning provides robust support for simultaneous detection of multiple analytes, accelerating the practical application of electrochemical methods in environmental monitoring.

The increasing demand for thermally conductive adhesives (TCAs) in high-performance electronic devices necessitates materials with both flame retardancy and thermal conductivity. Conventional TCAs often struggle with balancing these properties, particularly due to poor interfacial compatibility between fillers and the resin matrix. To address this challenge, this work introduces a novel approach by functionalizing boron nitride nanosheets (BNNSs), with 2,6-bis(urazole-1-yl) pyridine (UPy). This modification enhances both thermal conductivity and flame retardancy in EP-based composites. Thermogravimetric analysis revealed that the initial decomposition temperature (T5%) of the 20-UPy-BNNSs/EP composite increased to 344.8 degrees C, a 74.0 degrees C rise from pure EP. Microcalorimetry tests showed a 42.7 % reduction in peak heat release rateand a 17.7 % reduction in total heat release compared to pure EP. Vertical burning tests demonstrated that the 20-UPy-BNNSs/EP composite significantly delayed ignition and suppressed molten droplet formation. In practical electronic device testing, the 20-UPy-BNNSs/EP composite reduced the surface temperature of LED drivers by 53.3 degrees C compared to the control, highlighting its effective thermal management. These results confirm that UPy modification improves the interfacial interaction between BNNSs and the EP matrix, leading to increased thermal conductivity and flame retardancy. This work proposes a promising strategy for developing high-performance TCAs tailored to next-generation electronics.

Plasmonic metal–semiconductor nanocomposites are promising candidates for considerably enhancing the solar‐to‐hydrogen conversion efficiency of semiconductor‐based photocatalysts across the entire solar spectrum. However, the underlying enhancement mechanism remains unclear, and the overall efficiency is still low. Herein, a hollow C@MoS 2 ‐Au@CdS nanocomposite photocatalyst is developed to achieve improved photocatalytic hydrogen evolution reaction (HER) across a broad spectral range. Transient absorption spectroscopy experiments and electromagnetic field simulations demonstrate that compared to the treated sample, the untreated sample exhibits a high density of sulfur vacancies. Consequently, under near‐field enhancement, photogenerated electrons from CdS and hot electrons generated by intra‐band or inter‐band transitions of Au nanoparticles are efficiently transferred to the CdS surface, thus significantly improving the HER activity of CdS. Additionally, in situ, Raman spectroscopy provided spectral evidence of S─H intermediate species on the CdS surface during the HER process, which is verified through isotope experiments. Density functional theory simulations identify sulfur atoms in CdS as the catalytic active sites for HER. These findings enhance the understanding of charge transfer mechanisms and HER pathways, offering valuable insights for the design of plasmonic photocatalysts with enhanced efficiency.

Flexible wearable potentiometric ion sensors for continuous monitoring of electrolyte cations have made significant advances in bioanalysis for personal healthcare and diagnostics. However, less attention is paid to the most abundant extracellular anion, chloride ion (Cl-) as a mark of electrolyte imbalance and an important diagnostic indicator of cystic fibrosis, which has important significance for accurate monitoring in complex biological fluids. An all-solid-state Cl--selective electrode is constructed utilizing oxygen vacancies reinforced vanadium oxide with a nitrogen-doped carbon shield as the solid contact (V2O3-x@NC/Cl--ISE). The prepared V2O3-x@NC/Cl--ISE exhibits a low detection limit of 10-5.45 M without an interfacial water layer and shows a highly stable potential with 7.24 μV/h during 24 h, which is attributed to the rapid interfacial electron transfer of the conductive carbon layers and the valence state transition of the polyvalent vanadium center in charge storage processes. Additionally, the custom flexible sensing patch presents an excellent sensitivity retention rate under bending (95%) and twisting (93%) strains and possesses good anti-interference performance (ΔE < 8 mV) against common interfering ions and organic substances in sweat. Real-time monitoring of the Cl- concentration in sweat aligns with ion chromatography analysis results. This study presents a compact wearable Cl- monitoring platform for the easy tracking of exercise-induced dehydration and cystic fibrosis screening with promising applications in smart healthcare.

This study employs a photodeposition method to load Ag and Pt nanoparticles onto the surface and interlayered structure of MXene, developing an efficient catalyst for CO2 reduction in industrial flue gas. The catalyst exhibits excellent thermal catalytic performance within a low-temperature range of 60-100 °C, achieving CH4 and CO production rates of 461 μmol g-1 h-1 and 86 μmol g-1 h-1, respectively, with a CH4 selectivity of 84.3 %. This temperature range requires no additional heating, relying solely on residual heat from flue gas, which offers a distinct temperature advantage and high catalytic efficiency compared to most thermal and photothermal CO2 reduction processes. Under simulated sunlight and at 100 °C, the production rates for CH4 and CO are 34 μmol g-1 h-1 and 589 μmol g-1 h-1, respectively, with a CO selectivity of 94.5 %. Notably, the catalyst demonstrates dual-product selectivity under varying experimental conditions. Experimental characterization and density functional theory (DFT) calculations reveal the thermodynamic and kinetic mechanisms underlying the enhanced production rates and selectivity shifts in both thermal and photothermal catalysis, detailing the CO2 reduction pathways and Gibbs free energy changes across conditions. This study not only provides a new approach for low temperature CO2 catalytic reduction but also offers valuable insights into dual-product selectivity, demonstrating great potential for practical applications in industrial flue gas management.

Although considerable progress has been achieved in miniaturized devices based on all-solid-state ion-selective electrodes, there are still two crucial issues: the operational requirement of frequent calibration restricts industrial production and commercial applications, and the absence of standardized miniaturized reference electrodes may make the performance of reference electrodes fluctuated and thereby affect the potential stability of the sensor. This study presents a method for automatic calibration by analyzing the interfacial process kinetic parameters of ion-selective electrodes with membranes of different volumes and calculating the slope to generate a built-in standard curve. Since this method obtains interfacial process parameters from transient currents, it gets rid of the traditional detection system's dependence on the stable potential provided by reference electrodes. This solution can offer universal adaptability under a unified workflow, making it compatible with diverse detection targets and transduction layer material systems. Taking carbon nanotubes as an example, this study established a standard model based on membranes with volumes of 1, 4, and 7 mu L, achieving self-calibrating detection of sodium ions within a wide concentration range of 0.1-100 mM without a reference electrode (validated within a temperature range of 288-313 K). Experimental results show that the system demonstrates great stability (average relative standard deviation <4%) and accuracy (relative concentration error <3%). This work provides a solution for self-calibration ion detection systems without reference electrodes and also offers methodological support for the miniaturized design and practical application of wearable devices.

Converting CO2 from flue gas into valuable chemicals has always been an important research field. This study developed a thermally assisted photocatalytic reduction of flue gas CO2 system at the gas-solid interface, utilizing an NH2-MXene/TiO2/ZnTCPP (Zn-NMT) composite. Zn-NMT exhibited a CO generation rate of 236.17 mu mol center dot g- 1 center dot h- 1 at 80 degrees C with thermal assistance, achieving 100 % CO selectivity. Notably, it showed superior cyclic stability at 88 %, significantly surpassing the NMT (39 %). The findings indicated that the introduction of photosensitizer ZnTCPP expands the light absorption spectrum, thereby enhancing photonic utilization efficiency. Moreover, ZnTCPP and TiO2 can form an S-scheme heterojunction, and the use of MXene as a charge transport bridge effectively suppresses the recombination of electron-hole pairs generated by photoexcitation, which in turn notably extends the catalyst's longevity. Zn-NMT catalyst shows great potential in reducing CO2 emissions from flue gas and promoting the utilization of CO2 resources, offering new insights and methods for related fields.

Surface-enhanced Raman spectroscopy (SERS) is broadly used in the detection and analysis of materials with its fingerprint-like specificity and high sensitivity. However, resembling signals of analytes highly affect the identification and assignment of spectra, which has become a long-term issue to be solved. In this study, various models of machine learning are utilized and compared to support data analysis of complex SERS spectra. Silver-coated gold core-shell nanocubes (Au@AgNCs) are optimized as SERS substrates for the detection of four common dyes - methylene blue (MB), crystal violet (CV), rhodamine B (RhB) and malachite green (MG). Independent principal component analysis (ICA) was utilized to isolate the signals from the SERS spectra of the dye mixtures, and the isolated signals were further classified by commonly used classification models including K Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forests (RF), and Convolutional Neural Networks (CNN). The results show that the CNN model achieved an accuracy of 98% in the classification of single dyes and an accuracy of 97% in the classification of dye mixtures, which is significantly better than other models. Based on these findings, we propose ICA combined with CNN-assisted SERS spectroscopy as an effective analytical tool for analyzing dye mixtures.

The design of transition metal oxides (TMOs)-based sensing materials for the electrochemical detection of heavy metal ions in water environment is critically important. In this study, we propose an economical and straightforward strategy to synthesize a hollow flower-like NiO@Co3O4 heterojunction catalyst via in situ calcination of NiCo-layered double hydroxide (NiCo-LDH) hollow nanocages, using ZIF-67 as a template. A series of independent experiments including scanning electron microscopy, transmission electron microscopy, and X-ray photoelectron spectroscopy were performed to characterize the NiO@Co3O4 material. Furthermore, the electrochemical performance of NiO@Co3O4 was evaluated through cyclic voltammetry and electrochemical impedance spectroscopy. According to the experiment characterizations, the NiO@Co3O4 heterojunction exhibits a larger specific surface area, improved electrical conductivity, and stronger valence cycle capability between Ni (II)/(III) and Co(II)/(III). When used as a modified electrode-sensitive interface at the glassy carbon electrode (NiO@Co3O4/GCE) with Nafion-assisted, it can provide a sensitivity of 86.20 mu A mu M-1 and a theoretical detection limit of 12.66 nM for the electrochemical detection of Pb(II) within a concentration range of 0.05 -1.0 mu M in an acetate buffer solution (ABS, pH 5.0). Furthermore, the engineered hollow flower-like NiO@Co3O4 sensor demonstrated exceptional long-term stability, anti-interference capability, selectivity, and reproducibility. These characteristics indicate that NiO@Co3O4 is an auspicious material for detecting Pb(II) in actual water samples.

Rapidly advancing all‐solid‐state ion‐selective electrodes are promising candidates as key components in intelligent biological and chemical sensors. Ionics sensing performance, essential for sensor stability and reliability, is influenced not only by interface compositions but by often‐overlooked overall interface structures. This work develops a one‐step adaptive integrated interface structure (AIIS) with high interfacial stability for analyzing general cations (K + , Na + , Ca 2+ , Mg 2+ , Pb 2+ , Cd 2+ , and Cu 2+ ), showcasing exceptional near‐Nernst response across wide linear ranges. AIIS, based on cetyltrimethylammonium‐regulated lipophilic molybdenum disulfide (2.0 CTA‐MoS 2 ), forms single‐piece ISM on top and bottom transduction layers over time due to THF volatilization in ISM solutions, ensuring performance adaptability. A kinetic model developed through electrochemical numerical simulation confirms the optimal theoretical stability of an AIIS based on maximum transduction layer charge current and minimal diffusion current. The mixed capacitive transduction mechanism driven by the adsorption of TFPB − on the 2.0 CTA‐MoS 2 surface is elucidated. Adaptive integrated cadmium ion‐selective electrodes, as a case study, exhibit excellent interfacial stability (potential drift of 5.51 ± 0.32 µV h −1 for 24 h and sensitivity loss rate of 4.77% for 30 days) and selectivity. This study proposes a promising strategy for constructing extendable interface structures, providing valuable insights for advancing sensor chip development.