Localized Gradient Conductivity Enabled Ultrasensitive Flexible Tactile Sensors with Ultrawide Linearity Range.

The high sensitivity and wide linearity are crucial for flexible tactile sensors in adapting to diverse application scenarios with high accuracy and reliability. However, conventional optimization strategies of constructing microstructures suffer from the mutual restriction between the high sensitivity and wide linearity. Herein, a novel design of localized gradient conductivity (LGC) with partly covered low-conductivity (low-σ) carbon/Polydimethylsiloxane layer on high-conductivity (high-σ) silver nanowires film upon the micro-dome structure is proposed.

A solid-state battery capable of 180 C superfast charging and 100% energy retention at -30 °C.

Solid-state electrolytes (SSEs) are being extensively researched as replacements for liquid electrolytes in future batteries. Despite significant advancements, there are still challenges in using SSEs, particularly in extreme conditions. This study presents a hydrated metal-organic ionic cocrystal (HMIC) solid-state ion conductor with a solvent-assisted ion transport mechanism suitable for extreme operating conditions.

Collective fluctuations in the finite-size Kuramoto model below the critical coupling: Shot-noise approach.

The Kuramoto model, a paradigmatic framework for studying synchronization, exhibits a transition to collective oscillations only above a critical coupling strength in the thermodynamic limit. However, real-world systems are finite, and their dynamics can deviate significantly from mean-field predictions. Here, we investigate finite-size effects in the Kuramoto model below the critical coupling, where the theory in the thermodynamic limit predicts complete asynchrony.

Molecular Hybrid Bridging for Efficient and Stable Inverted Perovskite Solar Cells without a Pre-Deposited Hole Transporting Layer.

Establishing a low-resistance perovskite/ITO contact using self-assembled molecules (SAMs) is crucial for efficient hole transport in perovskite solar cells (PSCs) without a pre-deposited hole-transporting layer. However, SAMs at the buried interface often encounter issues like nonuniform distribution and molecular aggregation during the extrusion process, leading to significant energy loss. Herein, a molecular hybrid bridging strategy by incorporating a novel small molecule is proposed, (2-aminothiazole-4-yl)acetic acid (ATAA), featuring a thiazole ring and carboxylic acid group, along with the commonly used SAM, 4-(2,7-dibromo-9,9-dimethylacridin-10(9H)-yl)butyl)phosphonic acid (DMAcPA), into the perovskite precursor to synergistically optimize the buried interface.

Replacing Doping in Nano-Silicon: Ultimate Miniaturization, Energy Efficiency, and Cryo-Functionality.

Hard entropy limits of impurity doping prevent further miniaturization of low nanoscale silicon-based very large scale integration (VLSI) devices, thereby obstructing the path toward more energy-efficient VLSI designs with higher yield in compute power. As demonstrated here by synchrotron UV photoelectron spectroscopy (UPS) and X-ray absorption spectroscopy in total fluorescence yield mode (XAS-TFY), intrinsic Si at the bottom of the nanoscale (i-nano-Si) turns into strong p- or n-Si by embedding in silicon nitride (SiN) or silicon dioxide (SiO), respectively. The associated Nanoscale Electronic Structure Shift Induced by Anions at Surfaces (NESSIAS) creates a p/n junction in i-nano-Si by the quantum-chemical impact of SiN- vs SiO-coating, providing energy landscapes to accumulate electrons (holes) when SiO- (SiN-) coated, with free charge carriers provided by metallic interconnects.

Engineering peptide-catecholamine co-assembled nanostructures for tunable fluorescence.

Precise engineering of hydrophobic microenvironments in synthetic peptide-catecholamine co-assemblies remains challenging for tunable fluorescence. Hierarchical nanostructures were constructed through sequence-specific peptide encoding (GYK tripeptide and Ac-IIIGYK-NH₂ hexapeptide) and co-assembly with catecholamines of graded hydrophobicity. Structural dynamics were analyzed via molecular simulations, HPLC, AFM, and spectroscopy.

Cell membrane cholesterol affects serotonin transporter efflux due to altered transporter oligomerization.

The human monoamine transporters (MATs) for serotonin (SERT), dopamine (DAT), and norepinephrine (NET) play a key role in neurotransmission by transporting neurotransmitters from the synaptic cleft back into the neuron. MATs are embedded in the cell membrane's lipid bilayer, encompassing cholesterol, phospholipids, and sphingolipids as main components. Membrane cholesterol association has been shown for all MATs impacting transporter conformation, substrate affinity, transport velocity, and turnover rates.

Cryogen-free variable-temperature Kelvin probe force microscopy for probing local chemical potential in van der Waals heterostructures.

We report the development of a variable-temperature Kelvin probe force microscopy (KPFM) system based on a Gifford-McMahon cryocooler, which enables stable and highly sensitive operation across a broad temperature range. The system integrates a custom-designed phase-locked loop, automatic gain control, and compact passive vibration isolation stages, effectively suppressing mechanical vibrations intrinsic to cryostats. We demonstrate the system's performance using a monolayer graphene (MLG) device encapsulated in hexagonal boron nitride, serving as a benchmark platform to validate spatial resolution and CPD sensitivity.

Thermal conductivity of individual single-crystalline FCuPc nanoribbons.

Organic semiconductors are widely used in flexible electronics, optoelectronic devices, and thermoelectric systems. Among them, copper hexadecafluorophthalocyanine (FCuPc), an n-type organic semiconductor, exhibits excellent chemical and thermal stability, making it suitable for a range of device applications. As device architectures scale down to the nanoscale, understanding the intrinsic thermal transport properties of such materials becomes critical for effective thermal management.

Electrochemical Conversion of Methane to C Hydrocarbons and Hydrogen.

Catalytic methane (CH) upgrading to C products has attracted widespread interest. However, non-oxidative coupling and oxidative coupling of CH are limited by coking and over-oxidation, respectively. In this work, we propose an efficient and stable CH conversion system in a solid oxide steam electrolyzer, enabling the simultaneous production of C hydrocarbons and H.

Room temperature operated short-wave infrared phototransistor optimized via MXene-based metamaterial absorber structure.

Recent advances in nanostructured photodetectors have enabled precise control over light absorption while minimizing photon losses. In this work, we demonstrate a plasmonic metamaterial absorber based on two-dimensional MXene (Ti₃C₂Tₓ) featuring geometrically tunable tetragram-shaped arrays. Through finite-difference time-domain (FDTD) simulations and structural optimization, we achieved over 90% photon absorption across the broadband spectral range of 1000-2500 nm, representing a significant enhancement in operational bandwidth.

Design and modeling of eye-shaped double-slot GaAs microring resonator for CO sensing.

Monitoring of CO is crucial because of its profound impact on both environmental and human health. A novel highly sensitive refractive index (RI) sensor, utilizing a double-slot microring resonating structure, has been designed and numerically assessed for the sensitive detection of gas media. The structure consisted of a circular microring resonator nested in a racetrack resonating configuration mimicking the structure of an eye-shaped microring resonator (ESMRR).

A Deep Learning-Augmented Density Functional Framework for Reaction Modeling with Chemical Accuracy.

Accurate prediction of reaction energetics remains a fundamental challenge in computational chemistry, as conventional density functional theory (DFT) often fails to reconcile high accuracy with computational efficiency. Here, we introduce Deep post-Hartree-Fock (DeePHF), a machine learning framework that integrates neural networks with quantum mechanical descriptors to achieve CCSD-(T)-level precision while retaining the efficiency of DFT to solve the reaction problems. By establishing a direct mapping between the eigenvalues of local density matrices and high-level correlation energies, DeePHF circumvents the traditional accuracy-scalability tradeoff.

Breaking the Conversion Limit in an Intercalation-Type Cathode by Loosening Aqueous Cation Coordination.

Intercalation-type cathodes continue to dominate aqueous multivalent ion storage, despite higher theoretical capacities being available from conversion reactions involving multiple electron transfer. Pushing intercalation-type cathodes into conversion is certainly desirable, conventionally with great sacrificing cycling-stability, kinetics, and discharge potential. To date, limited progress has been achieved in overcoming this longstanding and formidable performance trade-off.

A Refractive Index Sensor Based on Spectral Interrogation of a Long Tapered Side-Hole Optical Fiber.

This article describes an external refractive index (RI) sensor based on a spectral analysis of the light transmission through a long tapered side-hole optical fiber (S-H OF). A section of the S-H OF was fusion-spliced with SMFs at both ends and connected to a supercontinuum source at the input and an optical spectrum analyzer (OSA) at the output. To investigate the effect of the external RI on the spectral characteristics, immersion liquids with refractive indices in the ranges of 1.

Order Lot Sizing: Insights from Lattice Gas-Type Model.

In this study, we introduce a novel interdisciplinary framework that applies concepts from statistical physics, specifically lattice-gas models, to the classical order lot-sizing problem in supply chain management. Traditional methods often rely on heuristic or deterministic approaches, which may fail to capture the inherently probabilistic and dynamic nature of decision-making across multiple periods. Drawing on structural parallels between inventory decisions and adsorption phenomena in physical systems, we constructed a mapping that represented order placements as particles on a lattice, governed by an energy function analogous to thermodynamic potentials.

Concentrated KOH-activated 3D-printed martensitic steel for high-performance alkaline water electrolysis.

Alkaline water electrolysis (AWE) is a promising green hydrogen production technology, yet it is hindered by high-cost noble metal catalysts and poor low-cost alternatives. This study shows that 3D-printed martensitic steel, particularly a Ni11.0-Co13.

Wafer-Scale Growth of Monolayer MoSe via Salt-Assisted Chemical Vapor Deposition.

2D transition metal dichalcogenide (TMDs) of monolayer molybdenum diselenide (MoSe) is an emerging semiconductor for next-generation electronics, owing to its remarkable physical and electronic properties. The realization of diverse device applications depends critically on the scalable synthesis of high-quality monolayer MoSe crystals, which remains challenging. In this study, the successful epitaxy of monolayer MoSe films is demonstrated on sapphire substrates at a maximum wafer size of 2 inches via a salt-assisted chemical vapor deposition (SA-CVD) technique.

Mg-Incorporated Nickel Oxide Hole Injection Layer for Stable and Efficient Quantum Dot Light-Emitting Diodes.

High-quality hole injection layers (HILs) are essential for efficient and stable quantum dot light-emitting diodes (QLEDs). While NiO is a stable alternative to the poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) HIL, its low hole injection limits its practical application. This work enhances NiO hole injection efficiency by combining Mg alloying to deepen work function (5.

Direct Glass-to-Metal Welding by Femtosecond Laser Pulse Bursts: II, Enhancing the Weld Between Glass and Polished Metal Surfaces.

We present a comprehensive study on the femtosecond laser direct welding of glass and metal, focusing on optimizing processing parameters and understanding the influence of material properties and beam shaping on welding quality. Using microscopy, we identified optimal pulse energy, focal position, and line-spacing for achieving high-quality welds. We further investigated the effects of laser beam shaping and material property differences in various glass-to-metal pairs, including borosilicate, fused silica, and Zerodur glasses welded with mirror-polished metals such as Cu, Mo, Al, Ti, and AISI316 steel.

Immobilized Azole Layer Tunes Interfacial Hydrogen Source for CO Electroreduction in Strong Acid.

Achieving selective electrochemical CO reduction reaction (CORR) in strong acid holds potential to resolve the "carbonate formation" problem yet is hindered by the competing hydrogen evolution reaction (HER). The interplay between different hydrogen sources (i.e.

Ab Initio Accuracy Neural Network Potential for Drug-Like Molecules.

The advent of machine learning (ML) in computational chemistry heralds a transformative approach to one of the quintessential challenges in computer-aided drug design (CADD): the accurate and cost-effective calculation of atomic interactions. By leveraging a neural network (NN) potential, we address this balance and push the boundaries of the NN potential's representational capacity. Our work details the development of a robust general-purpose NN potential, architected on the framework of DPA-2, a deep learning potential with attention, which demonstrates remarkable fidelity in replicating the interatomic potential energy surface for drug-like molecules comprising 8 critical chemical elements: H, C, N, O, F, S, Cl, and P.

Method for Mid-IR Spectroscopy of Extracellular Vesicles at the Subvesicle Level.

Extracellular vesicles (EVs) are nanosized particles that are associated with various physiological and pathological functions. They play a key role in intercell communication and are used as transport vehicles for various cell components. In human milk, EVs are believed to be important for the development of acquired immunity.

Roadmap for Photonics with 2D Materials.

Triggered by advances in atomic-layer exfoliation and growth techniques, along with the identification of a wide range of extraordinary physical properties in self-standing films consisting of one or a few atomic layers, two-dimensional (2D) materials such as graphene, transition metal dichalcogenides (TMDs), and other van der Waals (vdW) crystals now constitute a broad research field expanding in multiple directions through the combination of layer stacking and twisting, nanofabrication, surface-science methods, and integration into nanostructured environments. Photonics encompasses a multidisciplinary subset of those directions, where 2D materials contribute remarkable nonlinearities, long-lived and ultraconfined polaritons, strong excitons, topological and chiral effects, susceptibility to external stimuli, accessibility, robustness, and a completely new range of photonic materials based on layer stacking, gating, and the formation of moiré patterns. These properties are being leveraged to develop applications in electro-optical modulation, light emission and detection, imaging and metasurfaces, integrated optics, sensing, and quantum physics across a broad spectral range extending from the far-infrared to the ultraviolet, as well as enabling hybridization with spin and momentum textures of electronic band structures and magnetic degrees of freedom.