Efficient Interface Research Articles

Scalable Networking of Neutral-Atom Qubits: Nanofiber-Based Approach for Multiprocessor Fault-Tolerant Quantum Computers

Neutral atoms are among the leading platforms toward realizing fault-tolerant quantum computation (FTQC). However, scaling up a single neutral-atom device beyond 104 atoms to meet the demands of FTQC for practical applications remains a challenge. To overcome this challenge, we clarify the criteria and technological requirements for further scaling based on multiple neutral atom quantum processing units (QPUs) connected through photonic networking links. Our quantitative analysis shows that nanofiber optical cavities have the potential as an efficient atom-photon interface to enable fast entanglement generation between atoms in distinct neutral-atom modules, allowing multiple neutral-atom QPUs to operate cooperatively without sacrificing computational speed. Using state-of-the-art millimeter-scale nanofiber cavities with the finesse of thousands, over a hundred atoms can be coupled to the cavity mode with an optical tweezer array, with expected single-atom cooperativity exceeding 100 for telecom-band transition of ytterbium atoms. This enables efficient time-multiplexed entanglement generation with a predicted Bell-pair generation rate of 105s−1 while maintaining a small footprint for channel multiplexing. These proposals and results indicate a promising pathway for building large-scale multiprocessor fault-tolerant quantum computers using neutral atoms, nanofiber optical cavities, and fiber-optic networks. Published by the American Physical Society 2025

Metal coordination bond and rough interface enhanced triboelectric nanogenerator aiming for multiple complex conditions

Triboelectric nanogenerator (TENG) is widely used in the fields of sustainable green energy harvesting, self-powered motion parameter and tactile sensing, However, it still fails to meet the requirements under various complex conditions, such as low temperatures, self healing after destruction, punching, long-term placement, soaking in acid or alkali solution, scorch, continuous work. Herein, based on metal coordination, Zr4+ ions are introduced to enhance the first network k-carrageenan (k-CG) for achieving double enhancement in mechanics and electricity of the gel electrode layer, poly (N-hydroxyl acrylamide)/k-CG (PKZ) double network organic conductive gel enhanced by multiple hydrogen bonds and metal coordination bond is designed, and the gel exhibits high tensile strength, high conductivity, fast self-recovery, excellent self-repairing and low-temperature resistance. Based on simple sandpaper templates with different mesh numbers Ecoflex film with rough surfaces is designed for efficient triboelectric contact interface, and TENG with PKZ double network organic conductive gel as electrode layer is constructed, and possesses excellent resistant to multiple complex conditions. With high short-circuit current, open-circuit voltage and output power, the TENG is capable of powering electronic devices, and it can also be sensitive and stable sensing in writing recognition, real-time monitoring of motion parameters involving acceleration, speed and distance. The TENG is stable and reliable for sustainable green energy harvesting, motion parameter and tactile sensing in multiple complex environments. Thus, we provide novel ideas for designing energy harvesting and sensing for future wearable electronics under multiple complex conditions.

IPowder: advanced software for automated high-throughput X-ray diffraction analysis

In the burgeoning era of materials genomics, the need for fast and accurate X-ray diffraction (XRD) analysis has never been greater. To accommodate the rapid data generation of high-throughput XRD experiments, a new and powerful computer program, iPowder, has been developed. Not only does it have an efficient and straightforward graphical user interface but it also excels in high-throughput data analysis. The main function of the program is the rapid processing of datasets. This paper presents the convenient functionalities provided by iPowder for high-throughput XRD data analysis.

PyBioPortal: a Python package for simplifying cBioPortal data access in cancer research.

In recent years, the rise of big data and artificial intelligence has led to an increasing expansion of databases and web services in biomedical research. cBioPortal is one of the most widely used platforms for accessing cancer genomic and clinical data. The primary objective of this study was to develop a tool that simplifies programmatic interaction with cBioPortal's web service. We developed the pyBioPortal Python package, which leverages the cBioPortal REST API to access genomic and clinical data. The retrieved data is returned as a Pandas DataFrame, a format widely used for data analysis in Python. pyBioPortal offers an efficient interface between the user and the cBioPortal database. The data is provided in formats conducive to further analysis and visualization, promoting workflows and improving reproducibility. The development of pyBioPortal addresses the challenge of accessing and processing large volumes of biomedical data. By simplifying the interaction with the cBioPortal API and providing data in Pandas DataFrame format, pyBioPortal allows users to focus more on the analytical aspects rather than data extraction. This tool facilitates the retrieval of heterogeneous biological and clinical data in a standardized format, making it more accessible for analysis and enhancing the reproducibility of results in cancer informatics. Distributed as an open-source project, pyBioPortal is available to the broader bioinformatics community, promoting collaboration and advancing research in cancer genomics.

Three Birds with One Stone: Construction of Highly Efficient Interfaces via Ammonium Sulfamate Doping SnO2

Three Birds with One Stone: Construction of Highly Efficient Interfaces via Ammonium Sulfamate Doping SnO₂

Visual Impairment Spatial Awareness System for Indoor Navigation and Daily Activities.

The integration of artificial intelligence into daily life significantly enhances the autonomy and quality of life of visually impaired individuals. This paper introduces the Visual Impairment Spatial Awareness (VISA) system, designed to holistically assist visually impaired users in indoor activities through a structured, multi-level approach. At the foundational level, the system employs augmented reality (AR) markers for indoor positioning, neural networks for advanced object detection and tracking, and depth information for precise object localization. At the intermediate level, it integrates data from these technologies to aid in complex navigational tasks such as obstacle avoidance and pathfinding. The advanced level synthesizes these capabilities to enhance spatial awareness, enabling users to navigate complex environments and locate specific items. The VISA system exhibits an efficient human-machine interface (HMI), incorporating text-to-speech and speech-to-text technologies for natural and intuitive communication. Evaluations in simulated real-world environments demonstrate that the system allows users to interact naturally and with minimal effort. Our experimental results confirm that the VISA system efficiently assists visually impaired users in indoor navigation, object detection and localization, and label and text recognition, thereby significantly enhancing their spatial awareness and independence.

The chemistry of halide-based solid electrolytes: unlocking advances in solid-state Li-ion batteries.

Solid-state batteries (SSBs) represent a transformative advancement in electrochemical energy storage, offering exceptional energy density, enhanced safety, and broad operational temperature ranges, making them ideal for next-generation applications. While liquid electrolytes dominate conventional lithium-ion batteries (LIBs) due to their high conductivity and efficient electrode interface wetting, their flammability and volatility pose significant safety risks, particularly in electric vehicles and portable electronics. Solid electrolytes, a cornerstone of SSB technology, offer a promising pathway to enhance LIB energy density and safety. Among the various inorganic solid electrolytes, halide-based materials have garnered the main attention due to their remarkable ionic conductivity and robust mechanical properties. This review highlights recent advances in halide solid electrolyte design, synthesis, and electrochemical performance. Additionally, it discusses the challenges facing their integration into SSBs, an innovative technology poised to drive societal progress in sustainable energy solutions.

Integrated Optics Waveguides and Mesoporous Oxides for the Monitoring of Volatile Organic Compound Traces in the Mid-Infrared.

Volatile organic compounds (VOCs) are an ever-growing hazard for health and environment due to their increased emissions and accumulation in the air. Quantum cascade laser-based infrared (QCL-IR) sensors hold significant promise for gas monitoring, thanks to their compact, rugged design, high laser intensity, and high molecule-specific detection capabilities within the mid-infrared spectrum's fingerprint region. In this work, tunable external cavity QCLs were complemented by an innovative germanium-on-silicon integrated optics waveguide sensing platform with integrated microlenses for efficient backside optical interfacing for the tunable laser spectrometer. The waveguide chip was coated with a mesoporous silica coating, thereby increasing the signal by adsorptive enhancement of VOCs while at the same time limiting water vapor interferences. Different least square fitting methods were explored to deconvolute the resulting spectra, showing subparts-per-million by volume (sub-ppmv) limits of detection and enrichment factors of up to 22 000 while keeping the footprint of the setup small (29 × 23 × 11 cm³). Finally, a use-case simulation for the continuous detection of VOCs in a process analytical technology environment confirmed the high potential of the technique for the monitoring of contaminants. By successfully demonstrating the use of photonic waveguides for the monitoring of VOCs, this work offers a promising avenue for the further development of fully integrated sensors on a chip.

Biomimetic Honeycomb-like Ti3C2Tx MXene/Bacterial Cellulose Aerogel-Based Flexible Pressure Sensor for the Human-Computer Interface.

The pursuit of efficient and accurate human-computer interface design urgently requires high-performance sensors with pressure sensitivity, a wide detection range, and excellent cycling stability. Herein, a biomimetic honeycomb-like Ti3C2Tx MXene/bacterial cellulose (BC) aerogel with a negative Poisson's ratio (ν = -0.14) synthesized from the bidirectional freeze-drying method is used as the active material for a flexible pressure sensor, which exhibits high sensitivity (20.14 kPa-1), fast response time (100 ms), excellent mechanical durability (5000 cycles), and a low detection limit (responding to a grain of rice weighing about 0.022 g). Moreover, when assembled into the sandwich-structured bending sensor with the aerogel layer at just 0.8 mm in thickness, the aerogel-based device has a wide angular detection range (2.7-156.3°), high sensitivity (0.47 deg-1), and good robustness, proving outstanding electromechanical performance. Significantly, a smart glove consisting of five bending sensors fixed to the proximal knuckles and a flexible circuit board as the signal processing unit was designed for the identification of the shape, demonstrating its promising applications in the field of human-computer interaction.

Explainable fNIRS-based pain decoding under pharmacological conditions via deep transfer learning approach.

Assessment of pain and its clinical diagnosis rely on subjective methods which become even more complicated under analgesic drug administrations. We aim to propose a deep learning (DL)-based transfer learning (TL) methodology for objective classification of functional near-infrared spectroscopy (fNIRS)-derived cortical oxygenated hemoglobin responses to painful and non-painful stimuli presented under different timings post-analgesic and placebo drug administration. A publicly available fNIRS dataset obtained during painful/non-painful stimuli was used. Separate fNIRS scans were taken under the same protocol before drug (morphine and placebo) administration and at three different timings (30, 60, and 90min) post-administration. Data from pre-drug fNIRS scans were utilized for constructing a base DL model. Knowledge generated from the pre-drug model was transferred to six distinct post-drug conditions by following a TL approach. The DeepSHAP method was utilized to unveil the contribution weights of nine regions of interest for each of the pre-drug and post-drug decoding models. Accuracy, sensitivity, specificity, and area under curve (AUC) metrics of the pre-drug model were above 90%, whereas each of the post-drug models demonstrated a performance above 90% for the same metrics. Post-placebo models had higher decoding accuracy than post-morphine models. Knowledge obtained from a pre-drug base model could be successfully utilized to build pain decoding models for six distinct brain states that were scanned at three different timings after either analgesic or placebo drug administration. The contribution of different cortical regions to classification performance varied across the post-drug models. The proposed DL-based TL methodology may remove the necessity to build DL models for data collected at clinical or daily life conditions for which obtaining training data is not practical or building a new decoding model will have a computational cost. Unveiling the explanation power of different cortical regions may aid the design of more computationally efficient fNIRS-based brain-computer interface (BCI) system designs that target other application areas.

AI‐Driven Multi‐Modal Information Fusion System for Real Applications in Physical Training

ABSTRACTThis paper introduces an AI‐driven multi‐modal information fusion system tailored for real‐world physical training applications, integrating state‐of‐the‐art computer vision, deep learning, and multi‐sensor technologies. The system combines high‐precision cameras and inertial measurement unit (IMU) sensors to capture real‐time motion trajectories and posture data. These data are processed through convolutional neural networks (CNNs) and long short‐term memory (LSTM) models for accurate movement analysis. Additionally, augmented reality (AR) provides real‐time visual feedback, while deep reinforcement learning algorithms deliver personalized training recommendations. In an 8‐week study involving 100 high school students, the system improved movement accuracy by 23% and received a 4.6/5 user satisfaction rating. Although challenges remain in optimizing computational efficiency and user interface design, the system's modular and scalable nature makes it an adaptable solution for enhancing physical training across educational, home, and professional settings.

Mobile-Based Decision Support System for Recommending Tourism Attraction Using MAGIQ-ARAS Methodology

Tourism in Bali faces challenges such as overcrowding, poor management, and diverse visitor preferences, which hinder effective decision-making for tourist destinations. This study introduces an Android-based DSS using the MAGIQ-ARAS. The MAGIQ method simplifies the weighting process, while the ARAS method evaluates alternatives to produce utility-based rankings. Together, MAGIQ-ARAS provide a structured approach for generating tourist recommendations tailored to user preferences. The research follows a combination of the CRISP-DM framework for data preparation and the Waterfall Model for system development. Field studies, interviews, and literature reviews informed the design of a mobile application that integrates MAGIQ and ARAS. Black-box testing verified functionality, and accuracy testing using a confusion matrix evaluated the alignment between recommendations and user preferences. The results demonstrate the system's effectiveness, achieving 90% accuracy in matching recommendations with user preferences. Black-box testing confirmed that all features, including preference weighting and interactive navigation, operated as intended. The system simplifies MCDM and enhances user satisfaction through its efficient and user-friendly interface. The study concludes that the MAGIQ-ARAS-based mobile application offers a reliable solution for tourist recommendations. Future work should explore real-time data integration and expand application to other tourism regions to enhance adaptability and usability.

Characterization of Ultrathin Layers in Perovskite/TOPCon Tandem Cells With Photoelectron Spectroscopy Utilizing Advanced Data Evaluation Methods

Next generation solar cells like tunnel oxide passivated contacts (TOPCon) or perovskite/TOPCon tandem solar cells require efficient interface passivation by ultrathin organic or inorganic layers to uncover their true efficiency potential. Especially the microstructure and the Si suboxide content of the tunnel oxide in poly-Si(Ox)/SiO2/c-Si TOPCon stacks have been shown to have a tremendous influence on macroscopic device properties. Similarly, perovskite/ITO interface modification by a self-assembled monolayer (SAM) molecule is necessary to achieve high power conversion efficiencies of the perovskite sub cell. However, the characterization of such thin film structures and the interfacial composition is challenging. In this contribution, we present characterization results of ultrathin passivation layers using angle-resolved X-ray photoelectron spectroscopy (XPS) and advanced data evaluation routines, including maximum entropy methods and analyses of inelastically-scattered electron background data. In particular, the interfaces in the stacks (1) partially oxidized a-Si/SiO2/c-Si and (2) 2PACz/ITO after different thermal treatment were investigated. It could be shown that already a ~5 nm thin SiNx layer prevents unwanted oxidation from ambient during cooldown of TOPCon-like stacks and that annealing above 100 °C can convert a 2PACz multilayer to a 2PACz monolayer on an ITO substrate.

Highly efficient and salt resistant solar interface evaporator based on pressed wood flour with multi-level pores

Interfacial solar steam generation has garnered significant attention as an eco-friendly and sustainable technology for desalination and wastewater purification. Nevertheless, in challenging conditions such as strong brine and high-intensity settings, both the evaporation efficiency and the service life of the solar evaporator are inevitably diminished due to salt accumulation on the surface of interfacial solar evaporators. Here, a platelike evaporator that features high water evaporation rate in brine without salt accumulation is developed by rational design of polydopamine-coated wood powder with a hierarchical pore structure. This evaporator not only exhibits high water evaporation rate of 2.299 kg m−2 h−1 along with 155.4 % solar-to-steam conversion efficiency under 1 sun illumination, but also deliver exceptional salt accumulation resistance even in 20 wt% brine. With the advantages of a high light absorption rate, easy preparation process, and low cost, this evaporator shows promising potential in the field of solar steam generation and seawater desalination.

A new pile-soil interface and its application in battered mini piles under monotonic lateral load in cohesive soil

Numerical modelling of laterally loaded piles requires a robust pile-soil interface model. The conventional Coulomb friction model has limitations when predicting the soil-structure interaction at shallow depths for battered mini piles (BMPs) in cohesive (fine-grained) soils. This paper proposes an efficient pile-soil interface model to simulate laterally loaded BMPs in cohesive soils using three-dimensional finite element models (FEM). BMP systems have been commonly used to support lateral load-dominated lightweight superstructures. They are hybrid foundations with BMPs oriented at different inclinations and directions, mimicking tree root systems. FEM results indicate that the Coulomb model is unsuitable for simulating the pile-soil interface at shallow depth due to underprediction of shear resistance. The proposed interface model comprising a surface-to-surface cohesive damage interface with friction captures the lateral performance of BMPs accurately. The proposed model was implemented for a range of pile and soil properties to verify its suitability in understanding the behaviour of BMPs. The ultimate lateral capacity of BMPs increases with penetration length up to 1.5 m. While an increase in diameter and undrained shear strength increases the capacity, the lateral load eccentricity negatively impacts it. Interaction diagrams are developed to serve engineers estimate the ultimate lateral capacity of BMPs.

A bionic Janus membrane with spindle knots and ultra-high separation efficiency for various oil–water mixtures

Due to the prominent membrane interface effect micro-oil or micro-water droplets are easily fused with water or oil and are difficult to separate. The size of underwater/underoil droplets varies greatly, it needs a functional membrane for separating various oil–water mixtures. A novel bionic Janus membrane with a superhydrophobic spindle knots structure was prepared by a one-step dual-axis electrospinning technique. The Janus membrane relying on liquid unidirectional permeation achieves the separation of various oil–water mixtures including light/heavy oils-water, oil-in-water, and water-in-oil emulsions. Accordingly, it can be designed as an underoil diode water collector without any leakage. Importantly, the spindle knots absorb, transport and coalesce the micro-oil droplets into large oil droplets during separating oil-in-water emulsion, whereas it can repel water adhesion and allow oil spreading during separating water-in-oil emulsion. Thus, the spindle knots endow the ultra-high interface oil–water mixture, various emulsions separation efficiency (interface oil: >99.8 %, oil-in-water emulsion: 99.5 %, water-in-oil emulsion: 99.2 %), and excellent antifouling properties of Janus membranes for underwater oil and underoil water. This bionic Janus membrane presents a wide application in smart oil–water separation equipment, biomedical microreactors, and flotation filtration devices.

QHDOPT: A Software for Nonlinear Optimization with Quantum Hamiltonian Descent

We develop an open-source, end-to-end software (named QHDOPT), which can solve nonlinear optimization problems using the quantum Hamiltonian descent (QHD) algorithm. QHDOPT offers an accessible interface and automatically maps tasks to various supported quantum backends (i.e., quantum hardware machines). These features enable users, even those without prior knowledge or experience in quantum computing, to utilize the power of existing quantum devices for nonlinear and nonconvex optimization tasks. In its intermediate compilation layer, QHDOPT employs SimuQ, an efficient interface for Hamiltonian-oriented programming, to facilitate multiple algorithmic specifications and ensure compatible cross-hardware deployment. The detailed documentation of QHDOPT is available at https://github.com/jiaqileng/QHDOPT . History: Accepted by Giacomo Nannicini, Area Editor for Quantum Computing and Operations Research. Accepted for Special Issue. Funding: This work was supported by the U.S. Department of Energy’s Advanced Research Projects Agency–Energy [Grant DE-SC0020273], the Alfred P. Sloan Foundation, the Simons Foundation [Simons Investigator Award 825053], the Simons Quantum Postdoctoral Fellowship, the National Science Foundation [Grants CCF-1816695, CCF-1942837, and ECCS-2045978], the Unitary Fund, and the Air Force Office of Scientific Research [Grant FA95502110051]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0587 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0587 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Fullerene‐Hybridized Fused‐Ring Electron Acceptor with High Dielectric Constant and Isotropic Charge Transport for Organic Solar Cells

AbstractA novel isotropic fullerene‐hybridized fused‐ring electron acceptor, designated C60‐Y, has been synthesized via a mild [4+2] Diels–Alder cycloaddition reaction with fullerene C60 to enhance the performance of organic solar cells (OSCs). Comparative analysis shows that C60‐Y significantly outperforms the control acceptor Me−Y, with a notable increase in the relative dielectric constant from 2.79 to 3.95. This improvement enhances exciton dissociation and reduces non‐radiative energy losses. Additionally, the isotropic molecular packing of C60‐Y, similar to fullerene, facilitates efficient interface formation with donor polymers and improves charge mobility. As a result, incorporating C60‐Y as an electron acceptor increases the power conversion efficiency (PCE) of binary OSCs to 15.02 %, surpassing the 13.31 % achieved with Me−Y. Moreover, when integrated into a ternary blend system, an impressive PCE of 19.22 % is achieved, top‐performing among reported ternary OSCs utilizing fullerene derivatives as the third component. These results suggest that fullerene‐hybridized acceptors like C60‐Y hold great potential for advancing high‐efficiency OSCs by enhancing exciton dissociation, reducing energy losses, and improving charge mobility.

The PLSDB 2025 update: enhanced annotations and improved functionality for comprehensive plasmid research.

Plasmids are extrachromosomal DNA molecules in bacteria and archaea, playing critical roles in horizontal gene transfer, antibiotic resistance, and pathogenicity. Since its first release in 2018, our database on plasmids, PLSDB, has significantly grown and enhanced its content and scope. From 34513 records contained in the 2021 version, PLSDB now hosts 72360 entries. Designed to provide life scientists with convenient access to extensive plasmid data and to support computer scientists by offering curated datasets for artificial intelligence (AI) development, this latest update brings more comprehensive and accurate information for plasmid research, with interactive visualization options. We enriched PLSDB by refining the identification and classification of plasmid host ecosystems and host diseases. Additionally, we incorporated annotations for new functional structures, including protein-coding genes and biosynthetic gene clusters. Further, we enhanced existing annotations, such as antimicrobial resistance genes and mobility typing. To accommodate these improvements and to host the increase plasmid sets, the webserver architecture and underlying data structures of PLSDB have been re-reconstructed, resulting in decreased response times and enhanced visualization of features while ensuring that users have access to a more efficient and user-friendly interface. The latest release of PLSDB is freely accessible at https://www.ccb.uni-saarland.de/plsdb2025.

Constructing 3D Crosslinked Macromolecular Networks as a Highly Efficient Interface Layer for Ultra-Stable Zn Metal Anodes.

Aqueous zinc ion batteries (AZIBs) are experiencing rapid development due to their high theoretical capacity, abundant zinc resources, and intrinsic safety. However, the progress of AZIBs is hindered by uncontrollable parasitic reactions and excessive dendrite growth, which compromise the durability and effective utilization of zinc metal anodes. To address these challenges, the study has constructed a 3D crosslinked macromolecular network composed of zinc ion-bonded potato starch (StZ) as an interface layer on Zn foil (StZ-Zn) to inhibit hydrogen evolution, regulate Zn2+ flux, and ensure uniform Zn deposition. Density functional theory calculations, molecular dynamics simulations, COMSOL Multiphysics simulations, and in situ Raman spectra demonstrate that the 3D StZ interface layer facilitates Zn2+ desolvation by restructuring the solvation shells. This process reduces the concentration of H2O at the anode, thereby inhibiting the hydrogen evolution reaction. Consequently, Zn2+ transport is more efficient, promoting a homogeneous Zn2+ flux and enabling dendrite-free Zn deposition. As a result, StZ-Zn||StZ-Zn symmetric cell delivers a superb lifespan of 4800 h at the current density of 5 mA cm-2, and the corresponding cumulative capacity is as high as 12000 mAh cm-2. Notably, StZ-Zn||NaV3O8·1.5H2O full cell can stably operate for 2500 cycles at 5 A g-1 with an outstanding capacity retention of 92%.