Graphene oxide (GO) has established itself as a premier material for electrochemical biosensing due to its exceptional chemical tunability, aqueous processability, and unique sp²-sp³ hybridized structure. This review provides a comprehensive analysis of diverse engineering strategies to functionalize GO, enabling highly sensitive and selective detection of a broad spectrum of biological analytes. We systematically categorize these advancements into five key methodologies: (1) controlled reduction to precisely tune electrical conductivity and surface defects, (2) covalent functionalization for robust bioreceptor immobilization, (3) non-covalent modification to preserve biomolecular conformation, (4) metal nanoparticle hybridization for enhanced electrocatalysis, and (5) integration with polymeric/framework materials to build advanced three-dimensional sensing architectures. By examining applications ranging from small molecule metabolites and proteins to nucleic acids and whole pathogens, we demonstrate how tailored GO interfaces overcome conventional sensing trade-offs. Finally, we highlight the pivotal role of these engineered GO platforms in addressing the challenges of real-time monitoring at complex biological interfaces, including living cells and organoids, and outline the pathway toward clinically deployable diagnostic technologies.

Hard carbon is being actively explored as a candidate anode material for next-generation batteries, offering ion storage mechanisms distinct from and potentially advantageous to those of graphite. However, conventional synthesis of hard carbon usually relies on high-temperature pyrolysis and chemical activation, which involve high energy consumption and environmental challenges. Spent coffee grounds (SCGs), generated in large amounts worldwide, represent an abundant biomass resource with high carbon content that is often discarded with limited recycling. In this study, SCGs were directly converted into hard carbon and partially graphitized structures using femtosecond laser direct writing (FsLDW) under solvent-free and non-vacuum conditions. Localized photothermal reactions induced by the ultrashort pulses promoted particle consolidation and structural rearrangement, and by varying the laser parameters, the carbonization pathway could be directed to favor either hard carbon or graphene domains. A copper substrate was deliberately employed to spontaneously generate copper compound nanoparticles, which were subsequently etched to create micro-porous carbon with tunable pore characteristics. This laser-based approach provides a controllable and one-step pathway for transforming SCGs into functional carbon anodes, highlighting the potential to selectively prepare materials suitable for both lithium-ion and sodium-ion batteries by adjusting only the laser conditions. While this study focused on lithium-ion battery applications, the tunable control of carbon structure and porosity offers the possibility of extending this strategy to sodium-ion batteries.

As all-solid-state battery (ASSB) technologies continue to advance, interest has resurfaced in mid-nickel (mid-Ni) LiNixCoyMnzO2 (NCM; x = 0.5) cathodes due to their enhanced structural stability, reduced oxygen evolution, and higher capacities at elevated cutoff voltages compared to high-nickel compositions. However, interfacial degradation including parasitic reactions with solid-state electrolytes (SSEs) remains a major challenge. To address this issue, we conducted a high-throughput computational screening of oxide-based coating materials, evaluating their electrochemical stability, interfacial robustness, and Li-ion conductivity using Li–Li network descriptors. From this screening, 8 candidates were selected based on strict criteria. Among them, Li3Sc2(PO4)3 emerged as a particularly promising coating material, exhibiting strong electrochemical stability under high-voltage conditions (> 4 V) and substantial ionic conductivity (0.2 mS/cm), exceeding that of most oxide-type SSEs, as confirmed by ab initio molecular dynamics simulations. Furthermore, large-scale molecular dynamics simulations using a universal machine-learning interatomic potential demonstrate its ability to suppress surface degradation of mid-Ni NCM and prevent [PS4]3− decomposition in Li6PS5Cl, confirming its potential as a protective coating. These findings highlight the effectiveness of our computational screening strategy for coating-material discovery and underscore the potential of Li3Sc2(PO4)3 as a robust interfacial layer for stabilizing mid-Ni ASSBs.Graphical abstractSupplementary InformationThe online version contains supplementary material available at 10.1186/s40580-026-00555-z.

Antimony chalcogenides are highly promising thin-film photovoltaic materials. However, their quasi-one-dimensional structure inherently causes severe transport anisotropy. The thermodynamically stable [hk0] horizontal orientation induces van der Waals barriers that hinder carrier transport, whereas the kinetically favorable [hk1] vertical orientation constructs efficient charge pathways and dangling-bond-free “benign grain boundaries”. Focusing on the thermodynamic and kinetic competition mechanisms during film growth, this review systematically summarizes recent optimization strategies for inducing the [hk1] preferred orientation. Four core approaches are highlighted: solvent and precursor engineering, deposition parameter optimization, interface and substrate engineering, and post-treatment reconstruction. Finally, we delineate the “structure-process-performance” relationship and provide perspectives on deep-level defect passivation, heterojunction band engineering, and flexible, large-area applications, aiming to guide the fabrication of high-efficiency antimony-based solar cells approaching their theoretical limit.Graphical abstractFocusing on the thermodynamic and kinetic competition mechanisms during film growth, this review systematically summarizes four core optimization strategies for inducing the [hk1] preferred orientation, aiming to guide the fabrication of high-efficiency antimony-based solar cells.

Quantum dots (QDs) are fluorescent nanoparticles widely used for single-molecule imaging because of their exceptional brightness and photostability. However, the impact of QD surface chemistry on biomolecular interactions has not been systematically investigated. Here, we report that commercial QDs unexpectedly destabilize protein-DNA complexes by inducing protein dissociation from DNA. Using the human nucleotide excision repair protein, xeroderma pigmentosum complementation group A (XPA) as a model system, we demonstrate that antibody-conjugated QDs promote dissociation of XPA from DNA substrates, independently of sizes and surface modification of QDs, antibody types, epitope tags, buffer conditions, or DNA structures. We find that polyethylene glycol (PEG), a common polymer coating on QD surfaces, is the primary factor responsible for this effect. To tackle this problem, we engineered QDs with precisely controlled surface polymer compositions. By systematically changing the ratio of anchoring, hydrophilic, and PEG-based functional groups, we find that reducing PEG density below a critical threshold effectively suppresses protein dissociation while maintaining excellent colloidal stability and brightness. Furthermore, antibodies conjugated via click chemistry between azide groups and DBCO enabled specific labeling of XPA without perturbing the DNA binding activity. Using these optimized QDs, we conducted single-molecule DNA curtain assays to visualize XPA-DNA interactions. QD-labeled XPA exhibits one-dimensional diffusion with frequent pausing on undamaged DNA. DNA curtain assays revealed that XPA preferentially binds DNA bubbles and searches for bubble structures through both one-dimensional diffusion and three-dimensional collision. Quantitative analysis showed that three-dimensional collision is the dominant pathway for bubble recognition. Taken together, our results uncover a previously unrecognized limitation of PEG-coated QDs in single-molecule studies and provide an improved surface-engineering strategy to preserve native protein-DNA interactions. Newly engineered QDs establish robust platforms for accurate single-molecule visualization of biomolecular processes.Graphical abstractSupplementary InformationThe online version contains supplementary material available at 10.1186/s40580-026-00556-y.

The female reproductive system, including the endometrium, placenta, ovary, cervix, and fallopian tube, plays a critical role in conception, implantation, and fetal development. Recent advances in bioengineered models such as organoids, organ-on-a-chip platforms, and 3D bioprinting have expanded experimental capabilities, however, the rapid growth of this field has resulted in a large and fragmented body of literature, limiting systematic integration and analysis. Here, we present an artificial intelligence (AI)-driven text mining framework to systematically map research trends in the female reproductive system. A total of 347 peer-reviewed articles were collected and analyzed. Abstracts were embedded using BioBERT to capture contextual biomedical semantics. Subsequently, unsupervised topic modeling was performed using BERTopic with UMAP-based dimensionality reduction and HDBSCAN clustering. This analysis identified 15 fine-grained subtopics, which were further consolidated into six major thematic categories. The results show that current research is mainly focused on endometrial receptivity and implantation, placental barrier function and maternal-fetal interface, and tissue regeneration and biofabrication. In contrast, integrated multi-organ modeling and translational validation remain relatively underexplored. Overall, this AI-driven framework provides a quantitative and scalable approach to organizing complex biomedical literature. The findings offer a structured overview of the field and highlight emerging directions for multiscale modeling and personalized reproductive medicine.

Designing and developing innovative, cost-effective nanomaterials with outstanding activity and durability remains a significant challenge for next-generation electrochemical sensors. Herein, we developed an energy-efficient approach to synthesize N/Se-functionalized Co-ZIF-9(III) (CZ@N/Se) nanohybrids with a unique nanorod/nanosheet morphology, which serve as a high-performance platform for ultrasensitive nitrofurantoin (NFT) sensing. The unique nanorod/nanosheet morphology of N/Se-doped CZ provides an excellent platform for NFT sensing by enhancing electron-transfer kinetics and generating a high density of active sites through the synergistic interaction between the Co-framework and N/Se dopants. These features facilitate efficient NFT adsorption and catalytic reduction, thereby markedly improving the electrochemical sensitivity and selectivity. With optimized mass loading, the CZ@N/Se nanohybrid exhibits exceptional repeatability and reproducibility in NFT detection, achieving a low detection limit of 1.03nM and a high sensitivity of 3.783 µA µM-1cm-2 as confirmed by multiple electrochemical measurements. Selectivity tests against potential interferents confirmed the sensor's excellent specificity, with negligible interference observed. Besides, the analysis of spiked environmental and biological samples demonstrated accurate detection, validating the sensor reliability and practical applicability for NFT determination in real-world settings.

Neuroprostheses have become a pivotal technology for restoring sensory, motor, and cognitive functions, offering transformative therapeutic strategies for neurological disorders by bridging or bypassing damaged neural pathways through electronic systems. However, achieving long-term stability and high-fidelity interaction between biological and electronic systems remains a significant challenge due to the mismatch at the neural interface. This review examines the critical role of nanotechnology in building high performance neuroprostheses across six key classes: motor, visual, tactile, language, memory and olfactory. A system architecture of the neuroprostheses is proposed that highlights two critical interfaces, namely, “neural-electronic” and “environment-electronic” interfaces. We survey recent advances in materials and devices that shape better neural electrodes and novel sensors, and discuss the potential utilization of neuromorphic computing for efficient edge processing in neuroprostheses. This review aims to outline future trajectories toward high-throughput bidirectional interaction, biomimetic encoding, and adaptive closed-loop systems, aspiring to achieve seamless integration between electronic systems and biological neural circuitry.Graphical abstract

Synthetic graphites have been widely used in industrial applications, including as anodes in lithium-ion batteries. Because they are produced at temperatures above 3000 °C, which generate highly ordered graphitic domains, there is typically no discernible evidence of their precursor materials. In this study, three types of graphite, coal tar based anisotropic graphite, petroleum fluid oil based anisotropic graphite, and coal tar based isotropic graphite, were prepared. Conventional characterization techniques such as X-ray diffraction, Raman spectroscopy, transmission electron microscopy, and even electrochemical performance were unable to distinguish their precursors. Therefore, we introduced laser desorption/ionization time-of-flight mass spectrometry (LDI-MS) combined with multivariate statistical analysis to characterize three graphites prepared from different synthetic precursors as well as two commercial graphites. The resulting LDI-MS spectra were analyzed using principal component, hierarchical cluster, and heatmap analyses, which are widely used in clinical mass spectrometric diagnostics. Notably, LDI-MS coupled with multivariate statistics successfully classified the graphites depending on their precursor materials and processing parameters, such as heat-treatment temperature, whereas conventional analytical tools failed to reveal these differences. These results clearly demonstrate the strong potential of LDI-MS and statistical analysis for the precise characterization of carbon materials and for distinguishing their origins and processing routes.Graphical abstractSupplementary InformationThe online version contains supplementary material available at 10.1186/s40580-026-00551-3.

Metal-gate interlayer (G.IL)-ferroelectric (FE)-channel interlayer (Ch.IL)-Si (MIFIS) ferroelectric field-effect transistors (FeFETs) are attractive for large memory window (MW) and low-voltage FE NAND operation. Nevertheless, its fundamental operating principle also makes the device vulnerable to threshold voltage (Vth) shift under repeated disturb bias, which remains a major obstacle to array-level reliability. In this study, we employ a TiO2 nanolayer (NL) at the upper interface of the HZO FE layer to address this issue while preserving the low-voltage advantage of the MIFIS structure. The inserted TiO2 modifies the interfacial electrostatics and the ferroelectric switching characteristics at the same time. First, owing to its high dielectric constant and band alignment, it facilitates additional gate-side charge storage near the G.IL/FE interface. Second, it alters the switching nature of the underlying HZO toward a more abrupt response associated with enlarged effective domain size and improved remanent polarization. The proposed device with TiO2 NL operates below 15 V, while maintaining a large MW of 7.57 V, which is 18.9% higher than the reference device. Notably, the proposed device remains disturbance-free even after 105 cycles of 9 V/10 µs disturbance stress, whereas the counterpart experiences severe disturbance under the same conditions. Thus, we clarify that partial P switching acts as the primary driver of disturbances, as it precedes charge trapping and accelerates gate charge injection. Finally, while our top-interface engineering successfully optimizes gate-side dynamics, we propose that replacing the Si channel and bottom interlayer with emerging van der Waals (vdW) semiconductors and 2D insulators (e.g., h-BN) can fundamentally suppress channel-side charge injection (Qit). Combining this vdW-based bottom-interface with our TiO2 top-interface strategy presents a comprehensive blueprint to expand the MW and realize ultimate disturbance-free operation in next-generation computing architectures.Graphic abstract