Nanoscale Horizons最新文献

Memristors with programmable conductance are considered promising for energy-efficient analog memory and neuromorphic computing in edge AI systems. To improve memory density and computational efficiency, achieving multiple stable conductance states within a single device is particularly important. In this work, we demonstrate multilevel conductance tuning in few-layer tin hexathiophosphate (SnP₂S₆ or SPS) memristors, achieving 325 stable states through a pulse-based programming scheme. By analyzing conductive filament evolution, we devised a voltage-pulse approach that effectively suppresses current noise, thereby maximizing the number of distinguishable states within the device ON/OFF ratio. Furthermore, we experimentally emulated synaptic plasticity behaviors including long-term potentiation and depression, and validated their performance through artificial neural network simulations on digit classification. These results highlight the potential of SPS memristors as high-resolution analog memory and as building blocks for neuromorphic computing, offering a pathway toward compact and efficient architectures for next-generation edge intelligence.

Organic electrochemical synaptic transistors (OESTs) are attracting growing attention for neuromorphic computing, yet their long-term stability remains constrained by uncontrolled ion dynamics. Previous studies have incorporated glycol side chains to facilitate ionic transport, but a systematic understanding of how copolymerization with hydrophobic alkyl units governs ion doping and retention is still lacking. Here, we establish a rational backbone–side chain copolymer design strategy that precisely regulates ionic interactions, crystallinity, and charge transport. We also reveal clear correlations between copolymer structure, ion dedoping dynamics, and nonvolatile retention. These structural advantages enable the faithful emulation of key biological behaviors including paired-pulse facilitation, spike-timing dependent plasticity, and long-term potentiation/depression (LTP/D) with high linearity and stability. Based on these properties, the device achieved a high accuracy of 94.1% in ANN-based recognition simulations for MNIST handwritten digits. This work demonstrates that systematic glycol–alkyl copolymer engineering provides a robust and predictive design principle for high-performance neuromorphic synapses, moving beyond empirical side-chain modifications.

Our Emerging Investigator Series features exceptional work by early-career nanoscience and nanotechnology researchers. Read Verónica Mora Sanz's Emerging Investigator Series article ‘Dot-blot immunoassay based on antibody-nanocluster biohybrids as tags for naked-eye detection’ (https://doi.org/10.1039/D5NH00045A) and read more about her in the interview below.

DNA nanotechnology has transformed nucleic acids from simple genetic information carriers to programmable building blocks capable of manipulating matter at molecular-, nano-, and micro-scales. By harnessing Watson–Crick base pairing, researchers have created unprecedented architectures and devices, ranging from DNA origami and plasmonic nanoassemblies to molecular robots, computational science, biosensors, and therapeutic systems. This themed collection showcases state-of-the-art advances that exploit the sequence-encoded addressability of DNA to achieve precise structural control and functional integration, while revealing emerging opportunities across a wide range of disciplines.

This themed collection in the nanoscale family of journals (Nanoscale Horizons, Nanoscale and Nanoscale Advances) commemorates the 120th anniversary of the National University of Singapore (NUS). Founded in 1905 as a medical school, NUS has evolved into a world-leading university with a strong global presence. Over the past century, it has expanded far beyond its medical origins to become a comprehensive, research-intensive institution. Today, NUS is internationally recognized for its excellence in research, education, and innovation. Among its key strengths, nanoscience and nanotechnology stand out as a key area where the university continues to advance the frontiers of discovery, technological innovation, and real-world application.

Precise control over surface properties is crucial for the design of nanocarriers in biomedical applications. These properties influence biological interactions. Functional co-monomers can be used to tailor the surface chemistry of nanocarriers synthesized in radical heterophase polymerization in aqueous phase. However, achieving similar control over nanocarriers derived from natural materials in inverse miniemulsion, such as protein nanocapsules, remains challenging. Here, we demonstrate how the surface functional group density of protein nanocapsules can be tuned systematically by varying the hydrophobicity of the continuous phase during the synthesis via the click reaction between hydrophilic azide-modified proteins and a hydrophobic dialkyne crosslinker. By adjusting the solvent mixture of toluene and cyclohexane, the interfacial properties of the droplets are modified, influencing the partial denaturation of the protein and orientation of the amine-terminated lysine residues. This, in turn, affects the accessibility of the azide groups for the crosslinking. Changes in solvent composition furthermore influence the solubility and reactivity of the crosslinker, thereby modulating the degree of azide functionalization. This allows for precise control over the number of unreacted azide groups available for subsequent biorthogonal click reactions. We demonstrate that the multifunctional surface, with amine, azide and alkyne groups, enables the simultaneous attachment of different molecules to the nanocapsule. Finally, we show that while changes in continuous phase hydrophobicity lead only to minor changes in protein corona composition, they significantly affect macrophage uptake, likely due to differences in surface amine density. Our combined findings provide a novel approach for tailoring the surface functionality of nanocapsules, facilitating more precise and versatile biofunctionalization strategies, particularly for targeted drug delivery.

Amongst different desalination technologies to tackle freshwater demand, membrane distillation (MD) is promising in that it can effectively treat hypersaline feed or reverse osmosis reject and further improve freshwater recovery while simultaneously reducing the amount of liquid discharge. However, wetting of the membrane pores by surfactant compromises the separation efficiency since MD relies on maintaining a stable air gap in the membrane pore. The kinetics of surfactant-induced wetting for a hydrophobic membrane applied in MD technology have been shown to depend only on bulk surfactant concentration and vapour flux. In this study, we examine the decoupled effect of salt concentration and bulk surfactant concentration and its relation to surfactant-induced wetting. Even at low surfactant concentration (0.1 mM sodium dodecyl sulphate), the concentration of salt (sodium chloride) can significantly affect the wetting dynamics. In particular, high salt concentrations (above 1.2 M or 70 g L⁻¹ NaCl) can notably accelerate wetting, and thereby render MD unsuitable for such feeds. On the other hand, surfactant concentrations well above the critical micelle concentration (CMC) are tested with low salt concentration, and the results reveal that hydrophobic PVDF membranes perform quite stably without any significant loss in salt removal efficiency. A mathematical framework that captures ionic strength and surfactant activity is also proposed to predict different membrane wetting regimes. These findings point to the need for coupling bulk surfactant concentration with salt concentration to predict surfactant-induced wetting more accurately. These results also open an avenue for an alternative mechanism that complements the existing understanding of surfactant-induced wetting.

MoSe₂ and MoTe₂ based heterostructures, owing to their remarkable photoresponsivity and tunable electrical characteristics, have emerged as promising candidates for field-effect transistors (FETs) and near-infrared (NIR) optoelectronic applications. However, the contributions of different interfacial processes impose limitations on the band tunability and carrier dynamics of the heterostructure, posing challenges in their device engineering. In this work, we present the scalable, layer-by-layer growth of a trilayer MoSe₂/MoTe₂ heterostructure over a SiO₂ substrate via molecular beam epitaxy (MBE). By leveraging the tunable probing depth of AR-XPS, we successfully resolve the interfacial bonding modifications, such as Te migration across the interface and localized Mo–Se–Te bonding. Our investigations show that these site-specific processes at the interface induce asymmetric energy level shifts, Fermi level pinning, and modulation of the valence band edge. Consequently, deviations from predicted band alignment are observed, with the Fermi level pinned around 0.58 eV above the valence band edge on the MoTe₂ side and the anomalous upshift of the valence band maximum of MoSe₂ in the heterostructure. These interfacial effects also result in a reduced barrier for hole injection, which can improve bidirectional carrier transport and gate-tunable hole conduction in such heterostructure-based devices. The findings highlight the critical role of interfacial interactions in governing band alignment of the ultrathin transition metal dichalcogenide (TMDC) heterostructures, providing key insights for advancing nanoelectronic and optoelectronic devices through heterostructure band engineering.

Every decision made during a machine learning pipeline has an impact on the outcome. Feature selection can reduce overfitting and focus models on the attributes that matter most, and sample selection can reduce bias to ensure models recognise patterns comprehensively. eXplainable AI (XAI) can provide quantitative ways of evaluating the impact of these decisions, and help ensure the right data is used for training models predicting structure property relationships. In this paper we explore the use of residual decomposition with Shapely values to identify which nanoparticle shapes are most influential in predicting charge transfer properties of gold nanoparticles and how they impact the ability to predict the properties of the different morphologies.

Advanced profiling of multiple biomarkers can individualize patient characterization and empower precision medicine. Conventional diagnostic methods, however, often require extensive processing and lack assay versatility and/or multiplexing capacity to accommodate different biomarkers. To address these challenges, nucleic acid–protein hybrid nanostructures have emerged as a promising technology. These hybrids offer multifaceted versatility. On the component level, they benefit from the inherent structural programmability of nucleic acids and the functional versatility of proteins to accommodate diverse biomarkers; as integrated assemblies, they can operate as passive labeling constructs or active enzymatic machines to meet varying diagnostic needs. In this review, we highlight recent synergistic advances in the molecular configuration and mechanism design of these hybrid systems to measure a broad spectrum of biomarkers, ranging from classical nucleic acid and protein biomarkers to novel modifications and interactions. Finally, we provide an outlook on emerging trends in biomarker discovery and technology development that position nucleic acid–protein hybrids as powerful tools for precision diagnostics.