Abstract Neuromorphic computing has attracted considerable attention as an efficient approach to overcoming the limitations of conventional von Neumann architectures. For the practical hardware implementation of artificial neural networks (ANNs), device operation should extend beyond individual cells to array-level architectures, typically realized as crossbar arrays in which synaptic weights are stored as the conductance states of individual cells and directly utilized for matrix–vector multiplication (MVM). In this context, crossbar arrays serve not only as high-density integration platforms but also as core computing architectures in which stored conductance distributions are directly utilized for MVM. However, conventional electrically modulated arrays often suffer from unintended disturbances through shared interconnects during programming and erasing, thereby limiting computational reliability. As a promising alternative, optically modulated systems enable selective and parallel weight control in a non-contact manner. Realizing these advantages in practical computing, however, requires optical learning to be implemented at the array level. In this Perspective, we review optically programmable crossbar arrays from the perspective of array-level ANN implementation. We first discuss the key mechanisms governing optical programming and erasing, and then examine the major requirements for array-level optical learning, including update uniformity, reliability, and efficiency. Finally, we highlight current progress, remaining challenges, and future perspectives toward the realization of optically programmable crossbar array-based ANN hardware.
Abstract The roadmap focusses on the current state of the art of three key reactions for sustainable energy transformation, water electrolysis for hydrogen production, carbon dioxide reduction for electrofuel production, and nitrogen reduction for ammonia production. Each technique is covered in separate chapters as detailed in the outline below. Each chapter is meant to update the reader in a short and concise way about the current state-of the art, the most significant challenges, and the trends and ideas lying ahead as solutions. We foresee a road map paper which will be of high interest for readers involved In current green technologies.
Abstract Plasmonic nanostructures manipulate light at dimensions much smaller than wavelength, leading to strong electromagnetic field confinement, enhanced light absorption and efficient photocarrier generation. This makes them promising components for future solar energy conversion systems. This Roadmap surveys recent advances and future directions in the application of plasmonic principles to solar energy technologies across nine topical areas. Key themes include the plasmonic enhancement of light harvesting in perovskite solar cells; thermoplasmonic conversion of solar photons into localized heat for chemical transformation; and plasmonic photocatalysis for selective CO₂ reduction and hydrogen evolution. The collection also covers hybrid and ternary plasmonic–semiconductor–MOF architectures, S-scheme chalcogenides for water splitting, mechanistic studies of non-thermal and hot-carrier processes, and advanced electromagnetic and quantum–mechanical modelling of nanoplasmonic systems. Together, these contributions delineate the current state of plasmonic solar energy research and current and future challenges. Throughout the roadmap enhancements are attributed to solar-excited plasmonic mechanisms (hot-carrier transfer, photothermal effects, near-field enhancements, etc). However, the contribution of each mechanism to enhancement is not well understood or easily quantifiable. A key challenge, therefore, is to fully quantify the contribution of each plasmonic mechanism, using both experimental techniques and material modelling, to enable the optimized design of plasmonic platforms. Further challenges relate to the chemical stability of the metals currently used as plasmonic materials, and their high cost. The use of non-metal shells around metal nanoparticles is seen as a promising way to overcome stability issues, while conductive transition metal nitrides are identified as attractive low-cost, chemically stable alternatives to noble metals in the visual-NIR spectrum. Finally, scalable nanofabrication techniques are required to produce efficient, durable, and economically viable plasmonic platforms for sustainable solar fuel and power generation.
Abstract The accurate measurement of electrical conductivity and the Seebeck coefficient are crucial for evaluating the thermoelectric performance of organic semiconductors, yet a standardized method is lacking, and measurement artifacts are not well understood. This study investigates the significant influence of film thickness on these measurements, using spray-coated films of a doped p-type copolymer (PDVT-10) with thicknesses ranging from 24 to 830 nm. We found that as the film thickness increased, the Seebeck coefficient substantially increased from 383.4 µ V K −1 to 900.5 µ V K −1 , while the electrical conductivity decreased. These results demonstrate that the measured conductivity and Seebeck coefficient are heavily influenced by film thickness, likely due to factors such as non-uniform current distribution and inhomogeneous dopant distribution. This work underscores the critical importance of standardizing film thickness to ensure the accurate characterization and reliable comparison of thermoelectric materials.
Cathode interfacial layers (CILs) are crucial for improving the performance of organic photodetectors (OPDs). In this work, a bay-position-modified perylene diimide derivative, SePDI3, is employed as an efficient CIL and systematically compared with the conventional CIL material PDINN. SePDI3 exhibits a more pronounced self-doping effect and a stronger capability to modulate the work function of the metal electrode, thereby enabling improved ohmic contact at the active layer/cathode interface. As a result, the OPD based on SePDI3 achieves a responsivity of 0.495 A W-1 and a specific detectivity of 4.34 & times; 1013 Jones at 830 nm. In addition, the SePDI3-based device demonstrates a faster photoresponse and a wider linear dynamic range compared with the PDINN-based counterpart. The enhanced performance is attributed to the ability of SePDI3 to facilitate interfacial charge transfer and extraction while suppressing molecular aggregation, thereby forming a smoother interfacial contact. This work proposes a simple and effective molecular design strategy for optimizing the cathode interface and developing high-performance OPDs.
Single-photon emitters (SPEs) constitute a foundational resource for quantum technologies, including secure communication, photonic quantum computing, and emerging quantum network architectures. A wide range of quantum materials, from atom-like point defects in bulk crystals to excitonic states in low-dimensional semiconductors, now provide bright, coherent, and scalable sources of non-classical light. Meanwhile, advances in photonic integration have enabled efficient routing, filtering, and on-chip manipulation of these emitters. From this perspective, we survey and discuss the technological landscape in which solid-state emitters interface with quantum sensing, quantum communication, quantum computation, and emerging photonic artificial intelligence platforms. Further, we discuss the materials landscape underpinning modern single-photon sources from the zero-dimensional, one-dimensional, two-dimensional and three-dimensional materials. Lastly, we highlight key integration pathways for these SPEs into scalable quantum photonic systems.
Abstract Nano-enabled living materials and living electronics represent the next frontier in integrating biology with advanced nanotechnology, offering unprecedented opportunities to design systems with programmable, adaptive, and multifunctional capabilities. By combining living cells or tissues with engineered nanostructures, living materials and electronics create platforms for bi-directional communication, sensing, and actuation. These advancements hold immense potential for applications in healthcare, energy systems, and environmental sustainability. This roadmap provides a comprehensive vision for advancing this transformative field, addressing scientific challenges, technological pathways, and long-term goals for deploying these hybrid systems at scale.
Abstract As photonic systems grow more complex, it becomes increasingly difficult to capture their behaviour within the conventional four dimensions of space and time, particularly for systems operating at the nanometer scale, where strong confinement effects, near-field interactions, and subwavelength structuring introduce additional layers of complexity. The concept of 5D photonics reflects this shift by incorporating additional physical, material, computational, adaptive, and quantum degrees of freedom as active components in design, control, and function. Rather than defining a single extra coordinate, higher-dimensional photonics is about operating photonic systems within expanded, dynamically accessible state spaces where multiple dimensions can interact and evolve together. This roadmap brings together perspectives ranging from modeling and design concepts to experimental platforms, materials, components, and system-level implementations. It covers a wide spectrum of synthetic and structured dimensions, nonlinear and strong-field regimes, adaptive and reconfigurable architectures, cyber-physical and engineering approaches, as well as inherently high-dimensional quantum and excitonic systems. Across all these areas, higher-dimensional thinking emerges not as an abstract construct but as a practical tool for enabling new functionalities, overcoming conventional design limitations, and bridging physical systems with digital and AI-driven layers. By framing these diverse developments within a shared higher-dimensional perspective, the roadmap aims to provide orientation in a rapidly expanding field, reveal conceptual connections between traditionally separate areas of photonics, and highlight common challenges and opportunities. In doing so, it positions higher-dimensional photonics as a central paradigm for developing future photonic technologies that are increasingly adaptive, intelligent, and integrated across physical and virtual domains.
Multi-agent, multimodal artificial intelligence frameworks with scientific platforms represent more than an incremental advance - they mark a paradigm shift in computational research methodology, introducing unprecedented capabilities in realtime knowledge synthesis and autonomous scientific discovery. Drawing parallels with biological evolution, where simple components are optimized and recombined to create sophisticated functional structures, these artificial intelligence frameworks employ analogous principles to develop autonomous problem-solving systems by predicting new connections and expanding established relationships. The emergence of higher-order capabilities from relatively simple artificial intelligence building blocks enables these systems to tackle challenging problems in protein modeling, molecular mechanics, and engineering design. Physics-aware agentic models go beyond conventional data-driven inference, through their ability to self-improve, error-correct, and learn from real-time feedback, autonomously planning experiments, generating and executing code, and reasoning over results - effectively closing the loop in scientific discovery. Their inherent capacity to seamlessly integrate data-driven and physicsdriven modeling approaches enables artificial intelligence systems to independently formulate hypotheses, design simulations, and analyze outcomes, representing a significant advance in automated scientific investigation. Unlike prior surveys, this perspective emphasizes the emergence of higher-order cognition and reflection in multiagent systems, framing them as autonomous collaborators rather than passive assistants. This perspective examines how this new paradigm is accelerating multidisciplinary breakthroughs in protein design, mechanics, and materials science, and explores its implications for the future of computational, theoretical and experimental research methodologies. We further highlight how agentic artificial intelligence frameworks extend beyond science into domains of art and music, revealing deep isomorphisms across molecules, materials, and creative expression.
Abstract Metal oxides and Transition metal oxides (TMOs) have garnered significant attention as electrode materials for supercapacitors due to their high theoretical capacitance and multiple redox states. Nevertheless, their application is often constrained by low intrinsic electrical conductivity and structural instability during repeated charge-discharge cycles. This review summarizes recent strategies for overcoming these limitations by integrating metal oxides with metal-organic frameworks (MOFs), conductive polymers, and MXene. MOFs act as porous templates or sacrificial precursors, enhancing ion diffusion and yielding nanostructured oxides with high surface area, leading to specific capacitance values. Conductive polymers such as polyaniline (PANI), polypyrrole (PPy), and poly(3,4-ethylenedioxythiophene) (PEDOT) provide pseudocapacitive behavior and reduce charge-transfer resistance. MXenes, with their high conductivity and layered structure, serve as conductive scaffolds and intercalation hosts, significantly improving rate capability and energy densities. The review covers advances in material design, synthesis approaches, and synergistic mechanisms contributing to enhanced electrochemical performance.
Surface-enhanced Raman scattering (SERS) is a powerful technique that significantly enhances Raman spectroscopy sensitivity. The electromagnetic (EM) mechanism is widely regarded as a key contributor to this enhancement. Traditionally, this mechanism has been theoretically and experimentally validated primarily in plasmonic systems dominated by radiant plasmon resonances, through spectral correlations between the SERS signals and Rayleigh scattering (or extinction) spectra. However, subradiant plasmon modes often complicate these correlations, especially in larger or geometrically asymmetric systems, where they play a crucial role in EM enhancement and lead to spectral uncorrelations between SERS and far-field scattering measurements, i.e. Rayleigh scattering (or extinction). This short review first theoretically reexplains EM enhancement mechanism in the viewpoint of radiant plasmon, then summarizes important studies which show the spectral uncorrelation between SERS and far-field scattering. This spectral uncorrelation indicates the existence of subradiant plasmon resonances which contributes to the SERS signal. Second, we explain the contribution of subradiant plasmon to EM enhancement by analysing these spectral uncorrelations observed in various plasmonic nanostructures. Third, it discusses the radiation characteristics of SERS light generated by subradiant resonances, supported by EM simulations. The significance of absorption spectroscopy, interpreted through quantum optics theory, is also highlighted for accurately evaluating subradiant plasmonic effects. Finally, a novel approach is introduced to directly quantify EM enhancement induced by subradiant plasmons using ultrafast surface-enhanced fluorescence, which frequently appears as background in SERS measurements.
Metal-semiconductor core-shell nanoparticles provide a versatile platform for combining plasmonic light confinement with semiconductor-mediated charge excitation. In this study, Au@TiO2 and Ag@TiO2 nanoparticles are systematically compared using finite-difference time-domain simulations across four metrics: sensing factor (SF), quantifying refractive-index sensitivity; spectral overlap integral (SOI), measuring alignment with the AM1.5 G solar spectrum; absorbed photon flux (APF), representing total photon absorption; and the surface-enhanced Raman spectroscopy (SERS) enhancement factor (EFSERS), quantifying surface-driven near-field intensity. Ag@TiO2 exhibits the highest SF (approximate to 115 nm/RIU) and maximal SOI, reflecting optimal spectral matching with sunlight. In contrast, Au@TiO2 shows lower SF (approximate to 75 nm/RIU) but slightly higher APF, together with significantly stronger near-field intensity (|E/Ei|2 approximate to 25.1 & times; 103) and EFSERS approximate to 6.3 & times; 10(8). The extended near-field in Au@TiO2 arises from interband damping broadening and higher core polarizability, whereas the Ag plasmon is more tightly confined within the TiO2 shell (|E/Ei|2 approximate to 6.1 & times; 103, EFSERS approximate to 3.7 & times; 10(7)). These results demonstrate that Ag cores are optimal for refractive-index sensing and solar-spectrum overlap, while Au cores maximize photon absorption and near-field-driven surface processes. This unified, quantitative framework links plasmonic sensitivity, photon-harvesting capability, and near-field enhancement, providing actionable design guidelines for core-shell nanoparticles in photocatalysis, SERS, and sensing applications.
Nano-enabled indoor photovoltaics (IPVs), based on molecular, nano-, and nanostructured absorbers, are emerging as promising energy harvesters for battery-free smart devices and internet-of-things sensor nodes. Despite rapid performance improvements, reliable characterization remains a critical challenge, with device-dependent errors arising from test light sources and measurement instrumentation representing a major contributor. Here, we establish the irradiance exponent sigma in the photocurrent-irradiance relationship as a central, device-specific parameter governing these errors. We present analytical models linking temporal instability, spatial non-uniformity, and luxmeter inaccuracies to IPV efficiency characterization errors, demonstrating that sigma strongly modulates error propagation. A literature survey of relevant absorbers shows that sigma spans 0.53-2.39, highlighting the need to explicitly account for device-specific behavior to ensure accurate IPV characterization. Leveraging our models and considering a realistic sigma range, we demonstrate that the current IPV standards, which adopt device-independent approaches, can result in misleading evaluations of IPV characterization accuracy. To address this issue, we introduce a new device-aware framework-with error metrics and an upgraded classification scheme for IPV measurements-that builds on existing IPV standards while overcoming their limitations, enabling more accurate and reliable measurement of nano-enabled IPVs. Our findings provide a reliable foundation for the future exploration and validation of novel IPV materials and device designs based on molecular, nano-, and nanostructured absorbers.
Bioinspired surfaces encode function in micro/nanotopography rather than in complex chemical functionalization, but the critical challenge lies in scalable manufacturing on flexible and curved substrates while ensuring durability and cost control. This Perspective consolidates capillary force lithography, nanoimprint lithography, and electrohydrodynamic lithography (EHL) under the framework of pressure-driven lithography, emphasizing EHL as the field-programmable keystone of hybrid roll-to-roll architectures. We establish process-to-function maps connecting geometry descriptors with antibacterial, wetting, optical, and icephobic functionalities, guided by standardized key performance indicators. Inline optical and current metrology with feedback control are detailed as the pathway to reproducibility, robustness, and energy efficiency at scale. Representative datasets and a pragmatic 2025–2030 roadmap translate these principles into industrial playbooks and pre-normative standards. The outcome is a software-defined factory for multifunctional surfaces—one line, many products—achieved with agility, comparability, and durable performance.
As the new Editor-In-Chief of Nano Futures, I am excited to share our vision for the journal’s direction in the coming years. Our goal is to position Nano Futures as a leading platform for innovative nanoscience and nanotechnology research. To further advance the field, we are introducing two new article types: Trajectory papers, which track the evolution of key metrics in nanoscience and nanotechnology and provide projections for future developments, and Nano Standards, which focus on establishing new standards to address pressing challenges in methodologies, measurement protocols, figures of merit, and reporting practices. These initiatives, alongside our commitment to rigorous peer review, aim to enhance the journal’s impact and foster a collaborative community. By prioritizing integrity and innovation, we are dedicated to supporting the community in shaping the future of nanoscience and nanotechnology.
Nano-material based resistive gas sensors are gaining in popularity because of their small size, low cost, and simple integration with analogue interface electronics. In gas sensors, semiconducting metal oxides are most widely used as the sensing layer, because they offer high sensitivity to gases and have a low detection limit (ppb to ppm). However, metal oxides suffer from a poor selectivity, because they generally respond to both oxidising and reducing gas molecules. In addition, they can suffer from baseline drift/stability and in some cases degradation under humid conditions. Some of these drawbacks can be ameliorated using metal oxide heterojunctions. A metal oxide heterojunction is the formation of a junction between two materials with different work functions. The metal oxide heterojunction can improve sensor performance through a controlled depletion region (band bending), charge transfer, catalytic effects, and improved gas adsorption kinetics. They can also permit a lower operating temperature (hence lower power), improved sensitivity, faster response and better stability. This review paper discusses in detail the different techniques to synthesise metal oxide heterojunctions, the sensing mechanisms, and how they can be a generation of improved gas sensors. Finally, we discuss the emergence of artificial intelligence to enable the identification of gas type and concentrations from multi-component environments.
Ammonia is indispensable to agriculture and energy applications, but its conventional synthesis through the Haber–Bosch process remains both energy- and carbon-intensive. These concerns have driven interest in renewable-powered ammonia synthesis. The photoelectrochemical reduction of nitrogen and nitrate in aqueous electrolytes presents a promising alternative, converting solar energy into ammonia by integrating the advantages of electrochemical and photochemical methods. This process involves the use of photoelectrodes consisting of semiconductors that harness solar energy to excite electrons, and co-catalysts that facilitate electron transfer to reactants in the electrolyte. In this review, we examine recent developments in photoelectrode design for photoelectrochemical ammonia synthesis, with a particular focus on material properties such as semiconductor composition, band structure, surface morphology, co-catalyst selection, and emerging strategies that influence catalytic activity and selectivity. This review aims to provide a comprehensive overview of photoelectrode applications in solar-driven ammonia synthesis.
Two-dimensional (2D) semiconductors like tungsten diselenide (WSe 2 ) have significant potential for next-generation phototransistors because of their broad detection range and strong light-matter interaction. However, dielectric disorder at the interface with conventional silicon dioxide (SiO 2 ) substrates hampers the device performance by introducing surface charge traps and oxygen dangling bonds, which degrade carrier transport and increase low-frequency noise (LFN). Here, we show that simply incorporating a hexagonal boron nitride (hBN) passivation layer between WSe 2 and SiO 2 effectively reduces interfacial disorder by creating a clean van der Waals interface through optoelectrical and LFN analysis. The WSe 2 /hBN phototransistors demonstrate a 100-fold decrease in LFN, a 100-fold increase in responsivity, and a 10-fold improvement in specific detectivity compared to WSe 2 devices without an hBN layer. This enhancement in device performance is ascribed to shielding Coulomb scattering and maintaining the intrinsic transport properties of WSe 2 . Our findings underscore the importance of passivating the channel scattering sources of 2D-based phototransistors in enhancing photodetection performance.
Multinary chalcogenide quantum dots (MCQDs) exhibit unprecedented variability in composition and properties, size tunability, and high tolerance to multiple alloying, doping, and deviations from stoichiometry. This variability enables the synthesis of hundreds of thousands of MCQDs, characterized by a wide range of composition- and size-dependent spectral and photophysical properties, with a high potential for optoelectronic applications. At that, the whole compositional richness of MCQDs can be readily accessed using sustainable aqueous chemistry. The present Perspective focuses on the challenges of navigating the vast compositional space of MCQDs to discover new optoelectronic materials for the absorption, emission, and conversion of light. We argue that the exploration of the compositional versatility of MCQDs requires accelerated research, going beyond the conventional intuition-driven experiments. The acceleration can be achieved by high-throughput parallelized experimentation that yields extensive datasets and enables machine-learning-driven data analysis and automation of the targeted discovery of new MCQDs.
Two-dimensional (2D) atomic materials regard a large family of monolayer or few layers atomic sheets that can stand alone. While there are no dangling chemical bonds in the direction normal to the sheets, stacking different kinds of 2D atomic materials into so-called van der Waals (vdW) heterostructures enables a platform for designing high-performance quantum devices that can have unique and unprecedented physical properties and device functionalities in conventional semiconductors. Following the discovery of the first 2D atomic material of graphene in 2004, the progress in exploration of new 2D atomic materials and their vdW heterostructures has been explosive during the past two decades. This perspective aims to highlight some recent progress in the research of vdW heterostructures, particularly in transition-metal dichalcogenide/graphene optoelectronics, enabled by the quantum physics in constituent components and their interfaces. New approaches in layer by layer synthesizing these vdW heterostructures are promising for wafer-size fabrication of high-performance, low-cost optoelectronic devices for practical applications.
