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In the 'Beyond Moore's Law' era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore's Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries.

The dissemination of sensors is key to realizing a sustainable, 'intelligent' world, where everyday objects and environments are equipped with sensing capabilities to advance the sustainability and quality of our lives—e.g., via smart homes, smart cities, smart healthcare, smart logistics, Industry 4.0, and precision agriculture. The realization of the full potential of these applications critically depends on the availability of easy-to-make, low-cost sensor technologies. Sensors based on printable electronic materials offer the ideal platform: they can be fabricated through simple methods (e.g., printing and coating) and are compatible with high-throughput roll-to-roll processing. Moreover, printable electronic materials often allow the fabrication of sensors on flexible/stretchable/biodegradable substrates, thereby enabling the deployment of sensors in unconventional settings. Fulfilling the promise of printable electronic materials for sensing will require materials and device innovations to enhance their ability to transduce external stimuli—light, ionizing radiation, pressure, strain, force, temperature, gas, vapours, humidity, and other chemical and biological analytes. This Roadmap brings together the viewpoints of experts in various printable sensing materials—and devices thereof—to provide insights into the status and outlook of the field. Alongside recent materials and device innovations, the roadmap discusses the key outstanding challenges pertaining to each printable sensing technology. Finally, the Roadmap points to promising directions to overcome these challenges and thus enable ubiquitous sensing for a sustainable, 'intelligent' world.

We present a concise overview of the state of affairs in the development of single-photon sources based on two-dimensional (2D) crystals, focusing in particular on transition-metal dichalcogenides and hexagonal boron nitride. We briefly discuss the current level of advancement (i) in our understanding of the microscopic origin of the quantum emitters (QEs) identified in these two material systems, and (ii) in the characterisation of the optical properties of these emitters; then, we survey the main methods developed to enable the dynamic control of the QEs' emission energy. Finally, we summarise the main results stemming from the coupling of QEs embedded in 2D materials with photonic and plasmonic structures.

Two-dimensional materials with a single or few layers are exciting nano-scale materials that exhibit unprecedented multi-functional properties including optical, electronic, thermal, chemical and mechanical characteristics. A single layer of different 2D materials or a few layers of the same material may not always have the desired application-specific properties to an optimal level. In this context, a new trend has started gaining prominence lately to develop engineered nano-heterostructures by algorithmically stacking multiple layers of single or different 2D materials, wherein each layer could further have individual twisting angles. The enormous possibilities of forming heterostructures through combining a large number of 2D materials with different numbers, stacking sequences and twisting angles have expanded the scope of nano-scale design well beyond considering only a 2D material mono-layer with a specific set of given properties. Magic angle twisted bilayer graphene (BLG), a functional variant of van der Waals heterostructures, has created a buzz recently since it achieves unconventional superconductivity and Mott insulation at around 1.1^∘ twist angle. These findings have ignited the interest of researchers to explore a whole new family of 2D heterostructures by introducing twists between layers to tune and enhance various multi-physical properties individually as well as their weighted compound goals. Here we aim to abridge outcomes of the relevant literature concerning twist-dependent physical properties of BLG and other multi-layered heterostructures, and subsequently highlight their broad-spectrum potential in critical engineering applications. The evolving trends and challenges have been critically analysed along with insightful perspectives on the potential direction of future research.

Quantum computing is poised to solve practically useful problems which are computationally intractable for classical supercomputers. However, the current generation of quantum computers are limited by errors that may only partially be mitigated by developing higher-quality qubits. Quantum error correction (QEC) will thus be necessary to ensure fault tolerance. QEC protects the logical information by cyclically measuring syndrome information about the errors. An essential part of QEC is the decoder, which uses the syndrome to compute the likely effect of the errors on the logical degrees of freedom and provide a tentative correction. The decoder must be accurate, fast enough to keep pace with the QEC cycle (e.g. on a microsecond timescale for superconducting qubits) and with hard real-time system integration to support logical operations. As such, real-time decoding is essential to realize fault-tolerant quantum computing and to achieve quantum advantage. In this work, we highlight some of the key challenges facing the implementation of real-time decoders while providing a succinct summary of the progress to-date. Furthermore, we lay out our perspective for the future development and provide a possible roadmap for the field of real-time decoding in the next few years. As the quantum hardware is anticipated to scale up, this perspective article will provide a guidance for researchers, focusing on the most pressing issues in real-time decoding and facilitating the development of solutions across quantum, nano and computer science.

Near-field interaction between the monolayers of two-dimensional (2D) materials has been recently investigated. Another branch under investigation has been the interaction between 2D materials and zero-dimensional (0D) nanostructures including quantum dots (QDs) and metal nanoparticles. In this work, we take one more step to engineering the interaction between those systems. We probe the effect of mechanical strain on the non-radiative energy transfer (NRET) rate from a 0D material, ZnCdSe/ZnSe QD, to a 2D material, monolayer (1L) WS₂. It is known that the mechanical strain causes large shifts to the exciton energies in 1L WS₂. As a result, our calculations show that strain can tune the NRET rate by engineering the overlap between the emission spectrum of ZnCdSe/ZnSe QD and the exciton resonances of 1L WS₂.

Neuromorphic circuits based on spikes are currently envisioned as a viable option to achieve brain-like computation capabilities in specific electronic implementations while limiting power dissipation given their ability to mimic energy-efficient bioinspired mechanisms. While several network architectures have been developed to embed in hardware the bioinspired learning rules found in the biological brain, such as spike timing-dependent plasticity, it is still unclear if hardware spiking neural network architectures can handle and transfer information akin to biological networks. In this work, we investigate the analogies between an artificial neuron combining memristor synapses and rate-based learning rule with biological neuron response in terms of information propagation from a theoretical perspective. Bioinspired experiments have been reproduced by linking the biological probability of release with the artificial synapse conductance. Mutual information and surprise have been chosen as metrics to evidence how, for different values of synaptic weights, an artificial neuron allows to develop a reliable and biological resembling neural network in terms of information propagation and analysis.

A facile and effective catalyst deposition process for carbon nanotube (CNT) array growth via chemical vapor deposition using a resistively heated thermal evaporation technique to sublimate FeCl₃ onto the substrate is demonstrated. The catalytic activity of the sublimated FeCl₃ catalyst precursor is shown to be comparable to the well-studied e-beam evaporated Fe catalyst, and the resulting vertically aligned CNTs (VA-CNTs) have a similar diameter, walls, and defects, as well as improved bulk electrical conductivity. In contrast to standard e-beam-deposited Fe, which yields base-growth CNTs, scanning and transmission electron microscopy and X-ray photoelectron spectroscopy characterizations reveal a tip-growth mechanism for the FeCl₃-derived VA-CNT arrays/forests. The FeCl₃-derived forests have a lower (∼1/3 less) longitudinal indentation modulus, but higher longitudinal electrical conductivity (greater than twice) than that of the e-beam Fe-grown CNT arrays. The sublimation process to grow high-quality VA-CNTs is a highly facile and scalable process (extensive substrate shape and size, and moderate vacuum and temperatures) that provides a new route to synthesizing aligned CNT forests for numerous applications.

We present a one-step hydrothermal synthesis of hybrids consisting of nickel sulfides in the form of Ni₃S_4–NiS (NN) and Ni₃S₄–NiS-rGO (NNR), i.e. with the addition of reduced graphene oxide (rGO), for application as catalysts. After accurate physical characterization and confirmation of successful synthesis, we evaluate the ability of these catalysts in the processes of methanol and ethanol oxidation. The precise electrochemical analyses show relatively good potential and excellent cyclic stability in methanol oxidation reaction (MOR) and ethanol oxidation reaction (EOR) processes. The comparison of the two catalysts shows the superiority of NNR over NN, confirming that rGO introduces a higher specific surface area and a higher electrical conductivity in the NNR structure. In the process of MOR, NNR has an oxidation peak at a current density of 55 mA cm⁻² and a peak potential of 0.54 V. In EOR, this peak is located at a current density of 11 mA cm⁻² and at a peak potential of 0.59 V. NNR has 97% and 94% stability in MOR and EOR after 1000 consecutive cycles, respectively, which are acceptable values.

Para-nitroaniline (PNA) and ortho-nitroaniline (ONA) are highly toxic contaminants in aqueous solution and must be treated. In the current investigation, novel magnetic nanocomposites containing copper ferrite (CuFe₂O₄) and gelatin-derived carbon quantum dots (CQDs) were successfully synthesized. The prepared nanocatalyst was characterized by scanning electron microscopy, x-ray diffraction, transmission electron microscopy, Brunauer–Emmet–Teller (BET), Fourier transform infrared and ultraviolet–visible techniques. The mesoporous structure of the CuFe₂O₄/CQD nanocomposite was shown using the BET/Barrett–Joyner–Halenda technique. The catalytic performance of the nanocatalyst during the reduction of PNA and ONA was assessed in an aqueous medium at 25 °C. The complete reduction of PNA and ONA using the CuFe₂O₂/CQDs nanocomposite occurred in 13 s and 35 s, respectively. The pseudo-second-order rate constant (K_app) was obtained as 2.89 × 10⁻¹ s⁻¹ and 9.3 × 10⁻² s⁻¹ for reducing PNA and ONA, respectively. Moreover, the magnetic nanocatalyst was easily separated from the reaction solution and recycled for up to six consecutive cycles without significant loss of catalytic activity.

Network-based biocomputation (NBC) is an alternative, parallel computation approach that can potentially solve technologically important, combinatorial problems with much lower energy consumption than electronic processors. In NBC, a combinatorial problem is encoded into a physical, nanofabricated network. The problem is solved by biological agents (such as cytoskeletal filaments driven by molecular motors) that explore all possible pathways through the network in a massively parallel and highly energy-efficient manner. Whereas there is currently a rapid development in the size and types of problems that can be solved by NBC in proof-of-principle experiments, significant challenges still need to be overcome before NBC can be scaled up to fill a technological niche and reach an industrial level of manufacturing. Here, we provide a roadmap that identifies key scientific and technological needs. Specifically, we identify technology benchmarks that need to be reached or overcome, as well as possible solutions for how to achieve this. These include methods for large-scale production of nanoscale physical networks, for dynamically changing pathways in these networks, for encoding information onto biological agents, for single-molecule readout technology, as well as the integration of each of these approaches in large-scale production. We also introduce figures of merit that help analyze the scalability of various types of NBC networks and we use these to evaluate scenarios for major technological impact of NBC. A major milestone for NBC will be to increase parallelization to a point where the technology is able to outperform the current run time of electronic processors. If this can be achieved, NBC would offer a drastic advantage in terms of orders of magnitude lower energy consumption. In addition, the fundamentally different architecture of NBC compared to conventional electronic computers may make it more advantageous to use NBC to solve certain types of problems and instances that are easy to parallelize. To achieve these objectives, the purpose of this roadmap is to identify pre-competitive research domains, enabling cooperation between industry, institutes, and universities for sharing research and development efforts and reducing development cost and time.

The evolution of nanofluids over the years has opened new research opportunities in the field of renewable energy. Research on the optical properties of nanofluids for application in direct absorption solar collectors (DASCs) is progressing at a burgeoning speed. In a DASC system, nanofluid with high optical absorptivity can convert the incident solar energy into the thermal energy of the fluid. The dispersed nanoparticles in the fluid act in the process through the phenomenon of absorption and scattering. Studies conducted on the optical property characterization of monocomponent nanofluids have become saturated. Moreover, the photothermal efficiency (PTE) of the nanofluid can be enhanced by using multicomponent nanofluids. Nanofluids prepared using varying materials, shapes and sizes of nanoparticles can tune the absorption spectra of the bulk fluid to improve the PTE. A hybrid nanocomposite can similarly enhance the absorptivity due to the synergy of materials present in the nanocomposite particle. In this review, a comprehensive survey on the synthesis and optical characterization of different monocomponent, blended and hybrid nanocomposite nanofluids has been performed.

Raman spectroscopy is a powerful method that gives insight into the atomic structure and composition of nanomaterials, but also allows to draw conclusions about their electronic properties. It is based on the inelastic scattering of light, which is able to excite phonons in the material. In the field of semiconductor nanocrystals, Raman spectroscopy has been employed to make significant contributions to the analysis of lattice distortion, interfaces, phase mixing, and defect formation. Yet, there is no clear consensus on how the electronic and crystal structure of the material interacts with the incident light to yield the observed spectra. This review gives a brief overview over the method. It then reviews the most important findings, current developments, and discusses the efforts to formulate a consistent model that allows to establish the method as a tool for structural analysis.

Graphene-based thermal rectification was investigated by measuring the thermal transport properties of asymmetric suspended graphene nanomesh devices. A sub-10 nm periodic nanopore phononic crystal structure was successfully patterned on the half area of the suspended graphene ribbon by helium ion beam milling technology. The 'differential thermal leakage' method was developed for thermal transport measurement without disturbance from the leakage of electron current through the suspended graphene bridge. A thermal rectification ratio of up to 60% was observed in a typical device with a nanopore pitch of 20 nm. By increasing the nanopore pitch in a particular range, the thermal rectification ratio showed an increment. However, this ratio was degraded by increasing the environmental temperature. This experiment suggests a promising way to develop a high-performance thermal rectifier by using a phononic crystal to introduce asymmetry on homogeneous material.

The rapid rise of nanotechnology has resulted in a parallel rise in the number of products containing nanomaterials. The unusual properties that nano forms of materials exhibit relative to the bulk has driven intense research interest and relatively rapid adoption by industry. Regulatory agencies are charged with protecting workers, the public, and the environment from any adverse effects of nanomaterials that may also arise because of these novel physical and chemical properties. They need data and models that allow them to flag nanomaterials that may be of concern, while balancing potential stifling of commercial innovation. Roadmaps for the future of safe nanotechnology were defined more than a decade ago, but many roadblocks identified in these studies remain. Here, we discuss the roadblocks that are still hindering the effective application of informatics and predictive computational nanotoxicology methods from providing more effective guidance to nanomaterials regulatory agencies and safe-by-design rationale for industry. We describe how developments in high throughput synthesis, characterization, and biological assessment of nanomaterials will overcome many of these roadblocks, allowing a clearly defined roadmap for computational design of effective but safe-by-design nanomaterials to be realized.

Powered by the mutual developments in instrumentation, materials and theoretical descriptions, sensing and imaging capabilities of quantum emitters in solids have significantly increased in the past two decades. Quantum emitters in solids, whose properties resemble those of atoms and ions, provide alternative ways to probing natural and artificial nanoscopic systems with minimum disturbance and ultimate spatial resolution. Among those emerging quantum emitters, the nitrogen vacancy (NV) color center in diamond is an outstanding example due to its intrinsic properties at room temperature (highly-luminescent, photo-stable, biocompatible, highly-coherent spin states). This review article summarizes recent advances and achievements in using NV centers within nano- and single crystal diamonds in sensing and imaging. We also highlight prevalent challenges and material aspects for different types of diamond and outline the main parameters to consider when using color centers as sensors. As a novel sensing resource, we highlight the properties of NV centers as light emitting electrical dipoles and their coupling to other nanoscale dipoles e.g. graphene.

In this paper we revisit the structure of graphene oxide, and determine the pure and truly representative structures for graphene nanoflakes using machine learning. Using 20 396 random configurations relaxed at the electronic structure level, we observe the presence of hydroxyl, ether, double bonds, aliphatic (cyclohexane) disruption, defects and significant out-of-plane distortions that go beyond the Lerf–Klinowski model. Based on an diverse list of 224 chemical, structural and topological features we identify 25 archetypal 'pure' graphene oxide structures which capture all of the complexity and diversity of the entire data set; and three prototypes that are the truly representative averages in 224-dimensional space. Together these 28 structures, which are shown to be largely robust against changes in thermochemical conditions modeled using ab initio thermodynamics, can be downloaded and used collectively as a small data set for with a fraction of the computational cost in future work, or independently as an exemplar of graphene oxide with the required oxidation.

Single-electron transistor (SET) device fabrication for operation in the tens of Kelvin range is still challenging due to the need of controlled definition of the metallic island with a diameter far below 100 nm and proper tuning of the island's tunnel couplings to the drain and source leads. Here we present results on SET device fabrication using focused electron beam induced deposition (FEBID) for island definition between pre-fabricated SET electrode structures. The island's nano-granular microstructure allows us, in conjunction with in situ tuning of the inter-grain tunnel coupling by post-growth electron irradiation, to study the effect of the island's electronic granularity on SET device performance. In addition we show that for reliable SET operation FEBID-associated co-deposit in proximity of the island has to be removed which can be accomplished by a novel in situ Ar ion etching process. For the low-temperature properties of functioning SET devices we obtain good agreement of capacitance values deduced from the current–voltage characteristics and capacitance calculations based on the geometry of the device electrodes and the microstructure of the island. Complementary simulations of the SET current–voltage characteristics based on the master equation approach are in good agreement with the experimental data. The observation of well-defined Coulomb oscillations indicates that FEBID-based SET structures can be useful as on-demand charge monitor devices with high lateral positioning flexibility.

In this work, we demonstrate trapping of microparticles using plasmonic tweezers based on arrays of annular apertures. The transmission spectra and the electric-field distribution are simulated to calibrate the arrays. Theoretically, we observe sharp peaks in the transmission spectra for dipole resonance modes and these are red-shifted as the size of the annular aperture is reduced. We also expect an absorption peak at approximately 1115 m for the localised plasmon resonance. Using a laser frequency between the two resonances, multiple plasmonic hot spots are created and used to trap and transport micron and submicron particles. Experimentally, we demonstrate trapping of individual 0.5 μm and 1 μm polystyrene particles and the feasibility of particle transportation over the surface of the annular apertures using less than 1.5 mW μm⁻² incident laser intensity at 980 nm.

Using top-down fabricated silicon nanoribbons, we measure the opening and closing of ion channels alamethicin and gramicidin A. A capacitive model of the system is proposed to demonstrate that the geometric capacitance of the nanoribbon is charged by ion channel currents. The integration of top-down nanoribbons with electrophysiology holds promise for integration of electrically active living systems with artificial electronics.