Nano Futures

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.

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 synthesis of graphene through environmentally friendly and efficient methods has posed a persistent challenge, prompting extensive research in recent years to access sustainable sources and attain high quality graphene competing with the one obtained from graphite ores. Addressing this challenge becomes even more intricate when aiming to convert captured CO₂ into graphene structures, encountering hurdles stemming from the inherent stability of the CO₂ molecule and its steadfast transformation. Together with CO₂, there is a great potential to create carbon source by using natural biomass, cellulosic plant sources and industrial wastes. This comprehensive review delves into the recent synthesis techniques and developments, exploring both direct and indirect pathways for the integration of CO₂ that strive to overcome the complexities associated with transforming CO₂ into graphene. The review critically analyzes CO₂ capturing mechanisms designed for air, ocean, and alternative sources, outlining the progress made in harnessing captured CO₂ as a feedstock for graphene production by evaluating captured CO₂ values. This review consolidates the recent advancements by providing a roadmap for future research directions in the sustainable synthesis of graphene from captured CO₂ in the pursuit of a greener, circular economy.

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.

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.

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.

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.

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 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.

The synthesis of graphene through environmentally friendly and efficient methods has posed a persistent challenge, prompting extensive research in recent years to access sustainable sources and attain high quality graphene competing with the one obtained from graphite ores. Addressing this challenge becomes even more intricate when aiming to convert captured CO₂ into graphene structures, encountering hurdles stemming from the inherent stability of the CO₂ molecule and its steadfast transformation. Together with CO₂, there is a great potential to create carbon source by using natural biomass, cellulosic plant sources and industrial wastes. This comprehensive review delves into the recent synthesis techniques and developments, exploring both direct and indirect pathways for the integration of CO₂ that strive to overcome the complexities associated with transforming CO₂ into graphene. The review critically analyzes CO₂ capturing mechanisms designed for air, ocean, and alternative sources, outlining the progress made in harnessing captured CO₂ as a feedstock for graphene production by evaluating captured CO₂ values. This review consolidates the recent advancements by providing a roadmap for future research directions in the sustainable synthesis of graphene from captured CO₂ in the pursuit of a greener, circular economy.

With graphene and related two-dimensional (2D) materials now enhancing products used in everyday life, the scale of industrial production of many different types of 2D nanomaterials requires quality control (QC) processes that can be performed rapidly, non-destructively, in-line and in a cost-effective manner. These materials must be repeatably produced with targeted material properties, to reduce the costs associated with nonconformity of products, and so multiple QC methods that can monitor different material properties are required. Herein, we describe different measurands and associated techniques that either have the potential to be used for QC, or are already being used in this way, whether that off-line, at-line or in-line. The advantages and disadvantages of different techniques are detailed, as well as possible solutions that can ensure confidence in these methods and lead to measurement traceability in this growing industry.

We present overlapping top gate electrodes for the formation of gate defined lateral junctions in semiconducting layers as an alternative to the back gate/top gate combination and to the split gate configuration. The optical lithography microfabrication of the overlapping top gates is based on multiple layers of low-temperature atomic layer deposited hafnium oxide, which acts as a gate dielectric and as a robust insulating layer between two overlapping gate electrodes exhibiting a large dielectric breakdown field of $\gt\!\! 1 \times 10^9\,\textrm{V m}^{-1}$. The advantage of overlapping gates over the split gate approach is confirmed in model calculations of the electrostatics of the gate stack. The overlapping gate process is applied to Hall bar devices of mercury telluride in order to study the interaction of different quantum Hall states in the nn', np, pn and pp' regime.

The development of photonic platforms for the visible or ultra-violet spectral range represents a major challenge. In this article, we present an overview of the technological solutions available on the market. We discuss the pros and cons associated with heterogeneous or monolithic integration. We specifically focus on the III-nitride platform for integrated photonics. The III-nitrides offer every building block needed for a universal platform. We discuss the additional opportunities offered by combining III-nitride semiconductors with other materials such as two-dimensional materials.

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.

The synthesis of graphene through environmentally friendly and efficient methods has posed a persistent challenge, prompting extensive research in recent years to access sustainable sources and attain high quality graphene competing with the one obtained from graphite ores. Addressing this challenge becomes even more intricate when aiming to convert captured CO₂ into graphene structures, encountering hurdles stemming from the inherent stability of the CO₂ molecule and its steadfast transformation. Together with CO₂, there is a great potential to create carbon source by using natural biomass, cellulosic plant sources and industrial wastes. This comprehensive review delves into the recent synthesis techniques and developments, exploring both direct and indirect pathways for the integration of CO₂ that strive to overcome the complexities associated with transforming CO₂ into graphene. The review critically analyzes CO₂ capturing mechanisms designed for air, ocean, and alternative sources, outlining the progress made in harnessing captured CO₂ as a feedstock for graphene production by evaluating captured CO₂ values. This review consolidates the recent advancements by providing a roadmap for future research directions in the sustainable synthesis of graphene from captured CO₂ in the pursuit of a greener, circular economy.

The development of photonic platforms for the visible or ultra-violet spectral range represents a major challenge. In this article, we present an overview of the technological solutions available on the market. We discuss the pros and cons associated with heterogeneous or monolithic integration. We specifically focus on the III-nitride platform for integrated photonics. The III-nitrides offer every building block needed for a universal platform. We discuss the additional opportunities offered by combining III-nitride semiconductors with other materials such as two-dimensional materials.

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.

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.

Lipid nanoparticles have become a major disruptor within the drug delivery field of complex RNA molecules. The wide applicability of prototype nanomedicines have the potential to fill clinical requirements against current untreatable diseases. The uptake and implementation of analytical technologies to evaluate these prototype nanomedicines have not experienced similar growth rates hindering the translation of LNPs. Here, we evaluate a model RNA-LNP formulation with a selection of routine, and high-resolution orthogonal analytical techniques across studies on manufacturing process parameter impact and formulation stability evaluation under refrigerated and ultra-low temperatures. We analysed model cationic RNA complexed lipid nanoparticle formulation through process impact on formulation critical quality attributes, short term refrigerated stability evaluation, and frozen storage stability using zetasizer dynamic light scattering and nanoparticle tracking analysis. We also evaluated freeze/thaw induced stress on LNP formulation using high resolution field-flow fractionation. Statistical analysis and correlations between techniques were drawn to further enhance our understanding of LNP formulation design, and physiochemical attributes to facilitate LNP formulation clinical translation.

This article is withdrawn as it is a duplicate of https://doi.org/10.1088/2399-1984/aad9b8.

Lipid nanoparticles have become a major disruptor within the drug delivery field of complex RNA molecules. The wide applicability of prototype nanomedicines have the potential to fill clinical requirements against current untreatable diseases. The uptake and implementation of analytical technologies to evaluate these prototype nanomedicines have not experienced similar growth rates hindering the translation of LNPs. Here, we evaluate a model RNA-LNP formulation with a selection of routine, and high-resolution orthogonal analytical techniques across studies on manufacturing process parameter impact and formulation stability evaluation under refrigerated and ultra-low temperatures. We analysed model cationic RNA complexed lipid nanoparticle formulation through process impact on formulation critical quality attributes, short term refrigerated stability evaluation, and frozen storage stability using zetasizer dynamic light scattering and nanoparticle tracking analysis. We also evaluated freeze/thaw induced stress on LNP formulation using high resolution field-flow fractionation. Statistical analysis and correlations between techniques were drawn to further enhance our understanding of LNP formulation design, and physiochemical attributes to facilitate LNP formulation clinical translation.

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.

The synthesis of graphene through environmentally friendly and efficient methods has posed a persistent challenge, prompting extensive research in recent years to access sustainable sources and attain high quality graphene competing with the one obtained from graphite ores. Addressing this challenge becomes even more intricate when aiming to convert captured CO₂ into graphene structures, encountering hurdles stemming from the inherent stability of the CO₂ molecule and its steadfast transformation. Together with CO₂, there is a great potential to create carbon source by using natural biomass, cellulosic plant sources and industrial wastes. This comprehensive review delves into the recent synthesis techniques and developments, exploring both direct and indirect pathways for the integration of CO₂ that strive to overcome the complexities associated with transforming CO₂ into graphene. The review critically analyzes CO₂ capturing mechanisms designed for air, ocean, and alternative sources, outlining the progress made in harnessing captured CO₂ as a feedstock for graphene production by evaluating captured CO₂ values. This review consolidates the recent advancements by providing a roadmap for future research directions in the sustainable synthesis of graphene from captured CO₂ in the pursuit of a greener, circular economy.

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.

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.