Nano Futures

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.

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.

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.

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.

In this review, we begin by discussing the need for harnessing renewable energy resources in the context of global energy demands. A summary of first- and second-generation solar cells, their efficiency and grid parity is provided, followed by the need to reduce material and installation costs, and achieve higher efficiencies beyond the Shockley–Queisser limit imposed on single junction cells. We also discuss the specific advantages offered by nanomaterials in enhanced energy harvesting, what design platforms comprise nanostructured photovoltaics, and list the prominent categories of nanomaterials used in the design of third generation solar cells. We review the significant nanostructured photovoltaic platforms that have encouraging power conversion efficiencies, have the potential for long term stability (both structural and functional) and have received attention in the field. In addition to their operational principle, we highlight both their advantages and shortcomings, along with insights into possible improvements. We include alternate routes to improving power conversion efficiency, not by tuning material properties to match the solar spectrum but using additives and/or structural modifications to allow more efficient harvesting of sunlight, either by reducing losses or by altering the spectral properties. The properties of nanomaterials that make them well-suited as active materials in photovoltaic devices (broadband absorption, high quantum yield, etc.) also make them ideal candidates for luminescent solar concentrators (LSCs). These devices also harvest solar energy, but instead of directly allowing charge generation, they act as downconverters for other photovoltaic cells. We review dye, thin film, and quantum dot based LSCs that have garnered a lot of attention in recent years as these devices face a resurgence given the advances in materials science and engineering which have led to novel quantum dots and hybrid semiconductors. The review ends with where the future of nanostructured photovoltaics is headed, what device designs and materials development are needed to achieve efficiencies beyond the Shockley–Queisser limits and fulfil the goal of the 3rd and 4th generation photovoltaics.

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.

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.

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 continuous development of increasingly powerful supercomputers makes theory-guided discoveries in materials and molecular sciences more achievable than ever before. On this ground, the incoming arrival of exascale supercomputers (running over 10¹⁸ floating point operations per second) is a key milestone that will tremendously increase the capabilities of high-performance computing (HPC). The deployment of these massive platforms will enable continuous improvements in the accuracy and scalability of ab initio codes for materials simulation. Moreover, the recent progress in advanced experimental synthesis and characterisation methods with atomic precision has led ab initio-based materials modelling and experimental methods to a convergence in terms of system sizes. This makes it possible to mimic full-scale systems in silico almost without the requirement of experimental inputs. This article provides a perspective on how computational materials science will be further empowered by the recent arrival of exascale HPC, going alongside a mini-review on the state-of-the-art of HPC-aided materials research. Possible challenges related to the efficient use of increasingly larger and heterogeneous platforms are commented on, highlighting the importance of the co-design cycle. Also, some illustrative examples of materials for target applications, which could be investigated in detail in the coming years based on a rational nanoscale design in a bottom-up fashion, are summarised.

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.

Since carbon dots (CDs)-metal nanoparticles (MNPs) nanocomposites combine the advantages of both carbon quantum dots (CQDs) and MNPs, they show unique properties and are applied in heterogeneous catalysis. In the nanocomposite catalysts, CDs can act as modifiers to modulate the electronic properties of the metals or produce synergy with the metals. Consequently, the nanocomposite catalysts have good catalytic performance. This paper summarizes the preparation methods of nanocomposite catalysts and focuses on their applications in heterogeneous catalysis. Various specific preparation methods are not only summarized as completely as possible but also are also classified at the macro logic level. The applications of the nanocomposite catalysts in heterogeneous catalysis include photocatalysis, sonocatalysis, electrocatalysis, and thermal catalysis. It also reveals how the nanocomposite catalysts produce excellent catalytic performances in various catalytic reactions. Finally, the existing problems and the direction of future efforts are proposed. It is hoped that this paper will provide a slight reference for the future research of MNPs-CQDs nanocomposite catalysts and their application in the field of catalysis.

Gastric cancer is a prominent cause of death globally. The major risk factors responsible for its development include age, H. pylori infection, excessive salt intake, and lack of fruits and vegetables in the diet. It is diagnosed using ultrasound, CT scan, endoscopic biopsy, and by detection of certain biomarkers. The conventional therapies for treatment of gastric cancer include the use of radiations, surgical resection, and chemotherapy. However, there are certain major issues associated with these treatments, like high risk of tumour reoccurrence, drug resistance development, less bioavailability of the drug at target site, rapid drug metabolism and high systemic toxicity due to drug doses. All such limitations of conventional treatments can be overcome with the use of herbal bio-actives as they exhibit less toxicity to normal healthy cells and reduce the risk of tumour recurrence and resistance development. Nano-formulations are developed to aid in targeted drug delivery, and to enhance the solubility, stability, bioavailability, and therapeutic efficacy of phytoconstituents. With the emergence of nanomaterials, different imaging modalities have been integrated into one single platform, and combined therapies with synergetic effects against gastric cancer were established. Moreover, the development of theragnostic strategies with simultaneous diagnostic and therapeutic ability was boosted by multifunctional nanoparticles. The present review discusses about the gastric cancer including its mortality rate, secular trends, pathophysiology, etiology, risk factors, diagnosis, and different treatment approaches with major emphasis on herbal bioactives (quercetin, paclitaxel, resveratrol, curcumin and ginsenosides) and different herbal constituent encapsulated nano-formulations (such as nanoparticles, niosomes, liposomes, nano-emulsion, and micelles). Challenges and future prospects of herbal bioactive encapsulated nano-formulations for the treatment/management of gastric cancers has been included in the later part of the manuscript.

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.

Since carbon dots (CDs)-metal nanoparticles (MNPs) nanocomposites combine the advantages of both carbon quantum dots (CQDs) and MNPs, they show unique properties and are applied in heterogeneous catalysis. In the nanocomposite catalysts, CDs can act as modifiers to modulate the electronic properties of the metals or produce synergy with the metals. Consequently, the nanocomposite catalysts have good catalytic performance. This paper summarizes the preparation methods of nanocomposite catalysts and focuses on their applications in heterogeneous catalysis. Various specific preparation methods are not only summarized as completely as possible but also are also classified at the macro logic level. The applications of the nanocomposite catalysts in heterogeneous catalysis include photocatalysis, sonocatalysis, electrocatalysis, and thermal catalysis. It also reveals how the nanocomposite catalysts produce excellent catalytic performances in various catalytic reactions. Finally, the existing problems and the direction of future efforts are proposed. It is hoped that this paper will provide a slight reference for the future research of MNPs-CQDs nanocomposite catalysts and their application in the field of catalysis.

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.

Conventional methods of detecting hazardous gases and aerated microorganisms were judged unfeasible for use in a point of use environment. The use of a lightweight prototype and an easy fabrication provides significant advantages over conventional gas sensing systems. It would be ideal if scientists could develop relatively small, sensitive gas sensors that could detect trace amounts of biomarker gases and airborne pollutants. In the realm of sensors, microfluidics technology enables the analysis of a small quantity of samples by facilitating the use of a minimum amount of sensor materials. Moreover, the capacity to scrutinise a diminutive sample volume result in a sensor that exhibits prompt responsiveness. However, attaining selectivity towards the target analyte has been a major challenge. With this objective of obtaining specificity in gas sensing, this comprehensive study highlights recent breakthroughs in microfluidic device design and synthesis of sensing materials for selective gas and aerated pollutants. The present review focuses on brief explanation of a microfluidic device design, the substrate material, channel size, shape, deposition, and cleaning methods for synthesis of selective gas sensing materials based on noble metals, semiconductor oxide nanoparticles, and their composites. Further, the gas sensing application of these materials is also discussed in detail. This article is the first to provide an extensive overview of the substrate materials, design fabrication, deposition, and cleaning techniques, microfluidic synthesis of sensing materials for selective gas sensing, and the various detection approaches required for novel and efficient gas sensing analysis using recent microfluidic technology.

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.