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The colour of water-jet rewritable paper (WJRP) is difficult to be expanded via single hydrochromic molecule, especially black. Here, inspired by the amazing phenomenon of bound-water in cells enabling various biological transformations via facilitating synergistic inter-/intra-molecular proton transfer, we present a simple strategy toward WJRP based on binary systems containing less-sensitive acidochromic dyes and mild proton donors (or developers). With such a binary system containing commercial black dye as the colouring agent, benzyl 4-hydroxybenzoate as the developer, and biomimetic bound-water as proton-transferring medium, we successfully achieve the long-awaited black WJRP. Printed images on such WJRP have excellent performances and long retaining time (>1 month). In addition, the robustness, durability and reversibility of WJRP could be increased distinctly by using polyethylene terephthalate as substrate. This strategy significantly expands hydrochromic colours to entire visible range in an eco-friendly way, which opens an avenue of smart materials for practical needs and industrialization., Water based inks used for water-jet rewritable paper (WJRP) are an environmental friendly alternative to conventional printing, but black colour in WJRP could not be realized so far. Here the authors demonstrate black as well as other colour WJRP based on binary systems containing less-sensitive acidochromic dyes and mild proton donors.

There is a need for large-scale, longitudinal studies to determine the mechanisms by which the gut microbiome and its interactions with the host affect human health and disease. Current methods for profiling the microbiome typically utilize next-generation sequencing applications that are expensive, slow, and complex. Here, we present a synthetic biology platform for affordable, on-demand, and simple analysis of microbiome samples using RNA toehold switch sensors in paper-based, cell-free reactions. We demonstrate species-specific detection of mRNAs from 10 different bacteria that affect human health and four clinically relevant host biomarkers. We develop a method to quantify mRNA using our toehold sensors and validate our platform on clinical stool samples by comparison to RT-qPCR. We further highlight the potential clinical utility of the platform by showing that it can be used to rapidly and inexpensively detect toxin mRNA in the diagnosis of Clostridium difficile infections., National Institutes of Health (U.S.) (Grant T32-DK007191)

Rewritable paper has recently become prevalent in both academic research and marketplace due to the potential environmental advantages, including forest conservation, pollution reduction, energy saving and resource sustainability. However, its real-life applications are limited by a lack of effective strategy to realize multicolour and water-jet printing on rewritable paper with long legible image-lasting times. Herein, we report an effective strategy to construct rewritable paper based on colour or luminescence switching induced by dynamic metal–ligand coordination. This type of rewritable paper can be conveniently utilized for multicolour water-jet printing by using aqueous solutions containing different metal salts as ink. In addition, the printed images on the water-jet rewritable paper can be retained for a long time (> 6 months), which shows great progress compared to previous work. We believe that this type of rewritable paper could be considered as a prototype for multicolour water-jet printing to meet the practical needs., Rewritable paper is environmentally favourable, but its practical realization is stifled by limited ink colour versatility and poor image retention times. Here, the authors exploit the relatively stable but reversible nature of metal–organic coordination bonds to produce long-lasting and multicoloured inks for rewritable paper.

Today's consumer electronics, such as cell phones, tablets and other portable electronic devices, are typically made of non-renewable, non-biodegradable, and sometimes potentially toxic (for example, gallium arsenide) materials. These consumer electronics are frequently upgraded or discarded, leading to serious environmental contamination. Thus, electronic systems consisting of renewable and biodegradable materials and minimal amount of potentially toxic materials are desirable. Here we report high-performance flexible microwave and digital electronics that consume the smallest amount of potentially toxic materials on biobased, biodegradable and flexible cellulose nanofibril papers. Furthermore, we demonstrate gallium arsenide microwave devices, the consumer wireless workhorse, in a transferrable thin-film form. Successful fabrication of key electrical components on the flexible cellulose nanofibril paper with comparable performance to their rigid counterparts and clear demonstration of fungal biodegradation of the cellulose-nanofibril-based electronics suggest that it is feasible to fabricate high-performance flexible electronics using ecofriendly materials., The rapid evolution of consumer electronics means that out-of-date devices quickly end up in the scrap heap. Here, the authors fabricate electrical components using biodegradable and flexible cellulose nanofibril paper—a natural sustainable resource derived from wood.

Culture contamination, end-product toxicity, and energy efficient product recovery are long-standing bioprocess challenges. To solve these problems, we propose a high-pressure fermentation strategy, coupled with in situ extraction using the abundant and renewable solvent supercritical carbon dioxide (scCO2), which is also known for its broad microbial lethality. Towards this goal, we report the domestication and engineering of a scCO2-tolerant strain of Bacillus megaterium, previously isolated from formation waters from the McElmo Dome CO2 field, to produce branched alcohols that have potential use as biofuels. After establishing induced-expression under scCO2, isobutanol production from 2-ketoisovalerate is observed with greater than 40% yield with co-produced isopentanol. Finally, we present a process model to compare the energy required for our process to other in situ extraction methods, such as gas stripping, finding scCO2 extraction to be potentially competitive, if not superior., End-product toxicity, culture contamination, and energy efficient product recovery are long-standing issues in bioprocessing. Here, the authors address these problems using a fermentation strategy that combines microbial production of branched alcohols with supercritical carbon dioxide extraction.

Electrochemical water splitting is one of the most economical and sustainable methods for large-scale hydrogen production. However, the development of low-cost and earth-abundant non-noble-metal catalysts for the hydrogen evolution reaction remains a challenge. Here we report a two-dimensional coupled hybrid of molybdenum carbide and reduced graphene oxide with a ternary polyoxometalate-polypyrrole/reduced graphene oxide nanocomposite as a precursor. The hybrid exhibits outstanding electrocatalytic activity for the hydrogen evolution reaction and excellent stability in acidic media, which is, to the best of our knowledge, the best among these reported non-noble-metal catalysts. Theoretical calculations on the basis of density functional theory reveal that the active sites for hydrogen evolution stem from the pyridinic nitrogens, as well as the carbon atoms, in the graphene. In a proof-of-concept trial, an electrocatalyst for hydrogen evolution is fabricated, which may open new avenues for the design of nanomaterials utilizing POMs/conducting polymer/reduced-graphene oxide nanocomposites., The development of low-cost and earth-abundant non-noble-metal catalysts for the hydrogen evolution reaction remains a challenge. Here, the authors report and evaluate a catalyst based on a two-dimensional coupled hybrid of molybdenum carbide and reduced graphene oxide.

Graphene/hexagonal boron nitride (h-BN) vertical heterostructures have recently revealed unusual physical properties and new phenomena, such as commensurate-incommensurate transition and fractional quantum hall states featured with Hofstadter's butterfly. Graphene-based devices on h-BN substrate also exhibit high performance owing to the atomically flat surface of h-BN and its lack of charged impurities. To have a clean interface between the graphene and h-BN for better device performance, direct growth of large-area graphene/h-BN heterostructures is of great importance. Here we report the direct growth of large-area graphene/h-BN vertical heterostructures by a co-segregation method. By one-step annealing sandwiched growth substrates (Ni(C)/(B, N)-source/Ni) in vacuum, wafer-scale graphene/h-BN films can be directly formed on the metal surface. The as-grown vertically stacked graphene/h-BN structures are demonstrated by various morphology and spectroscopic characterizations. This co-segregation approach opens up a new pathway for large-batch production of graphene/h-BN heterostructures and would also be extended to the synthesis of other van der Waals heterostructures.

Synthesis and patterning of carbon nanomaterials cost effectively is a challenge in electronic and energy storage devices. Here report a one-step, scalable approach for producing and patterning porous graphene films with 3-dimensional networks from commercial polymer films using a CO2 infrared laser. The sp3-carbon atoms are photothermally converted to sp2-carbon atoms by pulsed laser irradiation. The resulting laser-induced graphene (LIG) exhibits high electrical conductivity. The LIG can be readily patterned to interdigitated electrodes for in-plane microsupercapacitors with specific capacitances of >4 mF·cm−2 and power densities of ~9 mW·cm−2. Theoretical calculations partially suggest that enhanced capacitance may result from LIG’s unusual ultra-polycrystalline lattice of pentagon-heptagon structures. Combined with the advantage of one-step processing of LIG in air from commercial polymer sheets, which would allow the employment of a roll-to-roll manufacturing process, this technique provides a rapid route to polymer-written electronic and energy storage devices.

Reduced graphene oxides (RG-Os) have attracted considerable interest, given their potential applications in electronic and optoelectronic devices and circuits. However, very little is known regarding the chemically induced reduction method of graphene oxide (G-O) in both solution and gas phases, with the exception of the hydrazine-reducing agent, even though it is essential to use the vapour phase for the patterning of hydrophilic G-Os on prepatterned substrates and in situ reduction to hydrophobic RG-Os. In this paper, we report a novel reducing agent system (hydriodic acid with acetic acid (HI-AcOH)) that allows for an efficient, one-pot reduction of a solution-phased RG-O powder and vapour-phased RG-O (VRG-O) paper and thin film. The reducing agent system provided highly qualified RG-Os by mass production, resulting in highly conducting RG-O(HI-AcOH). Moreover, VRG-O(HI-AcOH) paper and thin films were prepared at low temperatures (40 °C) and were found to be applicable to flexible devices. This one-pot method is expected to advance research on highly conducting graphene platelets.

Graphene, exhibiting superior mechanical, thermal, optical and electronic properties, has attracted great interest. Considering it being one-atom-thick, and the reduced mechanical strength at grain boundaries, the fabrication of large-area suspended chemical vapour deposition graphene remains a challenge. Here we report the fabrication of an ultra-thin free-standing carbon nanotube/graphene hybrid film, inspired by the vein-membrane structure found in nature. Such a square-centimetre-sized hybrid film can realize the overlaying of large-area single-layer chemical vapour deposition graphene on to a porous vein-like carbon nanotube network. The vein-membrane-like hybrid film, with graphene suspended on the carbon nanotube meshes, possesses excellent mechanical performance, optical transparency and good electrical conductivity. The ultra-thin hybrid film features an electron transparency close to 90%, which makes it an ideal gate electrode in vacuum electronics and a high-performance sample support in transmission electron microscopy.

Pressure-driven ultrafiltration membranes are important in separation applications. Advanced filtration membranes with high permeance and enhanced rejection must be developed to meet rising worldwide demand. Here we report nanostrand-channelled graphene oxide ultrafiltration membranes with a network of nanochannels with a narrow size distribution (3-5 nm) and superior separation performance. This permeance offers a 10-fold enhancement without sacrificing the rejection rate compared with that of graphene oxide membranes, and is more than 100 times higher than that of commercial ultrafiltration membranes with similar rejection. The flow enhancement is attributed to the porous structure and significantly reduced channel length. An abnormal pressure-dependent separation behaviour is also reported, where the elastic deformation of nanochannels offers tunable permeation and rejection. The water flow through these hydrophilic graphene oxide nanochannels is identified as viscous. This nanostrand-channelling approach is also extendable to other laminate membranes, providing potential for accelerating separation and water-purification processes.

Three-dimensional graphene architectures in the macroworld can in principle maintain all the extraordinary nanoscale properties of individual graphene flakes. However, current 3D graphene products suffer from poor electrical conductivity, low surface area and insufficient mechanical strength/elasticity; the interconnected self-supported reproducible 3D graphenes remain unavailable. Here we report a sugar-blowing approach based on a polymeric predecessor to synthesize a 3D graphene bubble network. The bubble network consists of mono- or few-layered graphitic membranes that are tightly glued, rigidly fixed and spatially scaffolded by micrometre-scale graphitic struts. Such a topological configuration provides intimate structural interconnectivities, freeway for electron/phonon transports, huge accessible surface area, as well as robust mechanical properties. The graphene network thus overcomes the drawbacks of presently available 3D graphene products and opens up a wide horizon for diverse practical usages, for example, high-power high-energy electrochemical capacitors, as highlighted in this work., Three-dimensional graphene offers an ideal sheet-to-sheet connectivity of assembled graphenes, but often suffers from poor electrochemical performance. Wang et al. present a sugar-blowing technique to prepare a 3D graphene, which overcomes such problems and shows potential in supercapacitor applications.

The growth of large-domain single crystalline graphene with the controllable number of layers is of central importance for large-scale integration of graphene devices. Here we report a new pathway to greatly reduce the graphene nucleation density from ~10(6) to 4 nuclei cm(-2), enabling the growth of giant single crystals of monolayer graphene with a lateral size up to 5 mm and Bernal-stacked bilayer graphene with the lateral size up to 300 μm, both the largest reported to date. The formation of the giant graphene single crystals eliminates the grain boundary scattering to ensure excellent device-to-device uniformity and remarkable electronic properties with the expected quantum Hall effect and the highest carrier mobility up to 16,000 cm(2) V(-1) s(-1). The availability of the ultra large graphene single crystals can allow for high-yield fabrication of integrated graphene devices, paving a pathway to scalable electronic and photonic devices based on graphene materials.

The outbreak of novel coronavirus disease (COVID-19) has resulted in changes in productivity and daily life patterns, and as a result electricity consumption (EC) has also shifted. In this paper, we construct estimates of EC changes at the metropolitan level across the continental U.S., including total EC and residential EC during the initial two months of the pandemic. The total and residential data on the state level were broken down into the county level, and then metropolitan level EC estimates were aggregated from the counties included in each metropolitan statistical area (MSA). This work shows that the reduction in total EC is related to the shares of certain industries in an MSA, whereas regardless of the incidence level or economic structure, the residential sector shows a trend of increasing EC across the continental U.S. Since the MSAs account for 86% of the total population and 87% of the total EC of the continental U.S., the analytical result in this paper can provide important guidelines for future social-economic crises.

Chemical reduction of graphene oxide can be used to produce large quantities of reduced graphene oxide for potential application in electronics, optoelectronics, composite materials and energy-storage devices. Here we report a highly efficient one-pot reduction of graphene oxide using a sodium-ammonia solution as the reducing agent. The solvated electrons in sodium-ammonia solution can effectively facilitate the de-oxygenation of graphene oxide and the restoration of π-conjugation to produce reduced graphene oxide samples with an oxygen content of 5.6 wt%. Electrical characterization of single reduced graphene oxide flakes demonstrates a high hole mobility of 123 cm2 Vs−1. In addition, we show that the pre-formed graphene oxide thin film can be directly reduced to form reduced graphene oxide film with a combined low sheet resistance (~350 Ω per square with ~80% transmittance). Our study demonstrates a new, low-temperature solution processing approach to high-quality graphene materials with lowest sheet resistance and highest carrier mobility. The chemical reduction of graphene oxide can provide large quantities of reduced graphene oxide for potential application in electronics and composite materials. Feng et al. report a highly efficient low-temperature one-pot reduction of graphene oxide that uses sodium-ammonia solution as the reducing agent.

Catalytic hydrogenation of graphite has recently attracted renewed attention as a route for nanopatterning of graphene and to produce graphene nanoribbons. These reports show that metallic nanoparticles etch the surface layers of graphite or graphene anisotropically along the crystallographic zig-zag ‹11-20› or armchair ‹10-10› directions. The etching direction can be influenced by external magnetic fields or the supporting substrate. Here we report the subsurface etching of highly oriented pyrolytic graphite by Ni nanoparticles, to form a network of tunnels, as seen by scanning electron microscopy and scanning tunnelling microscopy. In this new nanoporous form of graphite, the top layers bend inward on top of the tunnels, whereas their local density of states remains fundamentally unchanged. Engineered nanoporous tunnel networks in graphite allow for further chemical modification and may find applications in various fields and in fundamental science research.

Graphene and single-walled carbon nanotubes are carbon materials that exhibit excellent electrical conductivities and large specific surface areas. Theoretical work suggested that a covalently bonded graphene/single-walled carbon nanotube hybrid material would extend those properties to three dimensions, and be useful in energy storage and nanoelectronic technologies. Here we disclose a method to bond graphene and single-walled carbon nanotubes seamlessly during the growth stage. The hybrid material exhibits a surface area2,000 m(2) g(-1) with ohmic contact from the vertically aligned single-walled carbon nanotubes to the graphene. Using aberration-corrected scanning transmission electron microscopy, we observed the covalent transformation of sp(2) carbon between the planar graphene and the single-walled carbon nanotubes at the atomic resolution level. These findings provide a new benchmark for understanding the three-dimensional graphene/single-walled carbon nanotube-conjoined materials.

Large single-crystal graphene is highly desired and important for the applications of graphene in electronics, as grain boundaries between graphene grains markedly degrade its quality and properties. Here we report the growth of millimetre-sized hexagonal single-crystal graphene and graphene films joined from such grains on Pt by ambient-pressure chemical vapour deposition. We report a bubbling method to transfer these single graphene grains and graphene films to arbitrary substrate, which is nondestructive not only to graphene, but also to the Pt substrates. The Pt substrates can be repeatedly used for graphene growth. The graphene shows high crystal quality with the reported lowest wrinkle height of 0.8 nm and a carrier mobility of greater than 7,100 cm2 V−1 s−1 under ambient conditions. The repeatable growth of graphene with large single-crystal grains on Pt and its nondestructive transfer may enable various applications., Grain boundaries in graphene degrade its properties, and large single-crystal graphene is desirable for electronic applications of graphene. Gao et al. develop a method to produce millimetre-sized hexagonal single-crystal graphene grains, and films composed of the grains, on platinum by chemical vapour deposition.

Large-area graphene films are best synthesized via chemical vapour and/or solid deposition methods at elevated temperatures (similar to 1,000 degrees C) on polycrystalline metal surfaces and later transferred onto other substrates for device applications. Here we report a new method for the synthesis of graphene films directly on SiO2/Si substrates, even plastics and glass at close to room temperature (25-160 degrees C). In contrast to other approaches, where graphene is deposited on top of a metal substrate, our method invokes diffusion of carbon through a diffusion couple made up of carbon-nickel/substrate to form graphene underneath the nickel film at the nickel-substrate interface. The resulting graphene layers exhibit tunable structural and optoelectronic properties by nickel grain boundary engineering and show micrometre-sized grains on SiO2 surfaces and nanometre-sized grains on plastic and glass surfaces. The ability to synthesize graphene directly on non-conducting substrates at low temperatures opens up new possibilities for the fabrication of multiple nanoelectronic devices.

In this paper, we propose a multimodal flexible sensory interface for interactively teaching soft robots to perform skilled locomotion using bare human hands. First, we develop a flexible bimodal smart skin (FBSS) based on triboelectric nanogenerator and liquid metal sensing that can perform simultaneous tactile and touchless sensing and distinguish these two modes in real time. With the FBSS, soft robots can react on their own to tactile and touchless stimuli. We then propose a distance control method that enabled humans to teach soft robots movements via bare hand-eye coordination. The results showed that participants can effectively teach a self-reacting soft continuum manipulator complex motions in three-dimensional space through a “shifting sensors and teaching” method within just a few minutes. The soft manipulator can repeat the human-taught motions and replay them at different speeds. Finally, we demonstrate that humans can easily teach the soft manipulator to complete specific tasks such as completing a pen-and-paper maze, taking a throat swab, and crossing a barrier to grasp an object. We envision that this user-friendly, non-programmable teaching method based on flexible multimodal sensory interfaces could broadly expand the domains in which humans interact with and utilize soft robots.

Generating sustainable electricity from ambient humidity and natural evaporation has attracted tremendous interest recently as it requires no extra mechanical energy input and is deployable across all weather and geography conditions. Here, we present a device prototype for enhanced power generation from ambient humidity. This prototype uses both heterogenous materials assembled from a LiCl-loaded cellulon paper to facilitate moisture adsorption and a carbon-black-loaded cellulon paper to promote water evaporation. Exposing such a centimeter-sized device to ambient humidity can produce voltages of around 0.78 V and a current of around 7.5 μA, both of which can be sustained for more than 10 days. The enhanced electric output and durability are due to the continuous water flow that is directed by evaporation through numerous, negatively charged channels within the cellulon papers. The voltage and current exhibit an excellent scaling behavior upon device integration to sufficiently power commercial devices including even cell phones. The results open a promising prospect of sustainable electricity generation based on a synergy between spontaneous moisture adsorption and water evaporation.

The EU emissions trading system's (ETS) invalidation rule implies that shocks and overlapping policies can change cumulative carbon emissions. This paper explains these mechanisms and simulates the effect of COVID-19, the European Green Deal, and the recovery stimulus package on cumulative EU ETS emissions and allowance prices. Our results indicate that the negative demand shock of the pandemic should have a limited effect on allowance prices and rather translates into lower cumulative carbon emissions. Aligning EU ETS with the 2030 reduction target of -55% might increase allowance prices to 45-94 euro/ton CO2 today and reduce cumulative carbon emissions to 14.2-18.3 GtCO(2) compared to 23.5-33.1 GtCO(2) under a -40% 2030 reduction target. Our results crucially depend on when the waterbed will be sealed again, which is an endogenous market outcome, driven by the EU ETS design, shocks and overlapping climate policies such as the recovery plan. This paper finds that the EU's 2030 reduction target of -55% might correspond to EU ETS allowance prices between 45 and 94 e/ton CO2 today, while the invalidation rule reduces carbon emissions to 14.2 to 18.3 GtCO2 over the EU ETS' remaining lifetime.

Kirigami, a traditional paper cutting art, offers a promising strategy for 2D-to-3D shape morphing through cut-guided deformation. Existing kirigami designs for target 3D curved shapes rely on intricate cut patterns in thin sheets, making the inverse design challenging. Motivated by the Gauss-Bonnet theorem that correlates the geodesic curvature along the boundary with the Gaussian curvature, here, we exploit programming the curvature of cut boundaries rather than the complex cut patterns in kirigami sheets for target 3D curved morphologies through both forward and inverse designs. The strategy largely simplifies the inverse design. Leveraging this strategy, we demonstrate its potential applications as a universal and nondestructive gripper for delicate objects, including live fish, raw egg yolk, and a human hair, as well as dynamically conformable heaters for human knees. This study opens a new avenue to encode boundary curvatures for shape-programing materials with potential applications in soft robotics and wearable devices., Kirigami, a traditional paper cutting art, offers a promising strategy for 2D-to-3D shape morphing through cut-guided deformation. Here, authors report a simple strategy of cut boundary curvature-guided 3D shape morphing and its applications in non-destructive grippers and dynamically conformable heaters.

Mixing theoretical topological structures with cutting-edge simulation methods, a recent study in Nature Communications has finally confirmed the existence of topological defects in glasses and their crucial role for plasticity., Comment: News and Views commentary for the paper by Wu et Al. [Nat Commun 14, 2955 (2023)]

Sintering theory predicts no long-range mass transport or distortion for uniformly heated particles during particle coalescence. However, in sintering-based manufacturing processes, permanent part distortion is often observed. The driving forces and mechanisms leading to this phenomenon are not understood, and efforts to reduce distortion are largely limited to a trial-and-error approach. In this paper, we demonstrate that distortion during sintering results from mass-transport driven by nonhomogeneous temperature distribution. We then show that hitherto unknown mass transport mechanisms, working in the direction opposite to temperature gradient are the likely cause of distortion. The experimental setup, designed for this purpose, enables the quantification of distortion during sintering. Two possible mass transport mechanisms are defined, and the continuum model applicable to both is formulated. The model accurately predicts the transient and permanent distortion observed during experiments, including their size dependence. Methods to control distortion that can give rise to 4D printing are discussed.

Retrosynthesis analysis is an important task in organic chemistry with numerous industrial applications. Previously, machine learning approaches employing natural language processing techniques achieved promising results in this task by first representing reactant molecules as strings and subsequently predicting reactant molecules using text generation or machine translation models. Chemists cannot readily derive useful insights from traditional approaches that rely largely on atom-level decoding in the string representations, because human experts tend to interpret reactions by analyzing substructures that comprise a molecule. It is well-established that some substructures are stable and remain unchanged in reactions. In this paper, we developed a substructure-level decoding model, where commonly preserved portions of product molecules were automatically extracted with a fully data-driven approach. Our model achieves improvement over previously reported models, and we demonstrate that its performance can be boosted further by enhancing the accuracy of these substructures. Analyzing substructures extracted from our machine learning model can provide human experts with additional insights to assist decision-making in retrosynthesis analysis.

Chemical bonds, including covalent and ionic bonds, endow semiconductors with stable electronic configurations but also impose constraints on their synthesis and lattice-mismatched heteroepitaxy. Here, the unique multi-scale van der Waals (vdWs) interactions are explored in one-dimensional tellurium (Te) systems to overcome these restrictions, enabled by the vdWs bonds between Te atomic chains and the spontaneous misfit relaxation at quasi-vdWs interfaces. Wafer-scale Te vdWs nanomeshes composed of self-welding Te nanowires are laterally vapor grown on arbitrary surfaces at a low temperature of 100 °C, bringing greater integration freedoms for enhanced device functionality and broad applicability. The prepared Te vdWs nanomeshes can be patterned at the microscale and exhibit high field-effect hole mobility of 145 cm2/Vs, ultrafast photoresponse below 3 μs in paper-based infrared photodetectors, as well as controllable electronic structure in mixed-dimensional heterojunctions. All these device metrics of Te vdWs nanomesh electronics are promising to meet emerging technological demands.

Metastable noble metal nanocrystals may exhibit distinctive catalytic properties to address the sluggish kinetics of many important processes, including the hydrogen evolution reaction under alkaline conditions for water-electrolysis hydrogen production. However, the exploration of metastable noble metal nanocrystals is still in its infancy and suffers from a lack of sufficient synthesis and electronic engineering strategies to fully stimulate their potential in catalysis. In this paper, we report a synthesis of metastable hexagonal Pt nanostructures by coherent growth on 3d transition metal nanocrystals such as Ni without involving galvanic replacement reaction, which expands the frontier of the phase-replication synthesis. Unlike noble metal substrates, the 3d transition metal substrate owns more crystal phases and lower cost and endows the hexagonal Pt skin with substantial compressive strains and programmable charge density, making the electronic properties particularly preferred for the alkaline hydrogen evolution reaction. The energy barriers are greatly reduced, pushing the activity to 133 mA cmgeo–2 and 17.4 mA μgPt–1 at –70 mV with 1.5 µg of Pt in 1 M KOH. Our strategy paves the way for metastable noble metal catalysts with tailored electronic properties for highly efficient and cost-effective energy conversion.

Data availability: The data generated in this study and used in Figs. 1, 3 and 4 and Supplementary Figures are provided in the Source Data folder. Source data are provided with this paper online at https://www.nature.com/articles/s41467-023-37728-x#Sec27.. Code availability: The code can be found at https://github.com/LisaOJWinkler/UTPM. Copyright © The Author(s) 2023. The growing urban transport sector presents towns and cities with an escalating challenge in the reduction of their greenhouse gas emissions. Here we assess the effectiveness of several widely considered policy options (electrification, light-weighting, retrofitting, scrapping, regulated manufacturing standards and modal shift) in achieving the transition to sustainable urban mobility in terms of their emissions and energy impact until 2050. Our analysis investigates the severity of actions needed to comply with Paris compliant regional sub-sectoral carbon budgets. We introduce the Urban Transport Policy Model (UTPM) for passenger car fleets and use London as an urban case study to show that current policies are insufficient to meet climate targets. We conclude that, as well as implementation of emission-reducing changes in vehicle design, a rapid and large-scale reduction in car use is necessary to meet stringent carbon budgets and avoid high energy demand. Yet, without increased consensus in sub-national and sectoral carbon budgets, the scale of reduction necessary stays uncertain. Nevertheless, it is certain we need to act urgently and intensively across all policy mechanisms available as well as developing new policy options. We acknowledge the support of the Climate Compatible Growth programme, which is funded by UK aid from the UK government. However, the views expressed herein do not necessarily reflect the UK government’s official policies. L.W. and O.B. acknowledge Grantham Institute - Climate Change and the Environment at Imperial College London for their support. J.N. thanks the Royal Society for award of a Research Professorship. D.P. and J.N. acknowledge the support of the UKRI’s Strategic Priorities Fund. D.P., O.B. and J.N. acknowledge funding from Imperial’s EPSRC Impact Acceleration Account (award number EP/R511547/1) and funding from EPSRC (award number EP/T028513/1).

Diversity of physical encounters in urban environments is known to spur economic productivity while also fostering social capital. However, mobility restrictions during the pandemic have forced people to reduce urban encounters, raising questions about the social implications of behavioral changes. In this paper, we study how individual income diversity of urban encounters changed during the pandemic, using a large-scale, privacy-enhanced mobility dataset of more than one million anonymized mobile phone users in Boston, Dallas, Los Angeles, and Seattle, across three years spanning before and during the pandemic. We find that the diversity of urban encounters has substantially decreased (by 15% to 30%) during the pandemic and has persisted through late 2021, even though aggregated mobility metrics have recovered to pre-pandemic levels. Counterfactual analyses show that behavioral changes including lower willingness to explore new places further decreased the diversity of encounters in the long term. Our findings provide implications for managing the trade-off between the stringency of COVID-19 policies and the diversity of urban encounters as we move beyond the pandemic.

Magnetoelectric coupling, as a fundamental physical nature and with the potential to add functionality to devices while also reducing energy consumption, has been challenging to be probed in freestanding membranes or two-dimensional materials due to their instability and fragility. In this paper, we report a magnetoelectric coupling probed by optical second harmonic generation with external magnetic field, and show the manipulation of the ferroelectric and antiferromagnetic orders by the magnetic and thermal fields in BiFeO3 films epitaxially grown on the substrates and in the freestanding ones. Here we define an optical magnetoelectric-coupling constant, denoting the ability of controlling light-induced nonlinear polarization by the magnetic field, and found the magnetoelectric-coupling was suppressed by strain releasing but remain robust against thermal fluctuation for freestanding BiFeO3.

For simulation to be an effective tool for the development and testing of autonomous vehicles, the simulator must be able to produce realistic safety-critical scenarios with distribution-level accuracy. However, due to the high dimensionality of real-world driving environments and the rarity of long-tail safety-critical events, how to achieve statistical realism in simulation is a long-standing problem. In this paper, we develop NeuralNDE, a deep learning-based framework to learn multi-agent interaction behavior from vehicle trajectory data, and propose a conflict critic model and a safety mapping network to refine the generation process of safety-critical events, following real-world occurring frequencies and patterns. The results show that NeuralNDE can achieve both accurate safety-critical driving statistics (e.g., crash rate/type/severity and near-miss statistics, etc.) and normal driving statistics (e.g., vehicle speed/distance/yielding behavior distributions, etc.), as demonstrated in the simulation of urban driving environments. To the best of our knowledge, this is the first time that a simulation model can reproduce the real-world driving environment with statistical realism, particularly for safety-critical situations.

The elucidation of transition state (TS) structures is essential for understanding the mechanisms of chemical reactions and exploring reaction networks. Despite significant advances in computational approaches, TS searching remains a challenging problem owing to the difficulty of constructing an initial structure and heavy computational costs. In this paper, a machine learning (ML) model for predicting the TS structures of general organic reactions is proposed. The proposed model derives the interatomic distances of a TS structure from atomic pair features reflecting reactant, product, and linearly interpolated structures. The model exhibits excellent accuracy, particularly for atomic pairs in which bond formation or breakage occurs. The predicted TS structures yield a high success ratio (93.8%) for quantum chemical saddle point optimizations, and 88.8% of the optimization results have energy errors of less than 0.1 kcal mol−1. Additionally, as a proof of concept, the exploration of multiple reaction paths of an organic reaction is demonstrated based on ML inferences. I envision that the proposed approach will aid in the construction of initial geometries for TS optimization and reaction path exploration.

Recent years have seen the rapid growth of new approaches to optical imaging, with an emphasis on extracting three-dimensional (3D) information from what is normally a two-dimensional (2D) image capture. Perhaps most importantly, the rise of computational imaging enables both new physical layouts of optical components and new algorithms to be implemented. This paper concerns the convergence of two advances: the development of a transparent focal stack imaging system using graphene photodetector arrays, and the rapid expansion of the capabilities of machine learning including the development of powerful neural networks. This paper demonstrates 3D tracking of point-like objects with multilayer feedforward neural networks and the extension to tracking positions of multi-point objects. Computer simulations further demonstrate how this optical system can track extended objects in 3D, highlighting the promise of combining nanophotonic devices, new optical system designs, and machine learning for new frontiers in 3D imaging., Transparent photodetectors based on graphene stacked vertically along the optical axis have shown promising potential for light field reconstruction. Here, the authors develop transparent photodetector arrays and implement a neural network for real-time 3D optical imaging and object tracking.

The temperature scales of screening of local magnetic and orbital moments are important characteristics of strongly correlated substances. In a recent paper X. Deng et al. using dynamic mean-field theory (DMFT) have identified temperature scales of the onset of screening in orbital and spin channels in some correlated metals from the deviation of temperature dependence of local susceptibility from the Curie law. We argue that the scales obtained this way are in fact much larger, than the corresponding Kondo temperatures, and, therefore, do not characterize the screening process. By reanalyzing the results of this paper we find the characteristic (Kondo) temperatures for screening in the spin channel $T_K\approx 100$ K for V$_2$O$_3$ and $T_K\approx 350$ K for Sr$_2$RuO$_4$, which are almost an order of magnitude smaller than those for the onset of the screening estimated in the paper ($1000$ K and $2300$ K, respectively); for V$_2$O$_3$ the obtained temperature scale $T_K$ is therefore comparable to the temperature of completion of the screening, $T^{\rm comp}\sim 25$ K, which shows that the screening in this material can be described in terms of a single temperature scale., This is a post-peer-review, pre-copyedit version of an article published in Matters Arising section of Nature Communications. The final authenticated version is available online at http://dx.doi.org/10.1038/s41467-021-21641-2

Soft, wireless physiological sensors that gently adhere to the skin are capable of continuous clinical-grade health monitoring in hospital and/or home settings, of particular value to critically ill infants and other vulnerable patients, but they present risks for injury upon thermal failure. This paper introduces an active materials approach that automatically minimizes such risks, to complement traditional schemes that rely on integrated sensors and electronic control circuits. The strategy exploits thin, flexible bladders that contain small volumes of liquid with boiling points a few degrees above body temperature. When the heat exceeds the safe range, vaporization rapidly forms highly effective, thermally insulating structures and delaminates the device from the skin, thereby eliminating any danger to the skin. Experimental and computational thermomechanical studies and demonstrations in a skin-interfaced mechano-acoustic sensor illustrate the effectiveness of this simple thermal safety system and suggest its applicability to nearly any class of skin-integrated device technology.

Reconfigurable electromagnetic devices, specifically reconfigurable antennas, have shown to be integral to the future of communication systems. However, mechanically robust designs that can survive real-world, harsh environment applications and high-power conditions remain rare to this day. In this paper, the general framework for a field of both discrete and continuously mechanically reconfigurable devices is established by combining compliant mechanisms with electromagnetics. To exemplify this new concept, a reconfigurable compliant mechanism antenna is demonstrated which exhibits continuously tunable performance across a broad band of frequencies. Moreover, three additional examples are also introduced that further showcase the versatility and advanced capabilities of compliant mechanism enabled electromagnetic devices. Unlike previous approaches, this is achieved with minimal part counts, additive manufacturing techniques, and high reliability, which mechanical compliant mechanism devices are known for. The results presented exemplify how compliant mechanisms have the capacity to transform the broader field of reconfigurable electromagnetic devices.

Dimension reduction and data visualization aim to project a high-dimensional dataset to a low-dimensional space while capturing the intrinsic structures in the data. It is an indispensable part of modern data science, and many dimensional reduction and visualization algorithms have been developed. However, different algorithms have their own strengths and weaknesses, making it critically important to evaluate their relative performance for a given dataset, and to leverage and combine their individual strengths. In this paper, we propose an efficient spectral method for assessing and combining multiple visualizations of a given dataset produced by diverse algorithms. The proposed method provides a quantitative measure -- the visualization eigenscore -- of the relative performance of the visualizations for preserving the structure around each data point. Then it leverages the eigenscores to obtain a consensus visualization, which has much improved { quality over the individual visualizations in capturing the underlying true data structure.} Our approach is flexible and works as a wrapper around any visualizations. We analyze multiple simulated and real-world datasets from diverse applications to demonstrate the effectiveness of the eigenscores for evaluating visualizations and the superiority of the proposed consensus visualization. Furthermore, we establish rigorous theoretical justification of our method based on a general statistical framework, yielding fundamental principles behind the empirical success of consensus visualization along with practical guidance., Under revision of Nature Communications

Dimensionless numbers and scaling laws provide elegant insights into the characteristic properties of physical systems. Classical dimensional analysis and similitude theory fail to identify a set of unique dimensionless numbers for a highly multi-variable system with incomplete governing equations. This paper introduces a mechanistic data-driven approach that embeds the principle of dimensional invariance into a two-level machine learning scheme to automatically discover dominant dimensionless numbers and governing laws (including scaling laws and differential equations) from scarce measurement data. The proposed methodology, called dimensionless learning, is a physics-based dimension reduction technique. It can reduce high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless parameters, greatly simplifying complex process design and system optimization. We demonstrate the algorithm by solving several challenging engineering problems with noisy experimental measurements (not synthetic data) collected from the literature. Examples include turbulent Rayleigh-Bénard convection, vapor depression dynamics in laser melting of metals, and porosity formation in 3D printing. Lastly, we show that the proposed approach can identify dimensionally homogeneous differential equations with dimensionless number(s) by leveraging sparsity-promoting techniques.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.