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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.

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.

Recent advances in genome sequencing and imputation technologies provide an exciting opportunity to comprehensively study the contribution of genetic variants to complex phenotypes. However, our ability to translate genetic discoveries into mechanistic insights remains limited at this point. In this paper, we propose an efficient knockoff-based method, GhostKnockoff, for genome-wide association studies (GWAS) that leads to improved power and ability to prioritize putative causal variants relative to conventional GWAS approaches. The method requires only Z-scores from conventional GWAS and hence can be easily applied to enhance existing and future studies. The method can also be applied to meta-analysis of multiple GWAS allowing for arbitrary sample overlap. We demonstrate its performance using empirical simulations and two applications: (1) a meta-analysis for Alzheimer’s disease comprising nine overlapping large-scale GWAS, whole-exome and whole-genome sequencing studies and (2) analysis of 1403 binary phenotypes from the UK Biobank data in 408,961 samples of European ancestry. Our results demonstrate that GhostKnockoff can identify putatively functional variants with weaker statistical effects that are missed by conventional association tests.

Photoelectrochemical CO2 reduction reaction flow cells are promising devices to meet the requirements to produce solar fuels at the industrial scale. Photoelectrodes with wide bandgaps do not allow for efficient CO2 reduction at high current densities, while the integration of opaque photoelectrodes with narrow bandgaps in flow cell configurations still remains a challenge. This paper describes the design and fabrication of a back-illuminated Si photoanode promoted PEC flow cell for CO2 reduction reaction. The illumination area and catalytic sites of the Si photoelectrode are decoupled, owing to the effective passivation of defect states that allows for the long minority carrier diffusion length, that surpasses the thickness of the Si substrate. Hence, a solar-to-fuel conversion efficiency of CO of 2.42% and a Faradaic efficiency of 90% using Ag catalysts are achieved. For CO2 to C2+ products, the Faradaic efficiency of 53% and solar-to-fuel of 0.29% are achieved using Cu catalyst in flow cell.

The human light modulation response allows humans to perceive objects clearly by receiving the appropriate amount of light from the environment. This paper proposes a biomimetic ocular prosthesis system that mimics the human light modulation response capable of pupil and corneal reflections. First, photoinduced synaptic properties of the quantum dot embedded photonic synapse and its biosimilar signal transmission is confirmed. Subsequently, the pupillary light reflex is emulated by incorporating the quantum dot embedded photonic synapse, electrochromic device, and CMOS components. Moreover, a solenoid-based eyelid is connected to the pupillary light reflex system to emulate the corneal reflex. The proposed ocular prosthesis system represents a platform for biomimetic prosthesis that can accommodate an appropriate amount of stimulus by self-regulating the intensity of external stimuli.

The understanding of how the rhodopsin sequence can be modified to exactly modulate the spectroscopic properties of its retinal chromophore, is a prerequisite for the rational design of more effective optogenetic tools. One key problem is that of establishing the rules to be satisfied for achieving highly fluorescent rhodopsins with a near infrared absorption. In the present paper we use multi-configurational quantum chemistry to construct a computer model of a recently discovered natural rhodopsin, Neorhodopsin, displaying exactly such properties. We show that the model, that successfully replicates the relevant experimental observables, unveils a geometrical and electronic structure of the chromophore featuring a highly diffuse charge distribution along its conjugated chain. The same model reveals that a charge confinement process occurring along the chromophore excited state isomerization coordinate, is the primary cause of the observed fluorescence enhancement.

This paper presents a generalisable method for the calibration of fluorescence readings on microplate readers, in order to convert arbitrary fluorescence units into absolute units. FPCountR relies on the generation of bespoke fluorescent protein (FP) calibrants, assays to determine protein concentration and activity, and a corresponding analytical workflow. We systematically characterise the assay protocols for accuracy, sensitivity and simplicity, and describe an ‘ECmax’ assay that outperforms the others and even enables accurate calibration without requiring the purification of FPs. To obtain cellular protein concentrations, we consider methods for the conversion of optical density to either cell counts or alternatively to cell volumes, as well as examining how cells can interfere with protein counting via fluorescence quenching, which we quantify and correct for the first time. Calibration across different instruments, disparate filter sets and mismatched gains is demonstrated to yield equivalent results. It also reveals that mCherry absorption at 600 nm does not confound cell density measurements unless expressed to over 100,000 proteins per cell. FPCountR is presented as pair of open access tools (protocol and R package) to enable the community to use this method, and ultimately to facilitate the quantitative characterisation of synthetic microbial circuits.

Energy efficiency improvement is often hindered by the energy efficiency gap. This paper examines the effect of short-run temperature fluctuations on the Energy Star air conditioner purchases in the United States from 2006 to 2019 using transaction-level data. Results show that the probability of purchasing an Energy Star air conditioner increases as the weekly temperature before the transaction deviates from 20–22 °C. A larger response is related to fewer cooling degree days in the previous years, higher electricity prices/income/educational levels/age/rate of owners, more common use of electricity, and stronger concern about climate change. 1 °C increase and decrease from 21 °C would lead to a reduction of total energy expenditure by 35.46 and 17.73 million dollars nationwide (0.13% and 0.06% of the annual total energy expenditure on air conditioning), respectively. Our findings have important policy implications for demand-end interventions to incorporate the potential impact of the ambient physical environment.

Low modulus materials that can shape-morph into different three-dimensional (3D) configurations in response to external stimuli have wide-ranging applications in flexible/stretchable electronics, surgical instruments, soft machines and soft robotics. This paper reports a shape-programmable system that exploits liquid metal microfluidic networks embedded in an elastomer matrix, with electromagnetic forms of actuation, to achieve a unique set of properties. Specifically, this materials structure is capable of fast, continuous morphing into a diverse set of continuous, complex 3D surfaces starting from a two-dimensional (2D) planar configuration, with fully reversible operation. Computational, multi-physics modeling methods and advanced 3D imaging techniques enable rapid, real-time transformations between target shapes. The liquid-solid phase transition of the liquid metal allows for shape fixation and reprogramming on demand. An unusual vibration insensitive, dynamic 3D display screen serves as an application example of this type of morphable surface.

Motivated by empirical evidence on the interplay between geography, population density and societal interaction, we propose a generative process for the evolution of social structure in cities. Our analytical and simulation results predict both super-linear scaling of social tie density and information flow as a function of the population. We demonstrate that our model provides a robust and accurate fit for the dependency of city characteristics with city size, ranging from individual-level dyadic interactions (number of acquaintances, volume of communication) to population-level variables (contagious disease rates, patenting activity, economic productivity and crime) without the need to appeal to modularity, specialization, or hierarchy., Early version of this paper was presented in NetSci 2012 as a contributed talk in June 2012. An improved version of this paper is published in Nature Communications in June 2013. It has 14 pages and 5 figures

Ultraviolet colouration is thought to be an important form of signalling in many bird species, yet broad insights regarding the prevalence of ultraviolet plumage colouration and the factors promoting its evolution are currently lacking. In this paper, we develop a image segmentation pipeline based on deep learning that considerably outperforms classical (i.e. non deep learning) segmentation methods, and use this to extract accurate information on whole-body plumage colouration from photographs of >24,000 museum specimens covering >4500 species of passerine birds. Our results demonstrate that ultraviolet reflectance, particularly as a component of other colours, is widespread across the passerine radiation but is strongly phylogenetically conserved. We also find clear evidence in support of the role of light environment in promoting the evolution of ultraviolet plumage colouration, and a weak trend towards higher ultraviolet plumage reflectance among bird species with ultraviolet rather than violet-sensitive visual systems. Overall, our study provides important broad-scale insight into an enigmatic component of avian colouration, as well as demonstrating that deep learning has considerable promise for allowing new data to be brought to bear on long-standing questions in ecology and evolution.

While inequalities in science are common, most efforts to understand them treat scientists as isolated individuals, ignoring the network effects of collaboration. Here, we develop models that untangle the network effects of productivity defined as paper counts, and prominence referring to high-impact publications, of individual scientists from their collaboration networks. We find that gendered differences in the productivity and prominence of mid-career researchers can be largely explained by differences in their coauthorship networks. Hence, collaboration networks act as a form of social capital, and we find evidence of their transferability from senior to junior collaborators, with benefits that decay as researchers age. Collaboration network effects can also explain a large proportion of the productivity and prominence advantages held by researchers at prestigious institutions. These results highlight a substantial role of social networks in driving inequalities in science, and suggest that collaboration networks represent an important form of unequally distributed social capital that shapes who makes what scientific discoveries.

Protective layers are essential for Si-based photocathodes to achieve long-term stability. The conventionally used inorganic protective layers, such as TiO2, need to be free of pinholes to isolate Si from corrosive solution, which demands extremely high-quality deposition techniques. On the other hand, organic hydrophobic protective layers suffer from the trade-off between current density and stability. This paper describes the design and fabrication of a discontinuous hybrid organic protective layer with controllable surface wettability. The underlying hydrophobic layer induces the formation of thin gas layers at the discontinuous pores to isolate the electrolyte from Si substrate, while allowing Pt co-catalyst to contact the electrolyte for water splitting. Meanwhile, the surface of this organic layer is modified with hydrophilic hydroxyl groups to facilitate bubble detachment. The optimized photocathode achieves a stable photocurrent of 35 mA/cm2 for over 110 h with no trend of decay.

Ferroelectrics are considered excellent photocatalytic candidates for solar fuel production because of the unidirectional charge separation and above-gap photovoltage. Nevertheless, the performance of ferroelectric photocatalysts is often moderate. A few studies showed that these types of photocatalysts could achieve overall water splitting. This paper proposes an approach to fabricating interfacial charge-collecting nanostructures on positive and negative domains of ferroelectric, enabling water splitting in ferroelectric photocatalysts. The present study observes efficient accumulations of photogenerated electrons and holes within their thermalization length (~50 nm) around Au nanoparticles located in the positive and negative domains of a BaTiO3 single crystal. Photocatalytic overall water splitting is observed on a ferroelectric BaTiO3 single crystal after assembling oxidation and reduction cocatalysts on the positively and negatively charged Au nanoparticles, respectively. The fabrication of bipolar charge-collecting structures on ferroelectrics to achieve overall water splitting offers a way to utilize the energetic photogenerated charges in solar energy conversion.

Monolayer 2D semiconductors provide an attractive option for valleytronics due to the valley-addressability by helicity-specific light beam. But the short valley lifetime for excitons have hindered potential valleytronic applications. In this paper, we demonstrate a strategy for prolonging the valley lifetime by converting excitons to trions through effective gate control and by taking advantage of much longer valley lifetime for trions than for excitons. In continuous-wave experiments, we found the valley polarization increases as gate voltage is tuned away from the charge neutrality, with the degree of valley polarization increased from near zero to 38 % for excitons and to 33 % for trions. This is the first successful observation of valley-polarization in MoTe2 without a magnetic field. In pump-probe experiments, we found that the intervalley scattering process of excitons is significantly suppressed as gate voltage is tuned away from charge neutrality, with scattering time from 0.85 ps to ~ 2.17 ps. In contrast, the intervalley scattering rate for trions increases due to increased availability of partner charges for trion spin flipping, with scattering time from 1.39 ns down to ~100 ps away from charge neutrality. Interestingly, our results show that, despite the accelerated intervalley scattering, the trion polarization degree increases due to polarized trion generation from the exciton-to-trion conversion overtaking the intervalley trion scatterings. Importantly, the efficient exciton-to-trion conversion changed the dominant depolarization mechanisms. As a result, the valley lifetime is dramatically improved by 1000 times from excitons to trions at the charge neutrality. Our results shed new light into the depolarization dynamics and the interplay of various depolarization channels for excitons and trions and provide an effective strategy for prolonging the valley polarization.

Little is known about the structural basis of prion strains. Here we provide a high (3.0 Å) resolution cryo-electron microscopy-based structure of infectious brain-derived fibrils of the mouse anchorless RML scrapie strain which, like the recently determined hamster 263K strain, has a parallel in-register β-sheet-based core. Several structural motifs are shared between these ex vivo prion strains, including an amino-proximal steric zipper and three β-arches. However, detailed comparisons reveal variations in these shared structural topologies and other features. Unlike 263K and wildtype RML prions, the anchorless RML prions lack glycophosphatidylinositol anchors and are severely deficient in N-linked glycans. Nonetheless, the similarity of our anchorless RML structure to one reported for wildtype RML prion fibrils in an accompanying paper indicates that these post-translational modifications do not substantially alter the amyloid core conformation. This work demonstrates both common and divergent structural features of prion strains at the near-atomic level.

Dislocations are commonly present and important in metals but their effects have not been fully recognized in oxide ceramics. The large strain energy raised by the rigid ionic/covalent bonding in oxide ceramics leads to dislocations with low density (∼106 mm−2), thermodynamic instability and spatial inhomogeneity. In this paper, we report ultrahigh density (∼109 mm−2) of edge dislocations that are uniformly distributed in oxide ceramics with large compositional complexity. We demonstrate the dislocations are progressively and thermodynamically stabilized with increasing complexity of the composition, in which the entropy gain can compensate the strain energy of dislocations. We also find cracks are deflected and bridged with ∼70% enhancement of fracture toughness in the pyrochlore ceramics with multiple valence cations, due to the interaction with enlarged strain field around the immobile dislocations. This research provides a controllable approach to establish ultra-dense dislocations in oxide ceramics, which may open up another dimension to tune their properties.

Aromatic tertiary amines are one of the most important classes of organic compounds in organic chemistry and drug discovery. It is difficult to efficiently construct tertiary amines from primary amines via classical nucleophilic substitution due to consecutive overalkylation. In this paper, we have developed a radical tandem C-N coupling strategy to efficiently construct aromatic tertiary amines from commercially available carboxylic acids and nitroarenes. A variety of aromatic tertiary amines can be furnished in good yields (up to 98%) with excellent functional group compatibility under mild reaction conditions. The use of two different carboxylic acids also allows for the concise synthesis of nonsymmetric aromatic tertiary amines in satisfactory yields. Mechanistic studies suggest the intermediacy of the arylamine–(TPP)Fe(III) species and might provide a possible evidence for an SH2 (bimolecular homolytic substitution) pathway in the critical C-N bond formation step.

The radical chemistry of ynamides has recently drawn the attention of synthetic organic chemists to the construction of various N-heterocyclic compounds. Nevertheless, the ynamide-radical chemistry remains a long-standing challenge for chemists due to its high reactivity, undesirable byproducts, severe inherent regio- and chemoselective problems. Importantly, the ynamide C(sp)-N bond fission remains an unsolved challenge. In this paper, we observe Photoinduced radical trigger regio- and chemoselective ynamide bond fission, structural reshuffling and functionalization of 2-alkynyl-ynamides to prepare synthetically inaccessible/challenging chalcogen-substituted indole derivatives with excellent step/atom economy. The key breakthroughs of this work includes, ynamide bond cleavage, divergent radical precursors, broad scope, easy to handle, larger-scale reactions, generation of multiple bonds (N-C(sp2), C(sp2)-C(sp2), C(sp2)-SO2R/C-SR, and C-I/C-Se/C-H) in a few minutes without photocatalysts, metals, oxidants, additives. Control experiments and 13C-labeling experiments supporting the conclusion that sulfone radicals contribute to ynamide structural reshuffling processes via a radical pathway.

Accurate delineation of individual teeth and alveolar bones from dental cone-beam CT (CBCT) images is an essential step in digital dentistry for precision dental healthcare. In this paper, we present an AI system for efficient, precise, and fully automatic segmentation of real-patient CBCT images. Our AI system is evaluated on the largest dataset so far, i.e., using a dataset of 4,215 patients (with 4,938 CBCT scans) from 15 different centers. This fully automatic AI system achieves a segmentation accuracy comparable to experienced radiologists (e.g., 0.5% improvement in terms of average Dice similarity coefficient), while significant improvement in efficiency (i.e., 500 times faster). In addition, it consistently obtains accurate results on the challenging cases with variable dental abnormalities, with the average Dice scores of 91.5% and 93.0% for tooth and alveolar bone segmentation. These results demonstrate its potential as a powerful system to boost clinical workflows of digital dentistry.

In recent years, there has been an increased interest in continuous monitoring of patients and their Implanted Medical Devices (IMDs) with different wireless technologies such as ultrasounds. This paper demonstrates a high data-rate intrabody communication link based on Lithium Niobate (LN) Piezoelectric Micromachined Ultrasonic Transducers (pMUTs). The properties of the LN allow to activate multiple flexural mode of vibration with only top electrodes. When operating in materials like the human tissue, these modes are merging and forming a large communication bandwidth. Such large bandwidth, up to 400 kHz, allows for a high-data rate communication link for IMDs. Here we demonstrate a full communication link in a tissue phantom with a fabricated LN pMUT array of 225 elements with an area of just 3 by 3 mm square, showing data-rates up to 800 kbits/s, starting from 3.5 cm and going up to 13.5 cm, which covers the vast majority of IMDs.

Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper we discuss recent advances, limitations, and future perspectives of DL on five broad areas: protein structure prediction, protein function prediction, genome engineering, systems biology and data integration, and phylogenetic inference. We discuss each application area and cover the main bottlenecks of DL approaches, such as training data, problem scope, and the ability to leverage existing DL architectures in new contexts. To conclude, we provide a summary of the subject-specific and general challenges for DL across the biosciences.

The New Horizons spacecraft returned images and compositional data showing that terrains on Pluto span a variety of ages, ranging from relatively ancient, heavily cratered areas to very young surfaces with few-to-no impact craters. One of the regions with very few impact craters is dominated by enormous rises with hummocky flanks. Similar features do not exist anywhere else in the imaged solar system. Here we analyze the geomorphology and composition of the features and conclude this region was resurfaced by cryovolcanic processes, of a type and scale so far unique to Pluto. Creation of this terrain requires multiple eruption sites and a large volume of material (>104 km^3) to form what we propose are multiple, several-km-high domes, some of which merge to form more complex planforms. The existence of these massive features suggests Pluto's interior structure and evolution allows for either enhanced retention of heat or more heat overall than was anticipated before New Horizons, which permitted mobilization of water-ice-rich materials late in Pluto's history., 22 pages, including both main paper and supplement as one pdf

Electrochemical reduction of CO2 (CO2ER) has received significant attention due to its potential to sustainably produce valuable fuels and chemicals. However, the reaction mechanism is still not well understood. One vital debate is whether the rate-limiting step (RLS) is dominated by the availability of protons, the conversion of water molecules, or the adsorption of CO2. This paper describes insights into the RLS by investigating pH dependency and kinetic isotope effect with respect to the rate expression of CO2ER. Focusing on electrocatalysts geared towards two-electron transfer reactions, we find the generation rates of CO and formate to be invariant with either pH or deuteration of the electrolyte over Au, Ag, Sn, and In. We elucidate the RLS of two-electron transfer CO2ER to be the adsorption of CO2 onto the surface of electrocatalysts. We expect this finding to provide guidance for improving CO2ER activity through the enhancement of the CO2 adsorption processes by strategies such as surface modification of catalysts as well as careful control of pressure and interfacial electric field within reactors.

In a recent study, Lush et al. [1] claimed that they found substantial relationships between hypnotizability and experimental measures of the rubber hand illusion (RHI) [2] . The authors proposed that hypnotizable participants control their phenomenology to meet task expectations arising from the experimental paradigm. They further suggest that the RHI may or may not be entirely explained by hypnotic suggestions driven by task expectancies and therefore could reflect top-down control of perception, instead of multisensory mechanisms. However, in reanalyzing their data, we observe no significant relationships between hypnotic suggestibility and the RHI when quantified using a control condition in line with standard practice in the field and the authors’ preregistered hypothesis. Furthermore, we note that the relationships that the authors describe are weak and observed for a visual hallucination control experience and in the control condition, indicating a general influence of hypnotizability on cognition, rather than sensations that specifically relate to the RHI. Overall, the results from our analyses and Lush original paper fit well with the view that the RHI is a perceptual illusion driven primarily by multisensory mechanisms.

Sensing of clinically relevant biomolecules such as neurotransmitters at low concentrations can enable an early detection and treatment of a range of diseases. Several nanostructures are being explored by researchers to detect biomolecules at sensitivities beyond the picomolar range. It is recognized, however, that nanostructuring of surfaces alone is not sufficient to enhance sensor sensitivities down to the femtomolar level. In this paper, we break this barrier/limit by introducing a sensing platform that uses a multi-length-scale electrode architecture consisting of 3D printed silver micropillars decorated with graphene nanoflakes and use it to demonstrate the detection of dopamine at a limit-of-detection of 500 attomoles. The graphene provides a high surface area at nanoscale, while micropillar array accelerates the interaction of diffusing analyte molecules with the electrode at low concentrations. The hierarchical electrode architecture introduced in this work opens the possibility of detecting biomolecules at ultralow concentrations., Here, the authors introduce a biosensing platform with multi-length-scale electrode architecture consisting of 3D printed silver micropillars decorated with graphene flakes. They demonstrate that this breaks a barrier to the biomolecule limit-of-detection, enabling detection down to femtomolar level.

Site-specific incorporation of unnatural amino acids (UAAs) with similar incorporation efficiency to that of natural amino acids (NAAs) and low background activity is extremely valuable for efficient synthesis of proteins with diverse new chemical functions and design of various synthetic auxotrophs. However, such efficient translation systems remain largely unknown in the literature. Here, we describe engineered chimeric phenylalanine systems that dramatically increase the yield of proteins bearing UAAs, through systematic engineering of the aminoacyl-tRNA synthetase and its respective cognate tRNA. These engineered synthetase/tRNA pairs allow single-site and multi-site incorporation of UAAs with efficiencies similar to those of NAAs and high fidelity. In addition, using the evolved chimeric phenylalanine system, we construct a series of E. coli strains whose growth is strictly dependent on exogenously supplied of UAAs. We further show that synthetic auxotrophic cells can grow robustly in living mice when UAAs are supplemented., In this paper, the authors report the directed-evolution of translation systems that allow the incorporation of unnatural amino acids with similar efficiency to natural amino acids and the construction of synthetic auxotroph in a generalizable way.

Capabilities for continuous monitoring of pressures and temperatures at critical skin interfaces can help to guide care strategies that minimize the potential for pressure injuries in hospitalized patients or in individuals confined to the bed. This paper introduces a soft, skin-mountable class of sensor system for this purpose. The design includes a pressure-responsive element based on membrane deflection and a battery-free, wireless mode of operation capable of multi-site measurements at strategic locations across the body. Such devices yield continuous, simultaneous readings of pressure and temperature in a sequential readout scheme from a pair of primary antennas mounted under the bedding and connected to a wireless reader and a multiplexer located at the bedside. Experimental evaluation of the sensor and the complete system includes benchtop measurements and numerical simulations of the key features. Clinical trials involving two hemiplegic patients and a tetraplegic patient demonstrate the feasibility, functionality and long-term stability of this technology in operating hospital settings.

The presence of a scattering medium in the imaging path between an object and an observer is known to severely limit the visual acuity of the imaging system. We present an approach to circumvent the deleterious effects of scattering, by exploiting spectral correlations in scattered wavefronts. Our Synthetic Wavelength Holography (SWH) method is able to recover a holographic representation of hidden targets with sub-mm resolution over a nearly hemispheric angular field of view. The complete object field is recorded within 46 ms, by monitoring the scattered light return in a probe area smaller than 6 cm × 6 cm. This unique combination of attributes opens up a plethora of new Non-Line-of-Sight imaging applications ranging from medical imaging and forensics, to early-warning navigation systems and reconnaissance. Adapting the findings of this work to other wave phenomena will help unlock a wider gamut of applications beyond those envisioned in this paper., Here, the authors present Synthetic Wavelength Holography, an approach for Non-Line-of-Sight imaging. By exploiting spectral correlations in scattered light, the authors transform real world surfaces such as walls or scatterers into High-Resolution, Wide-Field-of-View imaging portals that provide holograms of objects obscured from view.

Capabilities for continuous monitoring of pressures and temperatures at critical skin interfaces can help to guide care strategies that minimize the potential for pressure injuries in hospitalized patients or in individuals confined to the bed. This paper introduces a soft, skin-mountable class of sensor system for this purpose. The design includes a pressure-responsive element based on membrane deflection and a battery-free, wireless mode of operation capable of multi-site measurements at strategic locations across the body. Such devices yield continuous, simultaneous readings of pressure and temperature in a sequential readout scheme from a pair of primary antennas mounted under the bedding and connected to a wireless reader and a multiplexer located at the bedside. Experimental evaluation of the sensor and the complete system includes benchtop measurements and numerical simulations of the key features. Clinical trials involving two hemiplegic patients and a tetraplegic patient demonstrate the feasibility, functionality and long-term stability of this technology in operating hospital settings., Uninterrupted monitoring of pressure and temperature at skin interfaces can help to minimize the potential for pressure injuries in hospitalized or bedridden patients. Here, the authors introduce a soft, skin-mountable sensor that can continuously provide readings via antennas mounted under bedding, and demonstrate the functionality and robustness of the devices on patients.

Urbanization and climate change are together exacerbating water scarcity—where water demand exceeds availability—for the world’s cities. We quantify global urban water scarcity in 2016 and 2050 under four socioeconomic and climate change scenarios, and explored potential solutions. Here we show the global urban population facing water scarcity is projected to increase from 933 million (one third of global urban population) in 2016 to 1.693–2.373 billion people (one third to nearly half of global urban population) in 2050, with India projected to be most severely affected in terms of growth in water-scarce urban population (increase of 153–422 million people). The number of large cities exposed to water scarcity is projected to increase from 193 to 193–284, including 10–20 megacities. More than two thirds of water-scarce cities can relieve water scarcity by infrastructure investment, but the potentially significant environmental trade-offs associated with large-scale water scarcity solutions must be guarded against., This paper quantifies global urban water scarcity in 2016 and 2050 and explores potential solutions. One third to nearly half of the global urban population is projected to face water scarcity problems.

The bonding of copper ions to lattice oxygens dictates the activity and selectivity of copper exchanged zeolites. By 17O isotopic labelling of the zeolite framework, in conjunction with advanced EPR methodologies and DFT modelling, we determine the local structure of single site CuII species, we quantify the covalency of the metal-framework bond and we assess how this scenario is modified by the presence of solvating H216O or H217O molecules. This enables to follow the migration of CuII species as a function of hydration conditions, providing evidence for a reversible transfer pathway within the zeolite cage as a function of the water pressure. The results presented in this paper establish 17O EPR as a versatile tool for characterizing metal-oxide interactions in open-shell systems., The identification of catalytically active sites with atomic-scale precision occupies a central place in the theory and practice of heterogeneous catalysis. Here the authors assess the nature of the copper-oxygen bond in a Cu-CHA zeolite and recover the microscopic structure of single-metal sites.

Development of catalytic amide bond-forming methods is important because they could potentially address the existing limitations of classical methods using superstoichiometric activating reagents. In this paper, we disclose an Umpolung amidation reaction of carboxylic acids with nitroarenes and nitroalkanes enabled by the triplet synergistic catalysis of FeI2, P(V)/P(III) and photoredox catalysis, which avoids the production of byproducts from stoichiometric coupling reagents. A wide range of carboxylic acids, including aliphatic, aromatic and alkenyl acids participate smoothly in such reactions, generating structurally diverse amides in good yields (86 examples, up to 97% yield). This Umpolung amidation strategy opens a method to address challenging regioselectivity issues between nucleophilic functional groups, and complements the functional group compatibility of the classical amidation protocols. The synthetic robustness of the reaction is demonstrated by late-stage modification of complex molecules and gram-scale applications., Catalytic amide bond-forming methods is important because they could potentially address the existing limitations of classical methods using superstoichiometric activating reagents. Here the authors show an Umpolung amidation reaction of carboxylic acids with nitroarenes and nitroalkanes enabled by FeI2, P(V)/P(III) and photoredox catalysis that avoids the production of byproducts.

The dynamic evolutions of fluid-mineral systems driving large-scale geochemical transformations in the Earth’s crust remain poorly understood. We observed experimentally that successive sodic and potassic alterations of feldspar can occur via a single self-evolved, originally Na-only, hydrothermal fluid. At 600 °C, 2 kbar, sanidine ((K,Na)AlSi3O8) reacted rapidly with a NaCl fluid to form albite (NaAlSi3O8); over time, some of this albite was replaced by K-feldspar (KAlSi3O8), in contrast to predictions from equilibrium reaction modelling. Fluorine accelerated the process, resulting in near-complete back-replacement of albite within 1 day. These findings reveal that potassic alteration can be triggered by Na-rich fluids, indicating that pervasive sequential sodic and potassic alterations associated with mineralization in some of the world’s largest ore deposits may not necessarily reflect externally-driven changes in fluid alkali contents. Here, we show that these reactions are promoted at the micro-scale by a self-evolving, kinetically-driven process; such positive feedbacks between equilibrium and kinetic factors may be essential in driving pervasive mineral transformations., This paper reveals that potassic alteration can be triggered by Na-rich fluids, indicating that pervasive sequential sodic and potassic alterations associated with mineralization in some of the world’s largest ore deposits may not necessarily reflect externally-driven changes in fluid alkali contents.

Complex dynamical fluctuations, from intracellular noise, brain dynamics or computer traffic display bursting dynamics consistent with a critical state between order and disorder. Living close to the critical point has adaptive advantages and it has been conjectured that evolution could select these critical states. Is this the case of living cells? A system can poise itself close to the critical point by means of the so-called self-organized criticality (SOC). In this paper we present an engineered gene network displaying SOC behaviour. This is achieved by exploiting the saturation of the proteolytic degradation machinery in E. coli cells by means of a negative feedback loop that reduces congestion. Our critical motif is built from a two-gene circuit, where SOC can be successfully implemented. The potential implications for both cellular dynamics and behaviour are discussed., This work was supported by the Botin Foundation by Banco Santander through its Santander Universities Global Division, the Spanish Ministry of Economy and Competitiveness, grant AEI-PID2019-111680GB-I00/AEI/10.13039/501100011033, an AGAUR FI 2018 grant, and the Santa Fe Institute (where the key idea was conceived). J.S. and A.G. have been partially funded by the CERCA Programme of the Generalitat de Catalunya. J.S. acknowledges support from Agencia Estatal de Investigación grant RTI2018-098322-B-I00 and Ramón y Cajal contract RYC-2017-22243. A.G. has been funded by the AGAUR grant 2017-SGR-1049 and by the MINECO-FEDER-UE grants PGC-2018-098676-B-100 and RTI2018-093860-B-C21.

Neuronal activity in sensory cortex fluctuates over time and across repetitions of the same input. This variability is often considered detrimental to neural coding. The theory of neural sampling proposes instead that variability encodes the uncertainty of perceptual inferences. In primary visual cortex (V1), modulation of variability by sensory and non-sensory factors supports this view. However, it is unknown whether V1 variability reflects the statistical structure of visual inputs, as would be required for inferences correctly tuned to the statistics of the natural environment. Here we combine analysis of image statistics and recordings in macaque V1 to show that probabilistic inference tuned to natural image statistics explains the widely observed dependence between spike count variance and mean, and the modulation of V1 activity and variability by spatial context in images. Our results show that the properties of a basic aspect of cortical responses—their variability—can be explained by a probabilistic representation tuned to naturalistic inputs., The neural sampling theory suggests that neuronal variability encodes the uncertainty of probabilistic inferences. This paper shows that response variability in primary visual cortex reflects the statistical structure of visual inputs, as required for inferences correctly tuned to the statistics of the natural environment.

Spin ice systems display a variety of very nontrivial properties, the most striking being the existence in them of magnetic monopoles. Such monopole states can also have nontrivial electric properties: there exist electric dipoles attached to each monopole. A novel situation is encountered in the moment fragmentation (MF) state, in which monopoles and antimonopoles are perfectly ordered, whereas spins themselves remain disordered. We show that such partial ordering strongly modifies the electric activity of such systems: the electric dipoles, which are usually random and dynamic, become paired in the MF state in (d, −d) pairs, thus strongly reducing their electric activity. The electric currents existing in systems with noncoplanar spins are also strongly influenced by MF. We also consider modifications in dipole and current patterns in magnetic textures (domain walls, local defects) and at excitations with nontrivial dynamics in a MF state, which show very rich behaviour and which could in principle allow to control them by electric field., Spin-ices can exhibit a variety of remarkable phenomena, not least the appearance of emergent magnetic monopoles. In this theory paper, Daniel Khomskii shows that in the moment fragmentation state, the electric dipoles associated with magnetic monopoles become ordered, leading to a change in electrical activity.

Microdevice integrating energy storage with wireless charging could create opportunities for electronics design, such as moveable charging. Herein, we report seamlessly integrated wireless charging micro-supercapacitors by taking advantage of a designed highly consistent material system that both wireless coils and electrodes are of the graphite paper. The transferring power efficiency of the wireless charging is 52.8%. Benefitting from unique circuit structure, the intact device displays low resistance and excellent voltage tolerability with a capacitance of 454.1 mF cm−2, superior to state-of-the-art conventional planar micro-supercapacitors. Besides, a record high energy density of 463.1 μWh cm−2 exceeds the existing metal ion hybrid micro-supercapacitors and even commercial thin film battery (350 μWh cm−2). After charging for 6 min, the integrated device reaches up to a power output of 45.9 mW, which can drive an electrical toy car immediately. This work brings an insight for contactless micro-electronics and flexible micro-robotics. Miniaturized energy storage devices integrated with wireless charging bring opportunities for next generation electronics. Here, authors report seamlessly integrated wireless charging micro-supercapacitors with high energy density capable of driving a model electrical car.

A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-22487-4