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Abstract Mechanochromic photonic crystals are attractive due to their force-dependent structural colors; however, showing unrecordable color and unsatisfied performances, which significantly limits their development and expansion toward advanced applications. Here, a thermal-responsive mechanochromic photonic crystal with a multicolor recordability-erasability was fabricated by combining non-close-packing mechanochromic photonic crystals and phase-change materials. Multicolor recordability is realized by pressing thermal-responsive mechanochromic photonic crystals to obtain target colors over the phase-change temperature followed by fixing the target colors and deformed configuration at room temperature. The stable recorded color can be erased and reconfigured by simply heating and similar color-recording procedures respectively due to the thermoswitchable on-off mechanochromism of thermal-responsive mechanochromic photonic crystals along with solid-gel phase transition. These thermal-responsive mechanochromic photonic crystals are ideal rewritable papers for ink-freely achieving multicolor patterns with high resolution, difficult for conventional photonic papers. This work offers a perspective for designing color-recordable/erasable and other stimulus-switchable materials with advanced applications.

Abstract There has been a growing effort to replace manual extraction of data from research papers with automated data extraction based on natural language processing, language models, and recently, large language models (LLMs). Although these methods enable efficient extraction of data from large sets of research papers, they require a significant amount of up-front effort, expertise, and coding. In this work, we propose the ChatExtract method that can fully automate very accurate data extraction with minimal initial effort and background, using an advanced conversational LLM. ChatExtract consists of a set of engineered prompts applied to a conversational LLM that both identify sentences with data, extract that data, and assure the data’s correctness through a series of follow-up questions. These follow-up questions largely overcome known issues with LLMs providing factually inaccurate responses. ChatExtract can be applied with any conversational LLMs and yields very high quality data extraction. In tests on materials data, we find precision and recall both close to 90% from the best conversational LLMs, like GPT-4. We demonstrate that the exceptional performance is enabled by the information retention in a conversational model combined with purposeful redundancy and introducing uncertainty through follow-up prompts. These results suggest that approaches similar to ChatExtract, due to their simplicity, transferability, and accuracy are likely to become powerful tools for data extraction in the near future. Finally, databases for critical cooling rates of metallic glasses and yield strengths of high entropy alloys are developed using ChatExtract.

Abstract Pronoun usage’s psychological underpinning and behavioral consequence have fascinated researchers, with much research attention paid to second-person pronouns like “you,” “your,” and “yours.” While these pronouns’ effects are understood in many contexts, their role in bilateral, dynamic conversations (especially those outside of close relationships) remains less explored. This research attempts to bridge this gap by examining 25,679 instances of peer review correspondence with Nature Communications using the difference-in-differences method. Here we show that authors addressing reviewers using second-person pronouns receive fewer questions, shorter responses, and more positive feedback. Further analyses suggest that this shift in the review process occurs because “you” (vs. non-“you”) usage creates a more personal and engaging conversation. Employing the peer review process of scientific papers as a backdrop, this research reveals the behavioral and psychological effects that second-person pronouns have in interactive written communications.

Abstract Osteoclasts are the primary target for osteoporosis drug development. Recent animal studies revealed the crucial roles of osteoblasts in regulating osteoclastogenesis and the longer lifespans of osteoclasts than previously thought with fission and recycling. However, existing culture platforms are limited to replicating these newly identified cellular processes. We report a demineralized bone paper (DBP)-based osteoblast culture and osteoclast assay platform that replicates osteoclast fusion, fission, resorption, and apoptosis with high fidelity and analytical power. An osteoid-inspired DBP supports rapid and structural mineral deposition by osteoblasts. Coculture osteoblasts and bone marrow monocytes under biochemical stimulation recapitulate osteoclast differentiation and function. The DBP-based bone model allows longitudinal quantitative fluorescent monitoring of osteoclast responses to bisphosphonate drug, substantiating significantly reducing their number and lifespan. Finally, we demonstrate the feasibility of humanizing the bone model. The DBP-based osteo assay platforms are expected to advance bone remodeling-targeting drug development with improved prediction of clinical outcomes.

Abstract Fluorescence analysis is a fast and sensitive method, and has great potential application in trace detection of environmental toxins. However, many important environmental toxins are non-fluorescent substances, and it is still a challenge to construct a fluorescence detection method for non-fluorescent substances. Here, by means of charge transfer effect and smart molecular imprinting technology, we report a sensitive indirect fluorescent sensing mechanism (IFSM) and microcystin (MC-RR) is selected as a model target. A molecular imprinted thin film is immobilized on the surface of zinc ferrite nanoparticles (ZnFe2O4 NPs) by using arginine, a dummy fragment of MC-RR. By implementation of IFSM on the paper-based microfluidic chip, a versatile platform for the quantitative assay of MC-RR is developed at trace level (the limit of detection of 0.43 μg/L and time of 20 min) in real water samples without any pretreatment. Importantly, the proposed IFSM can be easily modified and extended for the wide variety of species which lack direct interaction with the fluorescent substrate. This work offers the potential possibility to meet the requirements for the on-site analysis and may explore potential applications of molecularly imprinted fluorescent sensors.

Sex differences occur in many neurological and psychiatric diseases, and yet research is not always designed optimally to identify these. Here the authors perform a study of how sex was incorporated into the design and analyses of papers published six journals in neuroscience and psychiatry in 2009 compared with 2019.

Current HCV nucleic acid-based diagnosis is largely performed in centralised laboratories. Here, the authors present a pan-genotypic RNA assay, based on reverse transcriptase loop mediated isothermal amplification and develop a low-cost prototype paper-based lateral flow device for point-of-care use, providing a visually read result within 40 min.

Paper is a promising substrate for flexible and environmentally sustainable electronic devices. Here, the authors combine chemical vapor deposition of MoS2 with inkjet printing of a hexagonal boron nitride (hBN) dielectric and silver electrodes, to fabricate flexible MoS2 field-effect transistors on paper, and then combine the latter with printed graphene resistors and silver interconnects to create inverters, logic gates and current mirrors.

Connecting genotypes to complex social behaviour is challenging. Taylor et al. use machine learning to show a strong response of caste-associated gene expression to queen loss, wherein individual wasp’s expression profiles become intermediate between queen and worker states, even in the absence of behavioural changes.

Strong but flexible graphene tends to wrinkle, which compromises some properties. Here the authors report a solid plasticization method to prepare continuous graphene papers with high crystalline order, achieving high strength, stiffness, electrical and thermal conductivities.

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.

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.

Currently, gut microbiome profiling largely relies on next-generation sequencing, which is slow and expensive. Here, the authors develop a low-cost, paper-based synthetic biology platform that allows species-specific quantification of bacterial mRNAs and clinically relevant host biomarkers.

With ligand-mediated layer-by-layer assembly between metal nanoparticles and small organic molecules, the authors prepare metallic paper electrodes for supercapacitors with high power and energy densities. This approach could be extended to various electrodes for portable/wearable electronics.

In cooperatively breeding species, subordinates help to raise the dominant breeders’ offspring in return for benefits associated with group membership. Here, Grinsted and Field show that the amount of help provided by subordinate paper wasps depends on the availability of alternative nesting options, as predicted by biological market theory.

Most high-loading silicon-based anodes for lithium-ion batteries suffer from low efficiency and volumetric capacity. Here, the authors show that a paper-like electrode of silicon oxycarbide glass and graphene at mass loading of >2 mg cm−2can efficiently deliver high energy density for over 1,000 cycles.

Abstract Advanced design strategies are essential to realize the full potential of engineered living materials, including their biodegradability, manufacturability, sustainability, and ability to tailor functional properties. Toward these goals, we present mechanically engineered living material with compostability, healability, and scalability – a material that integrates these features in the form of a stretchable plastic that is simultaneously flushable, compostable, and exhibits the characteristics of paper. This plastic/paper-like material is produced in scalable quantities (0.5–1 g L−1), directly from cultured bacterial biomass (40%) containing engineered curli protein nanofibers. The elongation at break (1–160%) and Young’s modulus (6-450 MPa) is tuned to more than two orders of magnitude. By genetically encoded covalent crosslinking of curli nanofibers, we increase the Young’s modulus by two times. The designed engineered living materials biodegrade completely in 15–75 days, while its mechanical properties are comparable to petrochemical plastics and thus may find use as compostable materials for primary packaging.

Abstract The lack of data democratization and information leakage from trained models hinder the development and acceptance of robust deep learning-based healthcare solutions. This paper argues that irreversible data encoding can provide an effective solution to achieve data democratization without violating the privacy constraints imposed on healthcare data and clinical models. An ideal encoding framework transforms the data into a new space where it is imperceptible to a manual or computational inspection. However, encoded data should preserve the semantics of the original data such that deep learning models can be trained effectively. This paper hypothesizes the characteristics of the desired encoding framework and then exploits random projections and random quantum encoding to realize this framework for dense and longitudinal or time-series data. Experimental evaluation highlights that models trained on encoded time-series data effectively uphold the information bottleneck principle and hence, exhibit lesser information leakage from trained models.

Abstract Authors of scientific papers are usually encouraged to cite works that meaningfully influenced their research (substantive citations) and avoid citing works that had no meaningful influence (rhetorical citations). Rhetorical citations are assumed to degrade incentives for good work and benefit prominent papers and researchers. Here, we explore if rhetorical citations have some plausibly positive effects for science and disproportionately benefit the less prominent papers and researchers. We developed a set of agent-based models where agents can cite substantively and rhetorically. Agents first choose papers to read based on their expected quality, become influenced by those that are sufficiently good, and substantively cite them. Next, agents fill any remaining slots in their reference lists with rhetorical citations that support their narrative, regardless of whether they were actually influential. We then turned agents’ ability to cite rhetorically on-and-off to measure its effects. Enabling rhetorical citing increased the correlation between paper quality and citations, increased citation churn, and reduced citation inequality. This occurred because rhetorical citing redistributed some citations from a stable set of elite-quality papers to a more dynamic set with high-to-moderate quality and high rhetorical value. Increasing the size of reference lists, often seen as an undesirable trend, amplified the effects. Overall, rhetorical citing may help deconcentrate attention and make it easier to displace established ideas.

On the 12th of April 2010, Nature Communications published its first editorial and primary research articles. The topics of these first 11 papers represented the multidisciplinary nature of the journal: from DNA damage to optics alongside material science to energy and including polymer chemistry . We have spoken with the corresponding authors of some of these very first papers and asked them about their experience of publishing in this then new journal and how they see Nature Communications now.

Abstract Cellulose, as a component of green plants, becomes attractive for fabricating biocompatible flexible functional devices but is plagued by hydrophilic properties, which make it easily break down in water by poor mechanical stability. Here we report a class of SiO2-nanoparticle-decorated bacteria-cellulose meta-skin with superior stability in water, excellent machining property, ultrathin thickness, and active bacteria-repairing capacity. We further develop functional ultrasonic metasurfaces based on meta-skin paper-cutting that can generate intricate patterns of ~10 μm precision. Benefited from the perfect ultrasound insulation of surface Cassie-Baxter states, we utilize meta-skin paper-cutting to design and fabricate ultrathin (~20 μm) and super-light (

Antimicrobial resistance (AMR) occurs when microorganisms (bacteria, viruses, fungi and parasites) no longer respond to antimicrobials, rendering these specific treatments ineffective. Subsequently, this narrows the options for clinical treatment and increases the risk of complications, hospital admissions, and mortality rates. Ultimately, infections become more difficult to treat. The concern of AMR is not new, yet the COVID-19 pandemic has further highlighted this global burden and raised questions regarding the preparedness for the fight against increasing cases of AMR. In a joint collaboration, Nature Communications, Nature Microbiology, Nature Medicine, Communications Medicine and Scientific Reports have launched a Collection and call for papers, inviting submissions of papers that advance our understanding of all aspects of AMR, as outlined in the Collection scope .

Abstract Electrochemical CO2 reduction has emerged as a promising CO2 utilization technology, with Gas Diffusion Electrodes becoming the predominant architecture to maximize performance. Such electrodes must maintain robust hydrophobicity to prevent flooding, while also ensuring high conductivity to minimize ohmic losses. Intrinsic material tradeoffs have led to two main architectures: carbon paper is highly conductive but floods easily; while expanded Polytetrafluoroethylene is flooding resistant but non-conductive, limiting electrode sizes to just 5 cm2. Here we demonstrate a hierarchically conductive electrode architecture which overcomes these scaling limitations by employing inter-woven microscale conductors within a hydrophobic expanded Polytetrafluoroethylene membrane. We develop a model which captures the spatial variability in voltage and product distribution on electrodes due to ohmic losses and use it to rationally design the hierarchical architecture which can be applied independent of catalyst chemistry or morphology. We demonstrate C2+ Faradaic efficiencies of ~75% and reduce cell voltage by as much as 0.9 V for electrodes as large as 50 cm2 by employing our hierarchically conductive electrode architecture.

Abstract Controlling and channeling light emissions from unpolarized quantum dots into specific directions with chiral polarization remains a key challenge in modern photonics. Stacked metasurface designs offer a potential compact solution for chirality and directionality engineering. However, experimental observations of directional chiral radiation from resonant metasurfaces with quantum emitters remain obscure. In this paper, we present experimental observations of unidirectional chiral emission from a twisted bi-layer metasurface via multi-dimensional control, including twist angle, interlayer distance, and lateral displacement between the top and bottom layers, as enabled by doublet alignment lithography (DAL). First, maintaining alignment, the metasurface demonstrates a resonant intrinsic optical chirality with near-unity circular dichroism of 0.94 and reflectance difference of 74%, where a high circular dichroism greater than 0.9 persists across a wide range of angles from −11 to 11 degrees. Second, engineered lateral displacement induces a unidirectional chiral resonance, resulting in unidirectional chiral emission from the quantum dots deposited onto the metasurface. Our bi-layer metasurfaces offer a universal compact platform for efficient radiation manipulation over a wide angular range, promising potential applications in miniaturized lasers, grating couplers, and chiral nanoantennas.

Abstract Heavier group 14 carbene analogues, exhibiting transition-metal-like behavior, display remarkable capability for small molecule activation and coordination chemistry. However, their application in redox catalysis remains elusive. In this paper, we report the synthesis and isolation of a stannylene with carbodiphosphorane ligand. The nucleophilic reactivity at the divalent tin center is elucidated by computational and reactivity studies. Moreover, this stannylene exhibits catalytic activity in the hydrodefluorination reaction of fluoroarenes. Mechanistic investigations into the elementary steps confirm a SnII/SnIV redox cycle involving C–F oxidative addition, F/H ligand metathesis, and C–H reductive elimination. This low-valent SnII catalytic system resembles the classical transition metal catalysis. Notably, this represents metallomimetic redox catalysis utilizing carbene analogue with heavier group 14 element as a catalyst.

Abstract Nitride ferroelectrics have recently emerged as promising alternatives to oxide ferroelectrics due to their compatibility with mainstream semiconductor processing. ScAlN, in particular, has exhibited remarkable piezoelectric coupling strength (K 2) comparable to that of lithium niobate, making it a valuable choice for RF filters in wireless communications. Recently, ScAlN has sparked interest in its use for nanophotonic devices, chiefly due to its large bandgap facilitating operation in blue wavelengths coupled with promises of enhanced nonlinear optical properties such as a large second-order susceptibility (χ (2)). It is still an open question whether ScAlN can outperform oxide ferroelectrics concerning the Pockels effect—an electro-optic coupling extensively utilized in optical communications devices. In this paper, we present a comprehensive theoretical analysis and experimental demonstration of ScAlN’s Pockels effect. Our findings reveal that the electro-optic coupling of ScAlN, despite being weak at low Sc concentration, may be significantly enhanced and exceed LiNbO3 at high levels of Sc doping, which points the direction of continued research efforts to unlock the full potential of ScAlN.

Abstract Pharmaceutical drug dosage forms are critical for ensuring the effective and safe delivery of active pharmaceutical ingredients to patients. However, traditional formulation development often relies on extensive lab and animal experimentation, which can be time-consuming and costly. This manuscript presents a generative artificial intelligence method that creates digital versions of drug products from images of exemplar products. This approach employs an image generator guided by critical quality attributes, such as particle size and drug loading, to create realistic digital product variations that can be analyzed and optimized digitally. This paper shows how this method was validated through two case studies: one for the determination of the amount of material that will create a percolating network in an oral tablet product and another for the optimization of drug distribution in a long-acting HIV inhibitor implant. The results demonstrate that the generative AI method accurately predicts a percolation threshold of 4.2% weight of microcrystalline cellulose and generates implant formulations with controlled drug loading and particle size distributions. Comparisons with real samples reveal that the synthesized structures exhibit comparable particle size distributions and transport properties in release media.

Abstract The electrocatalytic reduction of nitrate toward ammonia under mild conditions addresses many challenges of the Haber-Bosch reaction, providing a sustainable method for ammonia synthesis, yet it is limited by sluggish reduction kinetics and multiple competing reactions. Here, the titanium hydride electrocatalyst is synthesized by electrochemical hydrogenation reconstruction of titanium fiber paper, which achieves a large ammonia yield rate of 83.64 mg h−1 cm−2 and a high Faradaic efficiency of 99.11% with an ampere-level current density of 1.05 A cm−2 at −0.7 V versus the reversible hydrogen electrode. Electrochemical evaluation and kinetic studies indicate that the lattice hydrogen transfer from titanium hydride promotes the electrocatalytic performance of nitrate reduction reaction and the reversible equilibrium reaction between lattice hydrogen and activate hydrogen not only improves the electrocatalytic activity of nitrate reduction reaction but also demonstrates notable catalytic stability. These finding offers a universal design principle for metal hydrides as catalysts for effectively electrochemical ammonia production, highlighting their potential for sustainable ammonia synthesis.

Abstract Creating statistically equivalent virtual microstructures (SEVM) for polycrystalline materials with complex microstructures that encompass multi-modal morphological and crystallographic distributions is a challenging enterprise. Cold spray-formed (CSF) AA7050 alloy containing coarse-grained prior particles and ultra-fine grains (UFG) and additively manufactured (AM) Ti64 alloys with alpha laths in beta substrates. The paper introduces an approach strategically integrating a Generative Adversarial Network (GAN) for multi-modal microstructures with a synthetic microstructure builder DREAM.3D for packing grains conforming to statistics in electron backscatter diffraction (EBSD) maps for generating SEVMs of CSF and AM alloy microstructures. A robust multiscale model is subsequently developed for self-consistent coupling of crystal plasticity finite element model (CPFEM) for coarse-grained crystals with an upscaled constitutive model for UFGs. Sub-volume elements are simulated for efficient computations and their responses are averaged for overall stress-strain response. The methods developed are important for image-based micromechanical modeling that is necessary for microstructure-property relations.

Abstract Accelerating text input in augmentative and alternative communication (AAC) is a long-standing area of research with bearings on the quality of life in individuals with profound motor impairments. Recent advances in large language models (LLMs) pose opportunities for re-thinking strategies for enhanced text entry in AAC. In this paper, we present SpeakFaster, consisting of an LLM-powered user interface for text entry in a highly-abbreviated form, saving 57% more motor actions than traditional predictive keyboards in offline simulation. A pilot study on a mobile device with 19 non-AAC participants demonstrated motor savings in line with simulation and relatively small changes in typing speed. Lab and field testing on two eye-gaze AAC users with amyotrophic lateral sclerosis demonstrated text-entry rates 29–60% above baselines, due to significant saving of expensive keystrokes based on LLM predictions. These findings form a foundation for further exploration of LLM-assisted text entry in AAC and other user interfaces.

Abstract The rapid advancement of Industry 4.0 necessitates close collaboration among material research institutions to accelerate the development of novel materials. However, multi-institutional cooperation faces significant challenges in protecting sensitive data, leading to data silos. Additionally, the heterogeneous and non-independent and identically distributed (non-i.i.d.) nature of material data hinders model accuracy and generalization in collaborative computing. In this paper, we introduce the MatSwarm framework, built on swarm learning, which integrates federated learning with blockchain technology. MatSwarm features two key innovations: a swarm transfer learning method with a regularization term to enhance the alignment of local model parameters, and the use of Trusted Execution Environments (TEE) with Intel SGX for heightened security. These advancements significantly enhance accuracy, generalization, and ensure data confidentiality throughout the model training and aggregation processes. Implemented within the National Material Data Management and Services (NMDMS) platform, MatSwarm has successfully aggregated over 14 million material data entries from more than thirty research institutions across China. The framework has demonstrated superior accuracy and generalization compared to models trained independently by individual institutions.

Abstract Analogous to the condensation of Cooper pairs in superconductors, the Bose–Einstein condensation (BEC) of electron–hole pairs in semiconductors and semimetals leads to an emergence of an exotic ground state — the excitonic insulator state. In this paper, we study the electronic structure of 1T-TiSe2 utilizing angle-resolved photoemission spectroscopy and alkali-metal deposition. Alkali-metal adatoms are deposited in-situ on the sample surface, doping the system with electrons. The conduction bands of 1T-TiSe2 are thereby pushed down below the Fermi energy, which enables us to characterize its temperature dependence with precision. We found that the formation of the charge density wave (CDW) in 1T-TiSe2 at ~ 205 K is accompanied by a significant increase of the band gap, supporting the existence of excitonic pairing in the CDW state of 1T-TiSe2. More importantly, by analyzing the linewidth of the single-particle excitation spectrum, we unveiled an incoherence-to-coherence crossover at 165 K, which could be attributed to a possible exciton condensation that occurs beneath the CDW transition in 1T-TiSe2. Our results not only explain the exotic transport properties of 1T-TiSe2, but also highlight the possible existence of an excitonic condensate in this semiconducting material.

Abstract Cash transfer policies have been widely discussed as mechanisms to curb intergenerational transmission of socioeconomic disadvantage. In this paper, we take advantage of a large casino-funded family transfer program introduced in a Southeastern American Indian Tribe to generate difference-in-difference estimates of the link between children’s cash transfer exposure and third grade math and reading test scores of their offspring. Here we show greater math (0.25 standard deviation [SD], p =.0148, 95% Confidence Interval [CI]: 0.05, 0.45) and reading (0.28 SD, p = .0066, 95% CI: 0.08, 0.49) scores among American Indian students whose mother was exposed ten years longer than other American Indian students to the cash transfer during her childhood (or relative to the non-American Indian student referent group). Exploratory analyses find that a mother’s decision to pursue higher education and delay fertility appears to explain some, but not all, of the relation between cash transfers and children’s test scores. In this rural population, large cash transfers have the potential to reduce intergenerational cycles of poverty-related educational outcomes.

Abstract Low-cost, efficient catalyst high-throughput screening is crucial for future renewable energy technology. Interpretable machine learning is a powerful method for accelerating catalyst design by extracting physical meaning but faces huge challenges. This paper describes an interpretable descriptor model to unify activity and selectivity prediction for multiple electrocatalytic reactions (i.e., O2/CO2/N2 reduction and O2 evolution reactions), utilizing only easily accessible intrinsic properties. This descriptor, named ARSC, successfully decouples the atomic property (A), reactant (R), synergistic (S), and coordination effects (C) on the d-band shape of dual-atom sites, which is built upon our developed physically meaningful feature engineering and feature selection/sparsification (PFESS) method. Driven by this descriptor, we can rapidly locate optimal catalysts for various products instead of over 50,000 density functional theory calculations. The model’s universality has been validated by abundant reported works and subsequent experiments, where Co-Co/Ir-Qv3 are identified as optimal bifunctional oxygen reduction and evolution electrocatalysts. This work opens the avenue for intelligent catalyst design in high-dimensional systems linked with physical insights.

Abstract DNA data storage is a potential alternative to magnetic tape for archival storage purposes, promising substantial gains in information density. Critical to the success of DNA as a storage media is an understanding of the role of environmental factors on the longevity of the stored information. In this paper, we evaluate the effect of exposure to ionizing particle radiation, a cause of data loss in traditional magnetic media, on the longevity of data in DNA data storage pools. We develop a mass action kinetics model to estimate the rate of damage accumulation in DNA strands due to neutron interactions with both nucleotides and residual water molecules, then utilize the model to evaluate the effect several design parameters of a typical DNA data storage scheme have on expected data longevity. Finally, we experimentally validate our model by exposing dried DNA samples to different levels of neutron irradiation and analyzing the resulting error profile. Our results show that particle radiation is not a significant contributor to data loss in DNA data storage pools under typical storage conditions.

Abstract Spatiotemporal datasets, which consist of spatially-referenced time series, are ubiquitous in diverse applications, such as air pollution monitoring, disease tracking, and cloud-demand forecasting. As the scale of modern datasets increases, there is a growing need for statistical methods that are flexible enough to capture complex spatiotemporal dynamics and scalable enough to handle many observations. This article introduces the Bayesian Neural Field (BayesNF), a domain-general statistical model that infers rich spatiotemporal probability distributions for data-analysis tasks including forecasting, interpolation, and variography. BayesNF integrates a deep neural network architecture for high-capacity function estimation with hierarchical Bayesian inference for robust predictive uncertainty quantification. Evaluations against prominent baselines show that BayesNF delivers improvements on prediction problems from climate and public health data containing tens to hundreds of thousands of measurements. Accompanying the paper is an open-source software package ( https://github.com/google/bayesnf ) that runs on GPU and TPU accelerators through the Jax machine learning platform.

Abstract Several gaps persist in haptic device development due to the multifaceted nature of the sense of touch. Existing gaps include challenges enhancing touch feedback fidelity, providing diverse haptic sensations, and ensuring wearability for delivering tactile stimuli to the fingertips. Here, we introduce the Bioinspired Adaptable Multiplanar Haptic system, offering mechanotactile/steady and vibrotactile pulse stimuli with adjustable intensity (up to 298.1 mN) and frequencies (up to 130 Hz). This system can deliver simultaneous stimuli across multiple fingertip areas. The paper includes a full characterisation of our system. As the device can play an important role in further understanding human touch, we performed human stimuli sensitivity and differentiation experiments to evaluate the capability of delivering mechano-vibrotactile, variable intensity, simultaneous, multiplanar and operator agnostic stimuli. Our system promises to accelerate the development of touch perception devices, providing painless, operator-independent data crucial for researching and diagnosing touch-related disorders.

Abstract Increasing the binding affinity of an antibody to its target antigen is a crucial task in antibody therapeutics development. This paper presents a pretrainable geometric graph neural network, GearBind, and explores its potential in in silico affinity maturation. Leveraging multi-relational graph construction, multi-level geometric message passing and contrastive pretraining on mass-scale, unlabeled protein structural data, GearBind outperforms previous state-of-the-art approaches on SKEMPI and an independent test set. A powerful ensemble model based on GearBind is then derived and used to successfully enhance the binding of two antibodies with distinct formats and target antigens. ELISA EC50 values of the designed antibody mutants are decreased by up to 17 fold, and K D values by up to 6.1 fold. These promising results underscore the utility of geometric deep learning and effective pretraining in macromolecule interaction modeling tasks.

Abstract Climate change is projected to have substantial economic, social, and environmental impacts worldwide. Currently, the leading solutions for hydrogen storage are in salt caverns, and depleted natural gas reservoirs. However, the required geological formations are limited to certain regions. To increase alternatives for hydrogen storage, this paper proposes storing hydrogen in pipes filled with gravel in lakes, hydropower, and pumped hydro storage reservoirs. Hydrogen is insoluble in water, non-toxic, and does not threaten aquatic life. Results show the levelized cost of hydrogen storage to be 0.17 USD kg−1 at 200 m depth, which is competitive with other large scale hydrogen storage options. Storing hydrogen in lakes, hydropower, and pumped hydro storage reservoirs increases the alternatives for storing hydrogen and might support the development of a hydrogen economy in the future. The global potential for hydrogen storage in reservoirs and lakes is 3 and 12 PWh, respectively. Hydrogen storage in lakes and reservoirs can support the development of a hydrogen economy in the future by providing abundant and cheap hydrogen storage.

Abstract Today’s wireless environments, soft robotics, and space applications demand delicate design of devices with tunable performances and simple fabrication processes. Here we show strain-based adjustability of RF/microwave performance by applying frequency-selective patterns of conductive Ti3C2T x MXene coatings on low-cost acetate substrates under ambient conditions. The tailored performances were achieved by applying frequency-selective patterns of thin Ti3C2Tx MXene coatings with high electrical conductivity as a replacement to metal on low-cost flexible acetate substrates under ambient conditions. Under quasi-axial stress, the Kirigami design enables displacements of individual resonant cells, changing the overall electromagnetic performance of a surface (i.e., array) within a simulated wireless channel. Two flexible Kirigami-inspired prototypes were implemented and tested within the S, C, and X (2-4 GHz, 4-8 GHz, and 8-12 GHz) microwave frequency bands. The resonant surface, having ~1/4 of the size of a standard A4 paper, was able to steer a beam of scattered waves from each resonator by ~25°. Under a strain of 22%, the resonant frequency of the wired co-planar resonator was shifted by 400 MHz, while the reflection coefficient changed by 158%. Deforming the geometry impacted the spectral response of the components across three arbitrary frequencies in the 4-10 GHz frequency range. With this proof of concept, we anticipate implementing thin films of MXenes on technologically relevant substrates, achieving multi-functionality through cost-effective and straightforward manufacturing.