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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.

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. Conventional electrochemical CO2 conversion electrodes are bound by a tradeoff which prevents electrodes from being both stable and scalable. Here the authors develop a composite electrode which achieves both, enabling scaling to a 50 cm2 electrode with low ohmic losses. [ABSTRACT FROM AUTHOR]

Solution-processable 2D semiconductor inks based on electrochemical molecular intercalation and exfoliation of bulk layered crystals using organic cations has offered an alternative pathway to low-cost fabrication of large-area flexible and wearable electronic devices. However, the growth of large-piece bulk crystals as starting material relies on costly and prolonged high-temperature process, representing a critical roadblock towards practical and large-scale applications. Here we report a general liquid-metal-assisted approach that enables the electrochemical molecular intercalation of low-cost and readily available crystal powders. The resulted solution-processable MoS2 nanosheets are of comparable quality to those exfoliated from bulk crystals. Furthermore, this method can create a rich library of functional 2D electronic inks (>50 types), including 2D wide-bandgap semiconductors of low electrical conductivity. Lastly, we demonstrated the all-solution-processable integration of 2D semiconductors with 2D conductors and 2D dielectrics for the fabrication of large-area thin-film transistors and memristors at a greatly reduced cost. Electrochemical molecular intercalation and exfoliation are established methods to obtain 2D semiconductor inks, but they usually require costly bulk layered crystals as starting materials. Here, the authors report a facile and general liquid-metal-assisted method to synthesize >50 types of 2D material inks from low-cost crystal powders. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Molecular diagnostics for crop diseases can guide the precise application of pesticides, thereby reducing pesticide usage while improving crop yield, but tools are lacking. Here, we report an in-field molecular diagnostic tool that uses a cheap colorimetric paper and a smartphone, allowing multiplexed, low-cost, rapid detection of crop pathogens. Rapid nucleic acid amplification-free detection of pathogenic RNA is achieved by combining toehold-mediated strand displacement with a metal ion-mediated urease catalysis reaction. We demonstrate multiplexed detection of six wheat pathogenic fungi and an early detection of wheat stripe rust. When coupled with a microneedle for rapid nucleic acid extraction and a smartphone app for results analysis, the sample-to-result test can be completed in ~10 min in the field. Importantly, by detecting fungal RNA and mutations, the approach allows to distinguish viable and dead pathogens and to sensitively identify mutation-carrying fungicide-resistant isolates, providing fundamental information for precision crop disease management. On-site crop disease diagnostics is critical for precise application of pesticides. Here, the authors report an in-field molecular diagnostic tool for wheat pathogens using a nucleic acid amplification-free, gene mutation-resolved and smartphone-integrated genetic assay. [ABSTRACT FROM AUTHOR]

Nature Communications published a correction notice for an article titled "A library of 2D electronic material inks synthesized by liquid-metal-assisted intercalation of crystal powders." The correction added a reference to previous work in 'Sci. Adv. 7, eabe3767 (2021)' that was initially omitted. The correction has been made in both the PDF and HTML versions of the article. [Extracted from the article]

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. Advanced design strategies are required for increased control of favourable characteristics of Engineered Living Materials. Here, the authors report the development of a material that has plastic-like stretchability and paper-like compostability and manufacturability. [ABSTRACT FROM AUTHOR]

Inspired by the natural shape-morphing abilities of biological organisms, we introduce a strategy for creating energy-efficient dynamic 3D metasurfaces through spatiotemporal jamming of interleaved assemblies. Our approach, diverging from traditional shape-morphing techniques reliant on continuous energy inputs, utilizes strategically jammed, paper-based interleaved assemblies. By rapidly altering their stiffness at various spatial points and temporal phases during the relaxation of the soft substrate through jamming, we enable the formation of refreshable, intricate 3D shapes with a desirable load-bearing capability. This process, which does not require ongoing energy consumption, ensures energy-efficient and lasting shape displays. Our theoretical model, linking buckling deformation to residual pre-strain, underpins the inverse design process for an array of interleaved assemblies, facilitating the creation of diverse 3D configurations. This metasurface holds notable potential for tactile displays, particularly for the visually impaired, heralding possibilities in visual impaired education, haptic feedback, and virtual/augmented reality applications. This paper introduces a load-bearing 3D dynamic metasurface that alters the stiffness of interleaved assemblies at various spatial points and temporal phases through jamming. This approach does not require continuous energy input and was demonstrated as a tactile display for the visually impaired. [ABSTRACT FROM AUTHOR]

This addendum to a research paper published in Nature Communications discusses a correction to the data presented in the original paper. The authors discovered an undersampling issue in the optical imaging of Fig. 2C, which led to unreliable results regarding the propagation velocity of Min protein patterns. The originally chosen frame rate was sufficient for capturing the velocity of patterns formed by MinE-wildtype, but not for patterns formed by MinE-L3E/I24N. The authors provide new data and analysis to support their conclusions and acknowledge the implications for their previous modeling. Further experimental and theoretical studies are needed to fully understand the observed discrepancies. [Extracted from the article]

This document is a reply to a comment made by Hartich and Godec on a previous paper. The authors clarify that the counterexample presented by Hartich and Godec does not invalidate their results but raises questions about the effect of coarse-graining on irreversibility. They explain that their equation is an exact expression and does not need to be tested. They also discuss the condition for their claim to be valid and acknowledge that their paper could have been clearer in its presentation. The authors provide additional explanations and justifications for their work. [Extracted from the article]

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. Individuals with severe motor impairments use gaze to type and communicate. This paper presents a large language model-based user interface that enables gaze typing in highly abbreviated forms, achieving significant motor saving and speed gain. [ABSTRACT FROM AUTHOR]

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. This paper integrates GANs with the DREAM.3D code to generate virtual microstructures for addressing structure-property relations in complex multi-modal cold spray formed microstructures. A robust multiscale model accounts for grains at two scales. [ABSTRACT FROM AUTHOR]

The aqueous interface-rich system has been proposed to act as a trigger and a reservoir for reactive radicals, playing a crucial role in chemical reactions. Although much is known about the redox reactivity of water microdroplets at "droplets-in-gas" interfaces, it remains poorly understood for "bubbles-in-water" interfaces that are created by feeding gas through the porous membrane of the gas diffusion electrode. Here we reveal the spontaneous generation of highly reactive redox radical species detected by using electron paramagnetic resonance under such conditions without applying any bias and loading any catalysts. In combination with ultraviolet-visible spectroscopy, the redox feature has been further verified through several probe molecules. Unexpectedly, introducing crown ether allows to isolate and stabilize both water radical cations and hydrated electrons thus substantially increasing redox reactivity. Our finding suggests a reactive microenvironment at the interface of the gas diffusion electrode owing to the coexistence of oxidative and reductive species. The gas diffusion electrode interface can boost chemical reactions, yet its microenvironment properties are largely unexplored. Here, the authors report the spontaneous generation of reactive redox radical species at the interface using Janus hydrophobic/hydrophilic porous carbon papers to feed gas. [ABSTRACT FROM AUTHOR]

This document is a response to a critique of a scientific paper published in Nature Communications. The authors of the response address the criticisms made by Dr. Goldsmith and his colleagues regarding the enzyme activities described in their paper. They clarify that their results were measured using a different method than assumed by Dr. Goldsmith. The authors also present their own experiments that challenge the proposed substrate for one of the enzymes involved in the synthesis of β-ODAP, a compound found in grass peas that can cause a neurodegenerative disorder called lathyrism. They conclude that the synthesis of a key compound involved in β-ODAP synthesis is likely limited in the cytosol of grass pea plants. The article highlights the proposed mechanism for β-ODAP synthesis and provides experimental evidence to support it, while also addressing criticisms and emphasizing the importance of understanding this process for improving the crop. [Extracted from the article]

Skillful subseasonal forecasts are crucial for various sectors of society but pose a grand scientific challenge. Recently, machine learning-based weather forecasting models outperform the most successful numerical weather predictions generated by the European Centre for Medium-Range Weather Forecasts (ECMWF), but have not yet surpassed conventional models at subseasonal timescales. This paper introduces FuXi Subseasonal-to-Seasonal (FuXi-S2S), a machine learning model that provides global daily mean forecasts up to 42 days, encompassing five upper-air atmospheric variables at 13 pressure levels and 11 surface variables. FuXi-S2S, trained on 72 years of daily statistics from ECMWF ERA5 reanalysis data, outperforms the ECMWF's state-of-the-art Subseasonal-to-Seasonal model in ensemble mean and ensemble forecasts for total precipitation and outgoing longwave radiation, notably enhancing global precipitation forecast. The improved performance of FuXi-S2S can be primarily attributed to its superior capability to capture forecast uncertainty and accurately predict the Madden-Julian Oscillation (MJO), extending the skillful MJO prediction from 30 days to 36 days. Moreover, FuXi-S2S not only captures realistic teleconnections associated with the MJO but also emerges as a valuable tool for discovering precursor signals, offering researchers insights and potentially establishing a new paradigm in Earth system science research. This paper introduces FuXi-S2S, a machine-learning model that outperforms conventional numerical weather prediction models at subseasonal timescales globally, extending the skillful Madden–Julian Oscillation prediction form 30 days to 36 days. [ABSTRACT FROM AUTHOR]

The abuse of antibiotics urgently requires rapid identification of drug-resistant bacteria at the point of care (POC). Here we report a visual paper sensor that allows rapid (0.25-3 h) discrimination of the subtypes of β-lactamase (the major cause of bacterial resistance) for precision antibiotic therapy. The sensor exhibits high performance in identifying antibiotic-resistant bacteria with 100 real samples from patients with diverse bacterial infections, demonstrating 100% clinical sensitivity and specificity. Further, this sensor can enhance the accuracy of antibiotic use from 48% empirically to 83%, and further from 50.6% to 97.6% after eliminating fungal infection cases. Our work provides a POC testing platform for guiding effective management of bacterial infections in both hospital and community settings. The rapid identification of drug-resistant bacteria is vital for effective treatment and to avoid antibiotic misuse. Here authors report a paper-based sensor which utilises chromogenic carbapenem and cephalosporin substrates for the identification and discrimination of β-lactamase subtypes. [ABSTRACT FROM AUTHOR]

Aquaculture is a rapidly growing food production technology, but there are significant concerns related to its environmental impact and adverse social effects. We examine aquaculture outcomes in a three pillars of sustainability framework by analyzing data collected using the Aquaculture Performance Indicators. Using this approach, comparable data has been collected for 57 aquaculture systems worldwide on 88 metrics that measure social, economic, or environmental outcomes. We first examine the relationships among the three pillars of sustainability and then analyze performance in the three pillars by technology and species. The results show that economic, social, and environmental outcomes are, on average, mutually reinforced in global aquaculture systems. However, the analysis also shows significant variation in the degree of sustainability in different aquaculture systems, and weak performance of some production systems in some dimensions provides opportunity for innovative policy measures and investment to further align sustainability objectives. Garlock and colleagues analyze 57 aquaculture systems worldwide on 88 metrics that measure social, economic, or environmental outcomes. They find significant variation in the degree of sustainability in different aquaculture systems, and weak performance of some production systems in some dimensions provides opportunity for innovative policy measures and investment to further align sustainability objectives. [ABSTRACT FROM AUTHOR]

Transplantation of engineered hematopoietic stem/progenitor cells (HSPCs) showed curative potential in patients affected by neurometabolic diseases treated in early stage. Favoring the engraftment and maturation of the engineered HSPCs in the central nervous system (CNS) could allow enhancing further the therapeutic potential of this approach. Here we unveil that HSPCs haplo-insufficient at the Cx3cr1 (Cx3cr1−/+) locus are favored in central nervous system (CNS) engraftment and generation of microglia-like progeny cells (MLCs) as compared to wild type (Cx3cr1+/+) HSPCs upon transplantation in mice. Based on this evidence, we have developed a CRISPR-based targeted gene addition strategy at the human CX3CR1 locus resulting in an enhanced ability of the edited human HSPCs to generate mature MLCs upon transplantation in immunodeficient mice, and in lineage specific, regulated and robust transgene expression. This approach, which benefits from the modulation of pathways involved in microglia maturation and migration in haplo-insufficient cells, may broaden the application of HSPC gene therapy to a larger spectrum of neurometabolic and neurodegenerative diseases. In this paper, a CRISPR-based gene addition strategy at the CX3CR1 locus of human hematopoietic stem cells is proposed to efficiently generate microglia engineered for the regulated expression of therapeutic genes for the treatment of neurodegenerative disorders. [ABSTRACT FROM AUTHOR]

Rapid and accurate state of health (SOH) estimation of retired batteries is a crucial pretreatment for reuse and recycling. However, data-driven methods require exhaustive data curation under random SOH and state of charge (SOC) retirement conditions. Here, we show that the generative learning-assisted SOH estimation is promising in alleviating data scarcity and heterogeneity challenges, validated through a pulse injection dataset of 2700 retired lithium-ion battery samples, covering 3 cathode material types, 3 physical formats, 4 capacity designs, and 4 historical usages with 10 SOC levels. Using generated data, a regressor realizes accurate SOH estimations, with mean absolute percentage errors below 6% under unseen SOC. We predict that assuming uniform deployment of the proposed technique, this would save 4.9 billion USD in electricity costs and 35.8 billion kg CO2 emissions by mitigating data curation costs for a 2030 worldwide battery retirement scenario. This paper highlights exploiting limited data for exploring extended data space using generative methods, given data can be time-consuming, expensive, and polluting to retrieve for many estimation and predictive tasks. Data scarcity and heterogeneity impede the estimation of retired battery capacity. Here, Tao et al. propose a generative learning method that extends measured data space, potentially reducing curation time, cost and facilitating their sustainable reuse and recycling. [ABSTRACT FROM AUTHOR]

Developing a generalist radiology diagnosis system can greatly enhance clinical diagnostics. In this paper, we introduce RadDiag, a foundational model supporting 2D and 3D inputs across various modalities and anatomies, using a transformer-based fusion module for comprehensive disease diagnosis. Due to patient privacy concerns and the lack of large-scale radiology diagnosis datasets, we utilize high-quality, clinician-reviewed radiological images available online with diagnosis labels. Our dataset, RP3D-DiagDS, contains 40,936 cases with 195,010 scans covering 5568 disorders (930 unique ICD-10-CM codes). Experimentally, our RadDiag achieves 95.14% AUC on internal evaluation with the knowledge-enhancement strategy. Additionally, RadDiag can be zero-shot applied or fine-tuned to external diagnosis datasets sourced from various medical centers, demonstrating state-of-the-art results. In conclusion, we show that publicly shared medical data on the Internet is a tremendous and valuable resource that can potentially support building strong models for image understanding in healthcare. Medical imaging has transformed clinical diagnostics. Here, authors present RadDiag, a foundational model for comprehensive disease diagnosis using multi-modal inputs, demonstrating superior zero-shot performance on external datasets compared to other foundation models and showing broad applicability across various medical conditions. [ABSTRACT FROM AUTHOR]

Striving to build an exhaustive guidebook of the types and properties of human cells, the Human Cell Atlas' (HCA) success relies on the sampling of diverse populations, developmental stages, and tissue types. Its open science philosophy preconizes the rapid, seamless sharing of data – as openly as possible. In light of the scope and ambition of such an international initiative, the HCA Ethics Working Group (EWG) has been working to build a solid foundation to address the complexities of data collection and sharing as part of Atlas development. Indeed, a particular challenge of the HCA is the diversity of sampling scenarios (e.g., living participants, deceased donors, pediatric populations, culturally diverse backgrounds, tissues from various developmental stages, etc.), and associated ethical and legal norms, which vary across countries contributing to the effort. Hence, to the extent possible, the EWG set out to provide harmonised, international and interoperable policies and tools, to guide its research community. This paper provides a high-level overview of the types of challenges and approaches proposed by the EWG. The human cell atlas (HCA) is intended as an exhaustive guidebook of human cell types and their properties. Here Kirby et al. outline how the HCA Ethics Working Group is working to build a solid foundation to address the complexities of data collection and sharing. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of the impact of adaptive cruise control on energy consumption in real-world driving conditions based on a natural experiment: a large-scale observational dataset of driving data from a diverse fleet of vehicles and drivers. The analysis is conducted at two different fidelity levels: (1) a macroscopic trip-level benefit estimate that compares trips with and without cruise control in a counterfactual way using statistical methods, and (2) a situation-based comparison achieved through the segmentation of trips into distinct driving situations such as acceleration, braking, cruising, and other maneuvers. The results of this research show that the effect of cruise control on energy consumption varies across different driving situations and levels of analysis. In a macroscopic trip-level analysis, cruise control engagement is associated with a slight increase in fuel consumption across the fleet. As revealed later by the situation-based analysis, this result can be attributed to the negative impact of cruise control on energy consumption in cruising mode, which is the most common driving situation. However, the situation-based comparison demonstrates that cruise control can provide fuel consumption benefits in situations involving acceleration and braking, particularly when a preceding vehicle is present. The study also emphasizes the importance of controlling for various factors that can influence both fuel consumption and the likelihood of cruise control engagement to properly evaluate its effects. Efficient automated driving technology to improve upon human driving behaviour offer a promising pathway to energy and fuel savings. Here, authors analyse real-world data to determine how adaptive cruise control affects fuel consumption and identify specific driving situations where it can be optimized. [ABSTRACT FROM AUTHOR]

Cognitive problem-solving benefits from cognitive maps aiding navigation and planning. Physical space navigation involves hippocampal (HC) allocentric codes, while abstract task space engages medial prefrontal cortex (mPFC) task-specific codes. Previous studies show that challenging tasks, like spatial alternation, require integrating these two types of maps. The disruption of the HC-mPFC circuit impairs performance. We propose a hierarchical active inference model clarifying how this circuit solves spatial interaction tasks by bridging physical and task-space maps. Simulations demonstrate that the model's dual layers develop effective cognitive maps for physical and task space. The model solves spatial alternation tasks through reciprocal interactions between the two layers. Disrupting its communication impairs decision-making, which is consistent with empirical evidence. Additionally, the model adapts to switching between multiple alternation rules, providing a mechanistic explanation of how the HC-mPFC circuit supports spatial alternation tasks and the effects of disruption. How cognitive maps of physical and task space interact when executing cognitive tasks is not fully understood. This paper models how the hippocampal-prefrontal circuits solves memory-guided spatial alternation tasks, by bridging cognitive maps of physical and taskspace. [ABSTRACT FROM AUTHOR]

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. Heavier group 14 carbene analogue display remarkable capability for small molecule activation but their application in redox catalysis remains elusive. Here, the authors report the synthesis and isolation of a stannylene with carbodiphosphorane ligand and characterize its catalytic activity in the hydrodefluorination reaction of fluoroarenes. [ABSTRACT FROM AUTHOR]

Mining has played an important role in the economies of South American countries. Although industrial mining prevails in most countries, the expansion of garimpo activity has increased substantially. Recently, Brazil exhibited two moments of garimpo dominance over industrial mining: 1989–1997 and 2019–2022. While industrial mining sites occupied ~ 360 km2 in 1985 but increased to 1800 km2 in 2022, a 5-fold increase, garimpo mining area increased by ~ 1200%, from ~ 218 km2 in 1985 to ~ 2627 km2 in 2022. More than 91% of this activity is concentrated in the Amazon. Where almost 40% of the sites are five years old or younger, this proportion increases to 62% within Indigenous lands (ILs). Regarding the legal aspect, at least 77% of the 2022 extraction sites showed explicit signs of illegality. Particular attention must be given to the Kayapo, Munduruku, and Yanomami ILs. Together, they concentrate over 90% of the garimpo across ILs. 91% of Brazil's garimpo activity is in the Amazon, where almost 40% of them are five years old or younger. This paper finds in 2022, 77% of the detected garimpos showed explicit signs of illegality. [ABSTRACT FROM AUTHOR]

Natural polymer substrates are gaining attention as substitutes for plastic substrates in electronics, aiming to combine high performance, intricate shape deformation, and environmental sustainability. Herein, natural wood veneer is converted into a transparent wood film (TWF) substrate. The combination of 3D printing and origami technique is established to create programmable wood-based origami electronics, which exhibit superior flexibility with high tensile strength (393 MPa) due to the highly aligned cellulose fibers and the formation of numerous intermolecular hydrogen bonds between them. Moreover, the flexible TWF electronics exhibit editable multiplexed configurations and maintain stable conductivity. This is attributed to the strong adhesion between the cellulose-based ink and TWF substrate by non-covalent bonds. Benefiting from its anisotropic structure, the programmability of TWF electronics is achieved through sequentially folding into predesigned shapes. This design not only promotes environmental sustainability but also introduces its customizable shapes with potential applications in sensors, microfluidics, and wearable electronics. Wood-based electronics are typically constrained in their ability to undergo complex shape deformation. Here, authors integrate transparent wood film and cellulose-based conductive ink for flexible electronic origami devices that have been demonstrated as a proof-of-concept for human motion sensors. [ABSTRACT FROM AUTHOR]

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. Simultaneous control of the direction and polarization of quantum emission using photonic nanostructures is a long-standing challenge in photonics. Here, the authors experimentally demonstrate unidirectional chiral emission from a twisted bilayer metasurface with multi-dimensional control. [ABSTRACT FROM AUTHOR]

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. Pharmaceutical drug dosage forms are traditionally determined through extensive physical experimentation. Here, the authors present a generative AI method that creates digital drug products from images, matching and improving critical quality attributes such as particle size and drug loading. [ABSTRACT FROM AUTHOR]

Shifting precipitation regimes are a well-documented and pervasive consequence of climate change. Subsistence-oriented communities worldwide can identify changes in rainfall patterns that most affect their lives. Here we scrutinize the importance of human-based rainfall observations (collated through a literature review spanning from 1994 to 2013) as climate metrics and the relevance of instrument-based precipitation indices to subsistence activities. For comparable time periods (1955-2005), changes observed by humans match well with instrumental records at same locations for well-established indices of rainfall (72% match), drought (76%), and extreme rainfall (81%), demonstrating that we can bring together human and instrumental observations. Many communities (1114 out of 1827) further identify increased variability and unpredictability in the start, end, and continuity of rainy seasons, all of which disrupt the cropping calendar, particularly in the Tropics. These changes in rainfall patterns and predictability are not fully captured by existing indices, and their social-ecological impacts are still understudied.This paper provides an analysis or rainfall changes that integrates human-based and instrument-based observations, identifying regions of agreement and disagreement, and providing insights on how traditional ecological knowledge can complement instrumental indices. [ABSTRACT FROM AUTHOR]

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 (K2) 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. The authors predict the Pockels effect of ScAlN with varying Sc concentration, realizing a ScAlN-on-insulator-on-silicon material platform, which allows the formation of low-loss, electrically-tunable microring resonators for accurate measurement of ScAlN's Pockels coefficients. [ABSTRACT FROM AUTHOR]

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. The electrocatalytic reduction of nitrate to ammonia offers a sustainable alternative to the Haber-Bosch process. Here, the authors report a lattice hydrogen transfer mechanism that enhances electrocatalytic activity by enabling reversible equilibrium reactions between lattice and active hydrogen. [ABSTRACT FROM AUTHOR]

Climate mitigation policies have broad environmental and socioeconomic impacts and thus underpin progress towards the United Nations Sustainable Development Goals (SDGs). Through national-scale integrated modeling, we explore the spillover effects of China's long-term climate mitigation pathways (CMPs) on achieving all 17 SDGs, and then identify a cost-effective CMP for China with co-benefits for sustainability. Our analysis indicates that the 9 original CMPs and 180 bundled CMPs can both substantially boost the SDGs, resulting in an increase of 6.33–8.86 and 5.90–9.33 points in overall SDG score (0=no progress, 100=full achievement) by 2060, compared to the Reference pathway of 70.75 points, respectively. The identified cost-effective CMP deals with the trade-offs among sustainability, CO2 emissions and mitigation cost, and maximizes the synergies between them. This CMP can inform future directions for China's policy-makers to maximize the potential synergies between carbon neutrality and long-term sustainable development. Climate mitigation have broad effects on achieving Sustainable Development Goals. This paper explores the spillover effects of China's climate mitigation pathways and identify a cost-effective pathway with co-benefits for sustainability. [ABSTRACT FROM AUTHOR]

Magnesium (Mg) metal is a promising anode candidate for high-energy and cost-effective multivalent metal batteries, but suffers from severe surface passivation in conventional electrolytes, especially aqueous solutions. Here, we uncover that MgH2, in addition to the well-known MgO and Mg(OH)2, can be formed during the passivation of Mg by water. The formation mechanism and spatial distribution of MgH2, and its detrimental effect on interfacial dynamics and stability of Mg anode are revealed by comprehensive experimental and theoretical investigations. Furthermore, a graphite-based hydrophobic and Mg2+-permeable water-tolerant interphase is drawn using a pencil on the surface of Mg anodes, allowing them to cycle stably in symmetric (> 900 h) and full cells (> 500 cycles) even after contact with water. The mechanistic understanding of MgH2-involved Mg passivation and the design of pencil-drawn waterproof Mg anodes may inspire the further development of Mg metal batteries with high water resistance. This paper uncovers the formation of MgH2, in addition to the well-known MgO and Mg(OH)2, during the passivation of Mg by water, and demonstrates a waterproof Mg metal anode by simply pencil drawing. [ABSTRACT FROM AUTHOR]

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. Industry 4.0 requires collaboration among material research institutions, but data silos hinder progress. Here the authors present MatSwarm, a swarm-learning framework that integrates secure computing and data sharing in the National Material Data Management and Services (NMDMS) platform. [ABSTRACT FROM AUTHOR]

The current thyroid ultrasound relies heavily on the experience and skills of the sonographer and the expertise of the radiologist, and the process is physically and cognitively exhausting. In this paper, we report a fully autonomous robotic ultrasound system, which is able to scan thyroid regions without human assistance and identify malignant nod- ules. In this system, human skeleton point recognition, reinforcement learning, and force feedback are used to deal with the difficulties in locating thyroid targets. The orientation of the ultrasound probe is adjusted dynamically via Bayesian optimization. Experimental results on human participants demonstrated that this system can perform high-quality ultrasound scans, close to manual scans obtained by clinicians. Additionally, it has the potential to detect thyroid nodules and provide data on nodule characteristics for American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) calculation. Current thyroid ultrasounds rely heavily on the experience and skills of the sonographer and of the radiologist, and the process is physically and cognitively exhausting. Here, the authors show a fully autonomous robotic ultrasound system, which is able to scan thyroid regions without human assistance and identify malignant nodules. [ABSTRACT FROM AUTHOR]

Achieving net-zero climate targets requires some level of carbon dioxide removal. Current assessments focus on tonnes of CO2 removed, without specifying what form these removals will take. Here, we show that countries' climate pledges require approximately 1 (0.9–1.1) billion ha of land for removals. For over 40% of this area, the pledges envisage the conversion of existing land uses to forests, while the remaining area restores existing ecosystems and land uses. We analyse how this demand for land is distributed geographically and over time. The results are concerning, both in terms of the aggregate area of land, but also the rate and extent of land use change. Our findings demonstrate a gap between governments' expected reliance on land and the role that land can realistically play in climate mitigation. This adds another layer to the observed shortcomings of national climate pledges and indicates a need for more transparency around the role of land in national climate mitigation plans. Achieving net-zero climate targets requires substantial land for carbon dioxide removal. This paper quantifies the land area in countries' climate pledges at approximately 1 billion hectares, often involving the conversion of existing land uses to forests. [ABSTRACT FROM AUTHOR]

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. Excitonic insulators remain a topic of great interest in condensed matter physics. Here, the authors provide spectroscopic evidence of increased electronic coherence in the charge-density-wave state of semiconducting 1T-TiSe2. [ABSTRACT FROM AUTHOR]

Improvements in high-resolution satellite remote sensing and computational advancements have sped up the development of global datasets that delineate urban land, crucial for understanding climate risks in our increasingly urbanizing world. Here, we analyze urban land cover patterns across spatiotemporal scales from several such current-generation products. While all the datasets show a rapidly urbanizing world, with global urban land nearly tripling between 1985 and 2015, there are substantial discrepancies in urban land area estimates among the products influenced by scale, differing urban definitions, and methodologies. We discuss the implications of these discrepancies for several use cases, including for monitoring urban climate hazards and for modeling urbanization-induced impacts on weather and climate from regional to global scales. Our results demonstrate the importance of choosing fit-for-purpose datasets for examining specific aspects of historical, present, and future urbanization with implications for sustainable development, resource allocation, and quantification of climate impacts. There has been a surge in global datasets of urban land recently. This paper shows large discrepancies in urban area across scales among multiple such datasets, which can influence the magnitude and direction of estimated urban climate impacts. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) displace millions every year. While TCs pose hardships and threaten lives, their negative impacts can be reduced by anticipatory actions like evacuation and humanitarian aid coordination. In addition to weather forecasts, impact forecast enables more effective response by providing richer information on the numbers and locations of people at risk of displacement. We introduce a fully open-source implementation of a globally consistent and regionally calibrated TC-related displacement forecast at low computational costs, combining meteorological forecast with population exposure and respective vulnerability. We present a case study of TC Yasa which hit Fiji in December 2020. We emphasise the importance of considering the uncertainties associated with hazard, exposure, and vulnerability in a global uncertainty analysis, which reveals a considerable spread of possible outcomes. Additionally, we perform a sensitivity analysis on all recorded TC displacement events from 2017 to 2020 to understand how the forecast outcomes depend on these uncertain inputs. Our findings suggest that for longer forecast lead times, decision-making should focus more on meteorological uncertainty, while greater emphasis should be placed on the vulnerability of the local community shortly before TC landfall. Our open-source codes and implementations are readily transferable to other users, hazards, and impact types. This paper proposes an open-source, impact-based forecast for tropical cyclone population displacement using both cyclone forecasts and population settlements and vulnerabilities. This is applied to Tropical Cyclone Yasa, striking Fiji in 2020. [ABSTRACT FROM AUTHOR]

Highly sensitive airborne virus monitoring is critical for preventing and containing epidemics. However, the detection of airborne viruses at ultra-low concentrations remains challenging due to the lack of ultra-sensitive methods and easy-to-deployment equipment. Here, we present an integrated microfluidic cartridge that can accurately detect SARS-COV-2, Influenza A, B, and respiratory syncytial virus with a sensitivity of 10 copies/mL. When integrated with a high-flow aerosol sampler, our microdevice can achieve a sub-single-copy spatial resolution of 0.83 copies/m3 for airborne virus surveillance with an air flow rate of 400 L/min and a sampling time of 30 minutes. We then designed a series of virus-in-aerosols monitoring systems (RIAMs), including versions of a multi-site sampling RIAMs (M-RIAMs), a stationary real-time RIAMs (S-RIAMs), and a roaming real-time RIAMs (R-RIAMs) for different application scenarios. Using M-RIAMs, we performed a comprehensive evaluation of 210 environmental samples from COVID-19 patient wards, including 30 aerosol samples. The highest positive detection rate of aerosol samples (60%) proved the aerosol-based SARS-CoV-2 monitoring represents an effective method for spatial risk assessment. The detection of 78 aerosol samples in real-world settings via S-RIAMs confirmed its reliability for ultra-sensitive and continuous airborne virus monitoring. Therefore, RIAMs shows the potential as an effective solution for mitigating the risk of airborne virus transmission. Effective surveillance of respiratory virus particles in air requires highly sensitive tools that can produce timely and accurate results. Here, the authors develop a device for detection of four respiratory viruses in air with high spatial viral resolution and test it in different settings. [ABSTRACT FROM AUTHOR]

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. Healthcare data democratization is often hampered by privacy constraints governing the sensitive healthcare data. Here, the authors show that encoding healthcare data could be a potential solution for achieving healthcare democratization within the context of deep learning. [ABSTRACT FROM AUTHOR]