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In the initial version of our paper, we included Clean Water Act data for all 50 states, DC, and Puerto Rico. Similarly, the percent of Clean Water Act permittees in Significant Noncompliance drops from 6.01 to 3.37% and the average percent of permittees in Significant Noncompliance drops from 9.00 to 6.23%. To ensure that we did not misrepresent the level of Clean Water Act Significant Noncompliance in these states, we have corrected our article by removing these states from the Clean Water Act portion of our analysis. [Extracted from the article]

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

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]

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]

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]

Reversible and irreversible amyloids are two diverging cases of protein (mis)folding associated with the cross-β motif in the protein folding and aggregation energy landscape. Yet, the molecular origins responsible for the formation of reversible vs irreversible amyloids have remained unknown. Here we provide evidence at the atomic level of distinct folding motifs for irreversible and reversible amyloids derived from a single protein sequence: human lysozyme. We compare the 2.8 Å structure of irreversible amyloid fibrils determined by cryo-electron microscopy helical reconstructions with molecular insights gained by solid-state NMR spectroscopy on reversible amyloids. We observe a canonical cross-β-sheet structure in irreversible amyloids, whereas in reversible amyloids, there is a less-ordered coexistence of β-sheet and helical secondary structures that originate from a partially unfolded lysozyme, thus carrying a "memory" of the original folded protein precursor. We also report the structure of hen egg-white lysozyme irreversible amyloids at 3.2 Å resolution, revealing another canonical amyloid fold, and reaffirming that irreversible amyloids undergo a complete conversion of the native protein into the cross-β structure. By combining atomic force microscopy, cryo-electron microscopy and solid-state NMR, we show that a full unfolding of the native protein precursor is a requirement for establishing irreversible amyloid fibrils. In this paper, the authors revealed the distinct folding motifs for irreversible and reversible amyloids derived from a single protein, by combining multiple mesoscopic and molecular techniques including AFM, cryo-EM and solid-state NMR. [ABSTRACT FROM AUTHOR]

Metal micro/nanoparticle ink-based printed circuits have shown promise for promoting the scalable application of flexible electronics due to enabling superhigh metallic conductivity with cost-effective mass production. However, it is challenging to activate printed metal-particle patterns to approach the intrinsic conductivity without damaging the flexible substrate, especially for high melting-point metals. Here, we report a pressure-constrained sonication activation (PCSA) method of the printed flexible circuits for more than dozens of metal (covering melting points from room temperature to 3422 °C) and even nonmetallic inks, which is integrated with the large-scale roll-to-roll process. The PCSA-induced synergistic heat-softening and vibration-bonding effect of particles can enable multilayer circuit interconnection and join electronic components onto printed circuits without solder within 1 s at room temperature. We demonstrate PCSA-based applications of 3D flexible origami electronics, erasable and foldable double-sided electroluminescent displays, and custom-designed and large-area electronic textiles, thus indicating its potential for universality in flexible electronics. It is challenging to activate the flexible printed metal circuit to approach the intrinsic conductivity with minimal damage to the substrate. Here, the authors report a large-scale pressure-constrained sonication activation method for various metals and non-metallic inks. [ABSTRACT FROM AUTHOR]

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.DNA data storage is a potential alternative to magnetic tape for archival storage purposes, promising substantial gains in information density. Here the authors investigate the susceptibility of DNA-encoded data to particle radiation damage by neutron irradiation and computational modeling, showing that particle radiation is not a significant contributor to data loss under typical storage conditions. [ABSTRACT FROM AUTHOR]

Developing future climate projections begins with choosing future emissions scenarios. While scenarios are often based on storylines, here instead we produce a probabilistic multi-million-member ensemble of radiative forcing trajectories to assess the relevance of future forcing thresholds. We coupled a probabilistic database of future greenhouse gas emission scenarios with a probabilistically calibrated reduced complexity climate model. In 2100, we project median forcings of 5.1 watt per square meters (5th to 95th percentiles of 3.3 to 7.1), with roughly 0.5% probability of exceeding 8.5 watt per square meters, and a 1% probability of being lower than 2.6 watt per square meters. Although the probability of 8.5 watt per square meters scenarios is low, our results support their continued utility for calibrating damage functions, characterizing climate in the 22nd century (the probability of exceeding 8.5 watt per square meters increases to about 7% by 2150), and assessing low-probability/high-impact futures. The probability of exceeding various climate thresholds has been calculated. While the warmest future scenario has less than a 1 percent chance of being exceeded this century, the paper discusses why such warm scenarios are still relevant. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) tomography is a powerful investigative tool for many scientific domains, going from materials science, to engineering, to medicine. Many factors may limit the 3D resolution, often spatially anisotropic, compromising the precision of the information retrievable. A neural network, designed for video-frame interpolation, is employed to enhance tomographic images, achieving cubic-voxel resolution. The method is applied to distinct domains: the investigation of the morphology of printed graphene nanosheets networks, obtained via focused ion beam-scanning electron microscope (FIB-SEM), magnetic resonance imaging of the human brain, and X-ray computed tomography scans of the abdomen. The accuracy of the 3D tomographic maps can be quantified through computer-vision metrics, but most importantly with the precision on the physical quantities retrievable from the reconstructions, in the case of FIB-SEM the porosity, tortuosity, and effective diffusivity. This work showcases a versatile image-augmentation strategy for optimizing 3D tomography acquisition conditions, while preserving the information content. This paper demonstrates that an algorithm designed for video augmentation can be efficiently used for 3D tomography reconstruction across the materials science and medical domain, namely across disciplines and length-scales. [ABSTRACT FROM AUTHOR]

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. Challenges persist in haptic device development due to the complex nature of touch. The authors propose a Bioinspired Adaptable Multiplanar Haptic system providing multipoint and variable intensity pulse stimuli (0-130 Hz) across multiple fingertip areas, enabling deeper study of touch skin sensors. [ABSTRACT FROM AUTHOR]

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. Spatiotemporal data consisting of measurements gathered at different times and locations is challenging to analyse due to variability and noise impact across different scales. The authors propose a statistical approach that delivers models of large-scale spatiotemporal datasets applicable to data-analysis tasks of forecasting and interpolation. [ABSTRACT FROM AUTHOR]

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 KD 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. Increasing the binding affinity of an antibody to its target antigen is key for antibody therapeutics. Here the authors report a pretrainable geometric graph neural network, GearBind, and explore its potential in in silico antibody affinity maturation. [ABSTRACT FROM AUTHOR]

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 Ti3C2Tx 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. Modern communication applications may demand devices with tunable performances and simple fabrications. Here, we show strain dependent, adjustable RF/microwave performance by applying patterns of conductive Ti3C2Tx MXene coatings on low-cost acetate substrates in a straightforward coating process. [ABSTRACT FROM AUTHOR]