Scalable production of high-performing woven lithium-ion fibre batteries.

Fibre lithium-ion batteries are attractive as flexible power solutions because they can be woven into textiles, offering a convenient way to power future wearable electronics1-4. However, they are difficult to produce in lengths of more than a few centimetres, and longer fibres were thought to have higher internal resistances3,5 that compromised electrochemical performance6,7. Here we show that the internal resistance of such fibres has a hyperbolic cotangent function relationship with fibre length, where it first decreases before levelling off as length increases. Systematic studies confirm that this unexpected result is true for different fibre batteries. We are able to produce metres of high-performing fibre lithium-ion batteries through an optimized scalable industrial process. Our mass-produced fibre batteries have an energy density of 85.69 watt hour per kilogram (typical values8 are less than 1 watt hour per kilogram), based on the total weight of a lithium cobalt oxide/graphite full battery, including packaging. Its capacity retention reaches 90.5% after 500 charge-discharge cycles and 93% at 1C rate (compared with 0.1C rate capacity), which is comparable to commercial batteries such as pouch cells. Over 80 per cent capacity can be maintained after bending the fibre for 100,000 cycles. We show that fibre lithium-ion batteries woven into safe and washable textiles by industrial rapier loom can wirelessly charge a cell phone or power a health management jacket integrated with fibre sensors and a textile display.

Polypeptide organic radical batteries.

In only a few decades, lithium-ion batteries have revolutionized technologies, enabling the proliferation of portable devices and electric vehicles1, with substantial benefits for society. However, the rapid growth in technology has highlighted the ethical and environmental challenges of mining lithium, cobalt and other mineral ore resources, and the issues associated with the safe usage and non-hazardous disposal of batteries2. Only a small fraction of lithium-ion batteries are recycled, further exacerbating global material supply of strategic elements3-5. A potential alternative is to use organic-based redox-active materials6-8 to develop rechargeable batteries that originate from ethically sourced, sustainable materials and enable on-demand deconstruction and reconstruction. Making such batteries is challenging because the active materials must be stable during operation but degradable at end of life. Further, the degradation products should be either environmentally benign or recyclable for reconstruction into a new battery. Here we demonstrate a metal-free, polypeptide-based battery, in which viologens and nitroxide radicals are incorporated as redox-active groups along polypeptide backbones to function as anode and cathode materials, respectively. These redox-active polypeptides perform as active materials that are stable during battery operation and subsequently degrade on demand in acidic conditions to generate amino acids, other building blocks and degradation products. Such a polypeptide-based battery is a first step to addressing the need for alternative chemistries for green and sustainable batteries in a future circular economy.

Accurate estimation of biological age and its application in disease prediction using a multimodal image Transformer system.

Aging in an individual refers to the temporal change, mostly decline, in the body's ability to meet physiological demands. Biological age (BA) is a biomarker of chronological aging and can be used to stratify populations to predict certain age-related chronic diseases. BA can be predicted from biomedical features such as brain MRI, retinal, or facial images, but the inherent heterogeneity in the aging process limits the usefulness of BA predicted from individual body systems. In this paper, we developed a multimodal Transformer-based architecture with cross-attention which was able to combine facial, tongue, and retinal images to estimate BA. We trained our model using facial, tongue, and retinal images from 11,223 healthy subjects and demonstrated that using a fusion of the three image modalities achieved the most accurate BA predictions. We validated our approach on a test population of 2,840 individuals with six chronic diseases and obtained significant difference between chronological age and BA (AgeDiff) than that of healthy subjects. We showed that AgeDiff has the potential to be utilized as a standalone biomarker or conjunctively alongside other known factors for risk stratification and progression prediction of chronic diseases. Our results therefore highlight the feasibility of using multimodal images to estimate and interrogate the aging process.

Wireless Battery-free and Fully Implantable Organ Interfaces.

Advances in soft materials, miniaturized electronics, sensors, stimulators, radios, and battery-free power supplies are resulting in a new generation of fully implantable organ interfaces that leverage volumetric reduction and soft mechanics by eliminating electrochemical power storage. This device class offers the ability to provide high-fidelity readouts of physiological processes, enables stimulation, and allows control over organs to realize new therapeutic and diagnostic paradigms. Driven by seamless integration with connected infrastructure, these devices enable personalized digital medicine. Key to advances are carefully designed material, electrophysical, electrochemical, and electromagnetic systems that form implantables with mechanical properties closely matched to the target organ to deliver functionality that supports high-fidelity sensors and stimulators. The elimination of electrochemical power supplies enables control over device operation, anywhere from acute, to lifetimes matching the target subject with physical dimensions that supports imperceptible operation. This review provides a comprehensive overview of the basic building blocks of battery-free organ interfaces and related topics such as implantation, delivery, sterilization, and user acceptance. State of the art examples categorized by organ system and an outlook of interconnection and advanced strategies for computation leveraging the consistent power influx to elevate functionality of this device class over current battery-powered strategies is highlighted.

In situ NMR metrology reveals reaction mechanisms in redox flow batteries.

Large-scale energy storage is becoming increasingly critical to balancing renewable energy production and consumption1. Organic redox flow batteries, made from inexpensive and sustainable redox-active materials, are promising storage technologies that are cheaper and less environmentally hazardous than vanadium-based batteries, but they have shorter lifetimes and lower energy density2,3. Thus, fundamental insight at the molecular level is required to improve performance4,5. Here we report two in situ nuclear magnetic resonance (NMR) methods of studying redox flow batteries, which are applied to two redox-active electrolytes: 2,6-dihydroxyanthraquinone (DHAQ) and 4,4'-((9,10-anthraquinone-2,6-diyl)dioxy) dibutyrate (DBEAQ). In the first method, we monitor the changes in the 1H NMR shift of the liquid electrolyte as it flows out of the electrochemical cell. In the second method, we observe the changes that occur simultaneously in the positive and negative electrodes in the full electrochemical cell. Using the bulk magnetization changes (observed via the 1H NMR shift of the water resonance) and the line broadening of the 1H shifts of the quinone resonances as a function of the state of charge, we measure the potential differences of the two single-electron couples, identify and quantify the rate of electron transfer between the reduced and oxidized species, and determine the extent of electron delocalization of the unpaired spins over the radical anions. These NMR techniques enable electrolyte decomposition and battery self-discharge to be explored in real time, and show that DHAQ is decomposed electrochemically via a reaction that can be minimized by limiting the voltage used on charging. We foresee applications of these NMR methods in understanding a wide range of redox processes in flow and other electrochemical systems.

Batteryless implantable device with built-in mechanical clock for automated and precisely timed drug administration.

Adherence to medication plays a crucial role in the effective management of chronic diseases. However, patients often miss their scheduled drug administrations, resulting in suboptimal disease control. Therefore, we propose an implantable device enabled with automated and precisely timed drug administration. Our device incorporates a built-in mechanical clock movement to utilize a clockwork mechanism, i.e., a periodic turn of the hour axis, enabling automatic drug infusion at precise 12-h intervals. The actuation principle relies on the sophisticated design of the device, where the rotational movement of the hour axis is converted into potential mechanical energy and is abruptly released at the exact moment for drug administration. The clock movement can be charged either automatically by mechanical agitations or manually by winding the crown, while the device remains implanted, thereby enabling the device to be used permanently without the need for batteries. When tested using metoprolol, an antihypertensive drug, in a spontaneously hypertensive animal model, the implanted device can deliver drug automatically at precise 12-h intervals without the need for further attention, leading to similarly effective blood pressure control and ultimately, prevention of ventricular hypertrophy as compared with scheduled drug administrations. These findings suggest that our device is a promising alternative to conventional methods for complex drug administration.

High-performance and low-power source-gated transistors enabled by a solution-processed metal oxide homojunction.

Cost-effective fabrication of mechanically flexible low-power electronics is important for emerging applications including wearable electronics, artificial intelligence, and the Internet of Things. Here, solution-processed source-gated transistors (SGTs) with an unprecedented intrinsic gain of ~2,000, low saturation voltage of +0.8 ± 0.1 V, and a ~25.6 µW power consumption are realized using an indium oxide In2O3/In2O3:polyethylenimine (PEI) blend homojunction with Au contacts on Si/SiO2. Kelvin probe force microscopy confirms source-controlled operation of the SGT and reveals that PEI doping leads to more effective depletion of the reverse-biased Schottky contact source region. Furthermore, using a fluoride-doped AlOx gate dielectric, rigid (on a Si substrate) and flexible (on a polyimide substrate) SGTs were fabricated. These devices exhibit a low driving voltage of +2 V and power consumption of ~11.5 µW, yielding inverters with an outstanding voltage gain of >5,000. Furthermore, electrooculographic (EOG) signal monitoring can now be demonstrated using an SGT inverter, where a ~1.0 mV EOG signal is amplified to over 300 mV, indicating significant potential for applications in wearable medical sensing and human-computer interfacing.

The transformative power of transformers in protein structure prediction.

Transformer neural networks have revolutionized structural biology with the ability to predict protein structures at unprecedented high accuracy. Here, we report the predictive modeling performance of the state-of-the-art protein structure prediction methods built on transformers for 69 protein targets from the recently concluded 15th Critical Assessment of Structure Prediction (CASP15) challenge. Our study shows the power of transformers in protein structure modeling and highlights future areas of improvement.

Monolithically Defined Wireless Fully Implantable Nervous System Interfaces.

Evolution of implantable neural interfaces is critical in addressing the challenges in understanding the fundamental working principles and therapeutic applications for central and peripheral nervous systems. Traditional approaches utilizing hermetically sealed, rigid electronics and detached electrodes face challenges in power supply, encapsulation, channel count, dispersed application location, and modality. Employing thin-film, wirelessly powered devices is promising to expand capabilities. Devices that forego bulky power supplies, favoring a configuration where electronics are integrated directly onto thin films, reduce displacement volumes for seamless, fully implantable interfaces with high energy availability and soft mechanics to conform to the neuronal target. We discuss 3 device architectures: (1) Highly miniaturized devices that merge electronics and neural interfaces into a single, injectable format; (2) Interfaces that consolidate power, computation, and neural connectivity on a thin sheet applied directly to the target area; (3) A spatially dislocated approach where power and computation are situated subdermally, connected via a thin interconnect to the neural interface.Each has advantages and constraints in terms of implantation invasiveness, power capturing efficiency, and directional sensitivity of power delivery. In powering these devices, near-field power delivery emerges as the most implemented technique. Key parameters are size and volume of primary and secondary antennas, which determine coupling efficiency and power delivery. Based on application requirements, ranging from small to large animal models, subjects require system level designs. Material strategies play a crucial role; monolithic designs, with materials like polyimide substrates, enable scalability with high performance. This contrasts with established hermetic encapsulation approaches that use a stainless steel or titanium box with passthroughs that result in large tissue displacements and prohibit intimate integration with target organ systems. Encapsulation, particularly with parylene, enables longevity and effectiveness; more research is needed to enable human lifetime operation. Implant-to-ambient device communication, focusing on strategies compatible with well-established standards and off-the-shelf electronics, is discussed with the goal of enabling seamless system integration, reliability, and scalability. The interface with the central nervous system is explored through various wireless, battery-free devices capable of both stimulation (electrical and optogenetic) and recording (photometric and electrochemical). These devices show advanced capabilities for chronic studies and insights into neural dynamics. In the peripheral nervous system, stimulation devices for applications, such as spinal and muscle stimulation, are discussed. The challenges lie in the mechanical and electrochemical durability. Examples that successfully navigate these challenges offer solutions for chronic studies in this domain. The potential of wireless, fully implantable nervous system interfaces using near field resonant power transfer is characterized by monolithically defined device architecture, providing a significant leap toward seamless access to the central and peripheral nervous systems. New avenues for research and therapeutic applications supporting a multimodal and multisite approach to neuromodulation with a high degree of connectivity and a holistic approach toward deciphering and supplementing the nervous system may enable recovery and treatment of injury and chronic disease.

Self-Powered Sensing in Wearable Electronics─A Paradigm Shift Technology.

With the advancements in materials science and micro/nanoengineering, the field of wearable electronics has experienced a rapid growth and significantly impacted and transformed various aspects of daily human life. These devices enable individuals to conveniently access health assessments without visiting hospitals and provide continuous, detailed monitoring to create comprehensive health data sets for physicians to analyze and diagnose. Nonetheless, several challenges continue to hinder the practical application of wearable electronics, such as skin compliance, biocompatibility, stability, and power supply. In this review, we address the power supply issue and examine recent innovative self-powered technologies for wearable electronics. Specifically, we explore self-powered sensors and self-powered systems, the two primary strategies employed in this field. The former emphasizes the integration of nanogenerator devices as sensing units, thereby reducing overall system power consumption, while the latter focuses on utilizing nanogenerator devices as power sources to drive the entire sensing system. Finally, we present the future challenges and perspectives for self-powered wearable electronics.

Machine Learning Methods for Small Data Challenges in Molecular Science.

Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.

Origin for electrochemically driven phase transformation in the oxygen electrode for a solid oxide cell.

The next generation of fuel cells, electrolyzers, and batteries requires higher power, faster kinetics, and larger energy density, which necessitate the use of compositionally complex oxides to achieve multifunctionalities and activity. These compositionally complex oxides may change their phases and structures during an electrochemical process-a so-called "electrochemically driven phase transformation." The origin for such a phase change has remained obscure. The aim of this paper is to present an experimental study and a theoretical analysis of phase evolution in praseodymium nickelates. Nickelate-based electrodes show up to 60 times greater phase transformation during operation when compared with thermally annealed ones. Theoretical analysis suggests that the presence of a reduced oxygen partial pressure at the interface between the oxygen electrode and the electrolyte is the origin for the phase change in an oxygen electrode. Guided by the theory, the addition of the electronic conduction in the interface layer leads to the significant suppression of phase change while improving cell performance and performance stability.

Ensembles of realistic power distribution networks.

The power grid is going through significant changes with the introduction of renewable energy sources and the incorporation of smart grid technologies. These rapid advancements necessitate new models and analyses to keep up with the various emergent phenomena they induce. A major prerequisite of such work is the acquisition of well-constructed and accurate network datasets for the power grid infrastructure. In this paper, we propose a robust, scalable framework to synthesize power distribution networks that resemble their physical counterparts for a given region. We use openly available information about interdependent road and building infrastructures to construct the networks. In contrast to prior work based on network statistics, we incorporate engineering and economic constraints to create the networks. Additionally, we provide a framework to create ensembles of power distribution networks to generate multiple possible instances of the network for a given region. The comprehensive dataset consists of nodes with attributes, such as geocoordinates; type of node (residence, transformer, or substation); and edges with attributes, such as geometry, type of line (feeder lines, primary or secondary), and line parameters. For validation, we provide detailed comparisons of the generated networks with actual distribution networks. The generated datasets represent realistic test systems (as compared with standard test cases published by Institute of Electrical and Electronics Engineers (IEEE)) that can be used by network scientists to analyze complex events in power grids and to perform detailed sensitivity and statistical analyses over ensembles of networks.

Tpgen: a language model for stable protein design with a specific topology structure.

BACKGROUND: Natural proteins occupy a small portion of the protein sequence space, whereas artificial proteins can explore a wider range of possibilities within the sequence space. However, specific requirements may not be met when generating sequences blindly. Research indicates that small proteins have notable advantages, including high stability, accurate resolution prediction, and facile specificity modification. RESULTS: This study involves the construction of a neural network model named TopoProGenerator(TPGen) using a transformer decoder. The model is trained with sequences consisting of a maximum of 65 amino acids. The training process of TopoProGenerator incorporates reinforcement learning and adversarial learning, for fine-tuning. Additionally, it encompasses a stability predictive model trained with a dataset comprising over 200,000 sequences. The results demonstrate that TopoProGenerator is capable of designing stable small protein sequences with specified topology structures. CONCLUSION: TPGen has the ability to generate protein sequences that fold into the specified topology, and the pretraining and fine-tuning methods proposed in this study can serve as a framework for designing various types of proteins.

Proformer: a hybrid macaron transformer model predicts expression values from promoter sequences.

The breakthrough high-throughput measurement of the cis-regulatory activity of millions of randomly generated promoters provides an unprecedented opportunity to systematically decode the cis-regulatory logic that determines the expression values. We developed an end-to-end transformer encoder architecture named Proformer to predict the expression values from DNA sequences. Proformer used a Macaron-like Transformer encoder architecture, where two half-step feed forward (FFN) layers were placed at the beginning and the end of each encoder block, and a separable 1D convolution layer was inserted after the first FFN layer and in front of the multi-head attention layer. The sliding k-mers from one-hot encoded sequences were mapped onto a continuous embedding, combined with the learned positional embedding and strand embedding (forward strand vs. reverse complemented strand) as the sequence input. Moreover, Proformer introduced multiple expression heads with mask filling to prevent the transformer models from collapsing when training on relatively small amount of data. We empirically determined that this design had significantly better performance than the conventional design such as using the global pooling layer as the output layer for the regression task. These analyses support the notion that Proformer provides a novel method of learning and enhances our understanding of how cis-regulatory sequences determine the expression values.

Fusing graph transformer with multi-aggregate GCN for enhanced drug-disease associations prediction.

BACKGROUND: Identification of potential drug-disease associations is important for both the discovery of new indications for drugs and for the reduction of unknown adverse drug reactions. Exploring the potential links between drugs and diseases is crucial for advancing biomedical research and improving healthcare. While advanced computational techniques play a vital role in revealing the connections between drugs and diseases, current research still faces challenges in the process of mining potential relationships between drugs and diseases using heterogeneous network data. RESULTS: In this study, we propose a learning framework for fusing Graph Transformer Networks and multi-aggregate graph convolutional network to learn efficient heterogenous information graph representations for drug-disease association prediction, termed WMAGT. This method extensively harnesses the capabilities of a robust graph transformer, effectively modeling the local and global interactions of nodes by integrating a graph convolutional network and a graph transformer with self-attention mechanisms in its encoder. We first integrate drug-drug, drug-disease, and disease-disease networks to construct heterogeneous information graph. Multi-aggregate graph convolutional network and graph transformer are then used in conjunction with neural collaborative filtering module to integrate information from different domains into highly effective feature representation. CONCLUSIONS: Rigorous cross-validation, ablation studies examined the robustness and effectiveness of the proposed method. Experimental results demonstrate that WMAGT outperforms other state-of-the-art methods in accurate drug-disease association prediction, which is beneficial for drug repositioning and drug safety research.

GraphKM: machine and deep learning for KM prediction of wildtype and mutant enzymes.

Michaelis constant (KM) is one of essential parameters for enzymes kinetics in the fields of protein engineering, enzyme engineering, and synthetic biology. As overwhelming experimental measurements of KM are difficult and time-consuming, prediction of the KM values from machine and deep learning models would increase the pace of the enzymes kinetics studies. Existing machine and deep learning models are limited to the specific enzymes, i.e., a minority of enzymes or wildtype enzymes. Here, we used a deep learning framework PaddlePaddle to implement a machine and deep learning approach (GraphKM) for KM prediction of wildtype and mutant enzymes. GraphKM is composed by graph neural networks (GNN), fully connected layers and gradient boosting framework. We represented the substrates through molecular graph and the enzymes through a pretrained transformer-based language model to construct the model inputs. We compared the difference of the model results made by the different GNN (GIN, GAT, GCN, and GAT-GCN). The GAT-GCN-based model generally outperformed. To evaluate the prediction performance of the GraphKM and other reported KM prediction models, we collected an independent KM dataset (HXKm) from literatures.

A comparison of embedding aggregation strategies in drug-target interaction prediction.

The prediction of interactions between novel drugs and biological targets is a vital step in the early stage of the drug discovery pipeline. Many deep learning approaches have been proposed over the last decade, with a substantial fraction of them sharing the same underlying two-branch architecture. Their distinction is limited to the use of different types of feature representations and branches (multi-layer perceptrons, convolutional neural networks, graph neural networks and transformers). In contrast, the strategy used to combine the outputs (embeddings) of the branches has remained mostly the same. The same general architecture has also been used extensively in the area of recommender systems, where the choice of an aggregation strategy is still an open question. In this work, we investigate the effectiveness of three different embedding aggregation strategies in the area of drug-target interaction (DTI) prediction. We formally define these strategies and prove their universal approximator capabilities. We then present experiments that compare the different strategies on benchmark datasets from the area of DTI prediction, showcasing conditions under which specific strategies could be the obvious choice.

Data Acquisition and Intraoperative Tissue Analysis on a Mobile, Battery-Operated, Orbitrap Mass Spectrometer.

Mass spectrometry has been increasingly explored in intraoperative studies as a potential technology to help guide surgical decision making. Yet, intraoperative experiments using high-performance mass spectrometry instrumentation present a unique set of operational challenges. For example, standard operating rooms are often not equipped with the electrical requirements to power a commercial mass spectrometer and are not designed to accommodate their permanent installation. These obstacles can impact progress and patient enrollment in intraoperative clinical studies because implementation of MS instrumentation becomes limited to specific operating rooms that have the required electrical connections and space. To expand our intraoperative clinical studies using the MasSpec Pen technology, we explored the feasibility of transporting and acquiring data on Orbitrap mass spectrometers operating on battery power in hospital buildings. We evaluated the effect of instrument movement including acceleration and rotational speeds on signal stability and mass accuracy by acquiring data using direct infusion electrospray ionization. Data were acquired while rolling the systems in/out of operating rooms and while descending/ascending a freight elevator. Despite these movements and operating the instrument on battery power, the relative standard deviation of the total ion current was <5% and the magnitude of the mass error relative to the internal calibrant never exceeded 5.06 ppm. We further evaluated the feasibility of performing intraoperative MasSpec Pen analysis while operating the Orbitrap mass spectrometer on battery power during an ovarian cancer surgery. We observed that the rich and tissue-specific molecular profile commonly detected from ovarian tissues was conserved when running on battery power. Together, these results demonstrate that Orbitrap mass spectrometers can be operated and acquire data on battery power while in motion and in rotation without losses in signal stability or mass accuracy. Furthermore, Orbitrap mass spectrometers can be used in conjunction to the MasSpec Pen while on battery power for intraoperative tissue analysis.