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By merging plastic-based skin sensors with silicon integrated circuits, a flexible, wearable perspiration analysis system is presented that measures skin temperature and the metabolites and electrolytes in human sweat and analyses the information in situ . A wearable plastic sweat biosensor Human sweat is attracting attention as a carrier of biomarkers of potential diagnostic importance, as well as in drug abuse detection and athletic performance optimization. In particular, sweat is much more tractable than other body fluids for continuous bio-monitoring. This paper presents a fully integrated flexible sensor platform for sweat analysis, based on existing technologies. Ali Javey and colleagues successfully connect plastic-based skin sensors to conventional silicon integrated circuitry to achieve multiple simultaneous measurement of sweat metabolites (glucose and lactate) and electrolytes (sodium and potassium). Skin temperature was measured to provide in situ calibration of the sensors. A small cohort human subject validation was performed to demonstrate the practical value of the platform — and a specially designed Android app created — for real-time assessment of physiological status, either as a wristband or forehead patch. Wearable sensor technologies are essential to the realization of personalized medicine through continuously monitoring an individual’s state of health 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 . Sampling human sweat, which is rich in physiological information 13 , could enable non-invasive monitoring. Previously reported sweat-based and other non-invasive biosensors either can only monitor a single analyte at a time or lack on-site signal processing circuitry and sensor calibration mechanisms for accurate analysis of the physiological state 14 , 15 , 16 , 17 , 18 . Given the complexity of sweat secretion, simultaneous and multiplexed screening of target biomarkers is critical and requires full system integration to ensure the accuracy of measurements. Here we present a mechanically flexible and fully integrated (that is, no external analysis is needed) sensor array for multiplexed in situ perspiration analysis, which simultaneously and selectively measures sweat metabolites (such as glucose and lactate) and electrolytes (such as sodium and potassium ions), as well as the skin temperature (to calibrate the response of the sensors). Our work bridges the technological gap between signal transduction, conditioning (amplification and filtering), processing and wireless transmission in wearable biosensors by merging plastic-based sensors that interface with the skin with silicon integrated circuits consolidated on a flexible circuit board for complex signal processing. This application could not have been realized using either of these technologies alone owing to their respective inherent limitations. The wearable system is used to measure the detailed sweat profile of human subjects engaged in prolonged indoor and outdoor physical activities, and to make a real-time assessment of the physiological state of the subjects. This platform enables a wide range of personalized diagnostic and physiological monitoring applications.

Improved analytical tools are urgently required to identify degradation and failure mechanisms in Li-ion batteries. However, understanding and ultimately avoiding these detrimental mechanisms requires continuous tracking of complex electrochemical processes in different battery components. Here, we report an operando spectroscopy method that enables monitoring the chemistry of a carbonate-based liquid electrolyte during electrochemical cycling in Li-ion batteries with a graphite anode and a LiNi 0.8 Mn 0.1 Co 0.1 O 2 cathode. By embedding a hollow-core optical fibre probe inside a lab-scale pouch cell, we demonstrate the effective evolution of the liquid electrolyte species by background-free Raman spectroscopy. The analysis of the spectroscopy measurements reveals changes in the ratio of carbonate solvents and electrolyte additives as a function of the cell voltage and show the potential to track the lithium-ion solvation dynamics. The proposed operando methodology contributes to understanding better the current Li-ion battery limitations and paves the way for studies of the degradation mechanisms in different electrochemical energy storage systems. New analytical tools are needed to identify chemical degradation and failure mechanisms in Li-ion batteries. Here, the authors report an operando Raman spectroscopy method, based on hollow-core optical fibres, that enables monitoring the chemistry of liquid electrolytes during battery cycling.

Accurate 3D representations of lithium-ion battery electrodes, in which the active particles, binder and pore phases are distinguished and labeled, can assist in understanding and ultimately improving battery performance. Here, we demonstrate a methodology for using deep-learning tools to achieve reliable segmentations of volumetric images of electrodes on which standard segmentation approaches fail due to insufficient contrast. We implement the 3D U-Net architecture for segmentation, and, to overcome the limitations of training data obtained experimentally through imaging, we show how synthetic learning data, consisting of realistic artificial electrode structures and their tomographic reconstructions, can be generated and used to enhance network performance. We apply our method to segment x-ray tomographic microscopy images of graphite-silicon composite electrodes and show it is accurate across standard metrics. We then apply it to obtain a statistically meaningful analysis of the microstructural evolution of the carbon-black and binder domain during battery operation. Accurate 3D representations of lithium-ion battery electrodes can help in understanding and ultimately improving battery performance. Here, the authors report a methodology for using deep-learning tools to reliably distinguish the different electrode material phases where standard approaches fail.

The monitoring of currents in the abyssal ocean is an essential foundation of deep-sea research. The state-of-the-art current meter has limitations such as the requirement of a power supply for signal transduction, low pressure resistance, and a narrow measurement range. Here, we report a fully integrated, self-powered, highly sensitive deep-sea current measurement system in which the ultra-sensitive triboelectric nanogenerator harvests ocean current energy for the self-powered sensing of tiny current motions down to 0.02 m/s. Through an unconventional magnetic coupling structure, the system withstands immense hydrostatic pressure exceeding 45 MPa. A variable-spacing structure broadens the measuring range to 0.02–6.69 m/s, which is 67% wider than that of commercial alternatives. The system successfully operates at a depth of 4531 m in the South China Sea, demonstrating the record-deep operations of triboelectric nanogenerator-based sensors in deep-sea environments. Our results show promise for sustainable ocean current monitoring with higher spatiotemporal resolution. This study shows a self-powered deep-sea current measurement system using a triboelectric nanogenerator (TENG) that measures currents from 0.02 to 6.69 m/s and withstands over 45 MPa pressure. Successful operation at 4531 m depth in the South China Sea is demonstrated.

Modern scanning microscopes can image materials with up to sub-atomic spatial and sub-picosecond time resolutions, but these capabilities come with large volumes of data, which can be difficult to store and analyze. We report the Fast Autonomous Scanning Toolkit (FAST) that addresses this challenge by combining a neural network, route optimization, and efficient hardware controls to enable a self-driving experiment that actively identifies and measures a sparse but representative data subset in lieu of the full dataset. FAST requires no prior information about the sample, is computationally efficient, and uses generic hardware controls with minimal experiment-specific wrapping. We test FAST in simulations and a dark-field X-ray microscopy experiment of a WSe 2 film. Our studies show that a FAST scan of <25% is sufficient to accurately image and analyze the sample. FAST is easy to adapt for any scanning microscope; its broad adoption will empower general multi-level studies of materials evolution with respect to time, temperature, or other parameters. Modern microscopes can image a sample with sub-Angstrom and sub-picosecond resolutions, but this often requires analysis of tremendously large datasets. Here, the authors demonstrate that an autonomous experiment can yield over a 70% reduction in dataset size while still producing high-fidelity images of the sample.

Divertor detachment offers a promising solution to the challenge of plasma-wall interactions for steady-state operation of fusion reactors. Here, we demonstrate the excellent compatibility of actively controlled full divertor detachment with a high-performance ( β N ~ 3, H 98 ~ 1.5) core plasma, using high-β p (poloidal beta, β p  > 2) scenario characterized by a sustained core internal transport barrier (ITB) and a modest edge transport barrier (ETB) in DIII-D tokamak. The high- β p high-confinement scenario facilitates divertor detachment which, in turn, promotes the development of an even stronger ITB at large radius with a weaker ETB. This self-organized synergy between ITB and ETB, leads to a net gain in energy confinement, in contrast to the net confinement loss caused by divertor detachment in standard H-modes. These results show the potential of integrating excellent core plasma performance with an efficient divertor solution, an essential step towards steady-state operation of reactor-grade plasmas. Plasma fusion devices like tokamaks are important for energy generation but there are many challenges for their steady state operation. Here, the authors show that full divertor detachment is compatible with high-confinement high-poloidal-beta core plasmas and this prevents the damage to the divertor target plates and the first wall.

Laser powder bed fusion is a promising technology for local deposition and microstructure control, but it suffers from defects such as delamination and porosity due to the lack of understanding of melt pool dynamics. To study the fundamental behavior of the melt pool, both geometric and thermal sensing with high spatial and temporal resolutions are necessary. This work applies and integrates three advanced sensing technologies: synchrotron X-ray imaging, high-speed IR camera, and high-spatial-resolution IR camera to characterize the evolution of the melt pool shape, keyhole, vapor plume, and thermal evolution in Ti–6Al–4V and 410 stainless steel spot melt cases. Aside from presenting the sensing capability, this paper develops an effective algorithm for high-speed X-ray imaging data to identify melt pool geometries accurately. Preprocessing methods are also implemented for the IR data to estimate the emissivity value and extrapolate the saturated pixels. Quantifications on boundary velocities, melt pool dimensions, thermal gradients, and cooling rates are performed, enabling future comprehensive melt pool dynamics and microstructure analysis. The study discovers a strong correlation between the thermal and X-ray data, demonstrating the feasibility of using relatively cheap IR cameras to predict features that currently can only be captured using costly synchrotron X-ray imaging. Such correlation can be used for future thermal-based melt pool control and model validation.

Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains, informatics strategies are beginning to take shape within materials science. These approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly solved. Data-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods—due to the cost, time or effort involved—but for which reliable data either already exists or can be generated for at least a subset of the critical cases. Predictions are typically interpolative, involving fingerprinting a material numerically first, and then following a mapping (established via a learning algorithm) between the fingerprint and the property of interest. Fingerprints, also referred to as “descriptors”, may be of many types and scales, as dictated by the application domain and needs. Predictions may also be extrapolative—extending into new materials spaces—provided prediction uncertainties are properly taken into account. This article attempts to provide an overview of some of the recent successful data-driven “materials informatics” strategies undertaken in the last decade, with particular emphasis on the fingerprint or descriptor choices. The review also identifies some challenges the community is facing and those that should be overcome in the near future.

High brightness, high charge electron beams are critical for a number of advanced accelerator applications. The initial emittance of the electron beam, which is determined by the mean transverse energy (MTE) and laser spot size, is one of the most important parameters determining the beam quality. The bialkali photocathodes illuminated by a visible laser have the advantages of high quantum efficiency (QE) and low MTE. Furthermore, Superconducting Radio Frequency (SRF) guns can operate in the continuous wave (CW) mode at high accelerating gradients, e.g. with significant reduction of the laser spot size at the photocathode. Combining the bialkali photocathode with the SRF gun enables generation of high charge, high brightness, and possibly high average current electron beams. However, integrating the high QE semiconductor photocathode into the SRF guns has been challenging. In this article, we report on the development of bialkali photocathodes for successful operation in the SRF gun with months-long lifetime while delivering CW beams with nano-coulomb charge per bunch. This achievement opens a new era for high charge, high brightness CW electron beams.

X-ray Free Electron Lasers provide femtosecond x-ray pulses with narrow bandwidth and unprecedented peak brightness. Special modes of operation have been developed to deliver double pulses for x-ray pump, x-ray probe experiments. However, the longest delay between the two pulses achieved with existing single bucket methods is less than 1 picosecond, thus preventing the exploration of longer time-scale dynamics. We present a novel two-bucket scheme covering delays from 350 picoseconds to hundreds of nanoseconds in discrete steps of 350 picoseconds. Performance for each pulse can be similar to the one in a single pulse operation. The method has been experimentally tested with the Linac Coherent Light Source (LCLS-I) and the copper linac with LCLS-II hard x-ray undulators.