Accurately predicting lifetime of complex systems like lithium-ion batteries is crucial for accelerating technology development. However, diverse aging 1 mechanisms, significant device variability, and varied operating conditions have remained major challenges. To study this problem, we generated a dataset consisting of 124 commercial lithium-iron-phosphate/graphite cells cycled under fast charging conditions. The cells exhibited widely varied cycle lives spanning from 150 to 2,300 cycles, with end-of-life defined as 20% degradation from nominal capacity. Using discharge voltage curves from early cycles yet to exhibit capacity degradation, we apply machine learning tools to predict cycle life with less than 15% error on average, which is improved to ~8% error by incorporating additional data. Our work represents a significant improvement over previous predictions that generally required data corresponding to >5% capacity degradation, without needing specialized diagnostics. Additionally, it highlights the promise of combining data generation with data-driven modeling to predict the behavior of complex and variable systems. Main Lithium-ion batteries are deployed in a wide range of applications due to their low and falling costs, high energy densities, and long cycle lives. 1-3 However, as is the case with many chemical, mechanical, and electronics systems, long battery cycle life implies delayed feedback of performance during development and manufacture, often many months to years. Accurately predicting cycle life using early-cycle data would accelerate this feedback loop as well as enable estimation of battery life expectancy for use in consumer electronics, electric vehicles, and second-life applications. 4-6

Extended Data Fig. 5 | Mean and standard deviation of the CLO-estimated predicted distribution over cycle lives after round 4. In this two-dimensional representation, mean estimated cycle life (colour scale) and standard deviation of cycle life (marker size) after round 4 are presented as a function of CC1, CC2 and CC3 (the x axis, y axis and panels a-f, respectively). Panels a-f represent CC3 = 3.6C, 4.0C, 4.4C, 4.8C, 5.2C, 5.6C and 6.0C, respectively. CC4 is represented by the contour lines. Note that the protocols with the highest cycle lives generally have the smallest standard deviations, since these protocols have been tested repeatedly.

Replacing fossil fuels with energy sources and carriers that are sustainable, environmentally benign, and affordable is amongst the most pressing challenges for future socio-economic development.

This multiauthor review article aims to bring readers up to date with some of the current trends in the field of process analytical technology (PAT) by summarizing each aspect of the subject (sensor development, PAT based process monitoring and control methods) and presenting applications both in industrial laboratories and in manufacture e.g. at GSK, AstraZeneca and Roche. Furthermore, the paper discusses the PAT paradigm from the regulatory science perspective. Given the multidisciplinary nature of PAT, such an endeavour would be almost impossible for a single author, so the concept of a multiauthor review was born. Each section of the multiauthor review has been written by a single expert or group of experts with the aim to report on its own research results. This paper also serves as a comprehensive source of information on PAT topics for the novice reader.

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A novel continuous crystallizer design is described with the potential to provide improved control of crystal properties, improved process reproducibility, and reduced scale-up risk. Liquid and gas are introduced into one end of the tube at flow rates selected to spontaneously generate alternating slugs of liquid and gas that remain stable while cooling crystallization occurs in each liquid slug. Mixing within each stable self-circulating slug is maximized by controlling the slug aspect ratio through specification of liquid and gas flow rates. The crystallizer is designed so that nucleation and growth processes are decoupled to enhance the individual control of each phenomenon. Coaxial or radial mixers combine liquid streams to generate seed crystals immediately upstream of the growth zone where nucleation is minimized, and crystal growth is controlled by the varying temperature profile along the length of the tube. The slug-flow crystallizer design is experimentally demonstrated to generate large uniform crystals of L-asparagine monohydrate in less than 5 min.

Infections associated with orthopedic implants cause increased morbidity and significant healthcare cost. A prolonged and expensive two-stage procedure requiring two surgical steps and a 6-8 week period of joint immobilization exists as today's gold standard for the revision arthroplasty of an infected prosthesis. Because infection is much more common in implant replacement surgeries, these issues greatly impact long-term patient care for a continually growing part of the population. Here, we demonstrate that a single-stage revision using prostheses coated with self-assembled, hydrolytically degradable multilayers that sequentially deliver the antibiotic (gentamicin) and the osteoinductive growth factor (BMP-2) in a time-staggered manner enables both eradication of established biofilms and complete and rapid bone tissue repair around the implant in rats with induced osteomyelitis. The nanolayered construct allows precise independent control of release kinetics and loading for each therapeutic agent in an infected implant environment. Antibiotics contained in top layers can be tuned to provide a rapid release at early times sufficient to eliminate infection, followed by sustained release for several weeks, and the underlying BMP-2 component enables a long-term sustained release of BMP-2, which induced more significant and mechanically competent bone formation than a short-term burst release. The successful growth factor-mediated osteointegration of the multilayered implants with the host tissue improved bone-implant interfacial strength 15-fold when compared with the uncoated one. These findings demonstrate the potential of this layered release strategy to introduce a durable next-generation implant solution, ultimately an important step forward to future large animal models toward the clinic.

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