Sustainable alternatives to microplastics from degradable polymer microparticles
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Sustainable supply chain management practices and performance: The moderating effect of stakeholder pressure
Currently, sustainable supply chain management practices have become an important strategy for firms to improve performance and gain competitive advantage. However, there is a current debate over the performance outcomes of sustainable supply chain management practices. Additionally, the role of stakeholder pressure is frequently overlooked. Drawing on Natural Resources-Based View and Stakeholder Theory, this study aims to elucidate the ambiguous connection between sustainable supply management, sustainable process management, stakeholder pressure and performance, and investigate the mediation role of sustainable process management and the moderation effect of stakeholder pressure. Our analysis, based on data collected from 235 Chinese manufacturing firms, reveals significant insights. First, stakeholder pressure positively moderates the relationship between sustainable process management and performance, while negatively moderates the relationship between sustainable supply management and performance. Second, sustainable process management has a complete mediation effect on the relationship between sustainable supply management and performance. The conclusion not only explains the inconsistent relationship between sustainable supply chain management practice and performance, but also reveals clearly the relationship between sustainable supply management and sustainable process management. Besides, it also highlights the difference in performance outcomes of sustainable supply management and sustainable process management under stakeholder pressures, and has valuable guidance to the practice of sustainable supply chain management in Chinese manufacturing firms.
On-patient medical record and mRNA therapeutics using intradermal microneedles
Medical interventions often require timed series of doses, thus necessitating accurate medical record-keeping. In many global settings, these records are unreliable or unavailable at the point of care, leading to less effective treatments or disease prevention. Here we present an invisible-to-the-naked-eye on-patient medical record-keeping technology that accurately stores medical information in the patient skin as part of microneedles that are used for intradermal therapeutics. We optimize the microneedle design for both a reliable delivery of messenger RNA (mRNA) therapeutics and the near-infrared fluorescent microparticles that encode the on-patient medical record-keeping. Deep learning-based image processing enables encoding and decoding of the information with excellent temporal and spatial robustness. Long-term studies in a swine model demonstrate the safety, efficacy and reliability of this approach for the co-delivery of on-patient medical record-keeping and the mRNA vaccine encoding severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This technology could help healthcare workers make informed decisions in circumstances where reliable record-keeping is unavailable, thus contributing to global healthcare equity.
Advancing extrapolative predictions of material properties through learning to learn using extrapolative episodic training
Recent advancements in machine learning have demonstrated its potential to significantly accelerate the discovery of new materials. Central to this progress is the development of rapidly computable property predictors, which allow identifying novel materials with the desired properties from vast material spaces. However, the limited availability of data resources poses a significant challenge in data-driven material research, particularly hindering the exploration of innovative materials beyond the boundaries of existing data. Although machine-learning predictors are inherently interpolative, establishing a general methodology to create an extrapolative predictor remains a fundamental challenge. In this study, we leveraged the attention-based architecture of neural networks and a meta-learning algorithm to enhance extrapolative generalization capabilities. Meta-learners trained repeatedly on arbitrarily generated extrapolative tasks show outstanding generalization for unexplored material spaces. Through the tasks of predicting the physical properties of polymeric materials and hybrid organic–inorganic perovskites, we highlight the potential of such extrapolatively trained models, particularly their ability to rapidly adapt to unseen material domains in transfer-learning scenarios.
Anti-icing properties of nonionic/hydrophilic concentrated polymer brushes and mechanistic insights via their swollen-state analysis
Anti-icing surfaces are important to prevent snow and ice accumulation, which can pose significant risks. Here, we analyze the anti-icing performance of concentrated polymer brushes (CPBs) consisting of a versatile nonionic/hydrophilic monomer and discuss the low-temperature properties of the CPB-retaining water. The anti-icing functionality is evaluated by measuring the ice adhesion strength as a function of the temperature and the structural parameters (e.g., density and length) of the polymer brushes. We demonstrate that only the CPB region (σ* ≥ 0.15) exhibits both high anti-icing functionality and excellent durability. Furthermore, the thickening of the CPBs is key to achieving a detailed characterization of the water present in the CPBs at low temperatures using in situ microscopic Fourier-transform infrared spectroscopy and differential scanning calorimetry. These results suggest that the water effectively remaining via quasi-equilibrium partial deswelling formed a lubricating layer, contributing to high anti-icing functionality and durability.
3D printing of micro-nano devices and their applications
In recent years, the utilization of 3D printing technology in micro and nano device manufacturing has garnered significant attention. Advancements in 3D printing have enabled achieving sub-micron level precision. Unlike conventional micro-machining techniques, 3D printing offers versatility in material selection, such as polymers. 3D printing technology has been gradually applied to the general field of microelectronic devices such as sensors, actuators and flexible electronics due to its adaptability and efficacy in microgeometric design and manufacturing processes. Furthermore, 3D printing technology has also been instrumental in the fabrication of microfluidic devices, both through direct and indirect processes. This paper provides an overview of the evolving landscape of 3D printing technology, delineating the essential materials and processes involved in fabricating microelectronic and microfluidic devices in recent times. Additionally, it synthesizes the diverse applications of these technologies across different domains.
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