Chemometrics has the potential to embolden microfluidics to become that enabling technology for so long sought after. In this Feature article, we describe a historical perspective on microfluidics and its current challenges, a perspective on chemometric methods including response surface methodology (RSM), and how a combination of artificial neural network with experimental design (ANN-ED) have demonstrated promise in addressing basic microfluidic problems.
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| http://dx.doi.org/10.1021/ac504863y | DOI Listing |
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AI-based processing of future prepared foods: Progress and prospects.
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The prepared foods sector has grown rapidly in recent years, driven by the fast pace of modern living and increasing consumer demand for convenience. Prepared foods are taking an increasingly important role in the modern catering industry due to their ease of storage, transportation, and operation. However, their processing faces several challenges, including labor shortages, inefficient sorting, inadequate cleaning, unsafe cutting processes, and a lack of industry standards.
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Hunan Provincial Key Laboratory of Two-Dimensional Materials, State Key Laboratory for Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
Two-dimensional (2D) transition metal dichalcogenides (TMDs), such as WSe, are promising candidates for next-generation integrated circuits. However, the dependence of intrinsic properties of TMD devices on various processing steps remains largely unexplored. Here, using pristine p-type WSe devices as references, we comprehensively studied the influence of each step in traditional nanofabrication methods on device performance.
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Species identification of botanical products is a crucial aspect of research and regulatory compliance; however, botanical classification can be difficult, especially for morphologically similar species with overlapping genetic and metabolomic markers, like those in the genus Ocimum. Untargeted LC-MS metabolomics coupled with multivariate predictive modeling provides a potential avenue for improving herbal identity investigations, but the current dearth of reference materials for many botanicals limits the applicability of these approaches. This study investigated the potential of using greenhouse-grown authentic Ocimum to build predictive models for classifying commercially available Ocimum products.
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The two-dimensional lamellar materials disperse platinum sites and minimize noble-metal usage for fuel cells, while mass transport resistance at the stacked layers spurs device failure with a significant performance decline in membrane electrode assembly (MEA). Herein, we implant porous and rigid sulfonated covalent organic frameworks (COF) into the graphene-based catalytic layer for the construction of steric mass-charge channels, which highly facilitates the activity of oxygen reduction reactions in both the rotating disk electrode (RDE) measurements and MEA device tests. Specifically, the normalized mass activity is remarkably boosted by 3.
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