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Abstract Obtaining in situ measurements of biological microparticles is crucial for both scientific research and numerous industrial applications (e.g., early detection of harmful algal blooms, monitoring yeast during fermentation). However, existing methods are limited to offer timely diagnostics of these particles with sufficient accuracy and information. Here, we introduce a novel method for real‐time, in situ analysis using machine learning (ML)‐assisted digital inline holography (DIH). Our ML model uses a customized YOLOv5 architecture specialized for the detection and classification of small biological particles. We demonstrate the effectiveness of our method in the analysis of 10 plankton species with equivalent high accuracy and significantly reduced processing time compared to previous methods. We also applied our method to differentiate yeast cells under four metabolic states and from two strains. Our results show that the proposed method can accurately detect and differentiate cellular and subcellular features related to metabolic states and strains. This study demonstrates the potential of ML‐driven DIH approach as a sensitive and versatile diagnostic tool for real‐time, in situ analysis of both biotic and abiotic particles. This method can be readily deployed in a distributive manner for scientific research and manufacturing on an industrial scale. 

Abstract Complex graphene electrode fabrication protocols including conventional chemical vapor deposition and graphene transfer techniques as well as more recent solution‐phase printing and postprint annealing methods have hindered the wide‐scale implementation of electrochemical devices including solid‐state ion‐selective electrodes (ISEs). Herein, a facile graphene ISE fabrication technique that utilizes laser induced graphene (LIG), formed by converting polyimide into graphene by a CO₂laser and functionalization with ammonium ion (NH₄⁺) and potassium ion (K⁺) ion‐selective membranes, is demonstrated. The electrochemical LIG ISEs exhibit a wide sensing range (0.1 × 10⁻³–150 × 10⁻³mfor NH₄⁺and 0.3 × 10⁻³–150 × 10⁻³mfor K⁺) with high stability (minimal drop in signal after 3 months of storage) across a wide pH range (3.5–9.0). The LIG ISEs are also able to monitor the concentrations of NH₄⁺and K⁺in urine samples (29–51% and 17–61% increase for the younger and older patient; respectively, after dehydration induction), which correlate well with conventional hydration status measurements. Hence, these results demonstrate a facile method to perform in‐field ion sensing and are the first steps in creating a protocol for quantifying hydration levels through urine testing in human subjects.