In this review, recent advances regarding the integration of machine learning into electrochemical analysis are overviewed, focusing on the strategies to increase the analytical context of electrochemical data for enhanced machine learning applications. While information-rich electrochemical data offer great potential for machine learning applications, limitations arise when sensors struggle to identify or quantitatively detect target substances in a complex matrix of non-target substances. Advanced machine learning techniques are crucial, but equally important is the development of methods to ensure that electrochemical systems can generate data with reasonable variations across different targets or the different concentrations of a single target. We discuss five strategies developed for building such electrochemical systems, employed in the steps of preparing sensing electrodes, recording signals, and analyzing data. In addition, we explore approaches for acquiring and augmenting the datasets used to train and validate machine learning models. Through these insights, we aim to inspire researchers to fully leverage the potential of machine learning in electroanalytical science.

We report an enhanced magnetoelastic contribution to the Gilbert damping in highly magnetostrictive ${\mathrm{Fe}}_{0.7}{\mathrm{Ga}}_{0.3}$ thin films. This effect is mitigated for perpendicular-to-plane fields, leading to a large anisotropy of the Gilbert damping in all of the films (up to a factor of 10 at room temperature). These claims are supported by broadband measurements of the ferromagnetic resonance linewidths over a range of temperatures (5 to 400 K), which serve to elucidate the effect of both the magnetostriction and phonon relaxation on the magnetoelastic Gilbert damping.

Recent advancements in large language models (LLMs) such as ChatGPT have been transforming the ways we approach science tasks, including data analysis, experimental design, writing, and literature review. However, due to the lack of specialized knowledge and inherent issues such as plagiarism and hallucinations (i.e., false or misleading outputs), it is necessary for users to verify the output information. To address these issues, prompt engineering has become a significant task. In this study, we evaluate the performance of different prompt styles for extracting information from literature abstracts and emphasize the importance of prompt engineering for such scientific tasks. The literature on white phosphor materials is used for this study due to the availability of important and quantitative information in the abstracts. Through detailed comparative and quantitative evaluation, we provide guidance on preparing suitable and effective prompts based on the types of information sought.