ABSTRACT
Electrochemical energy conversion and storage are playing an increasingly important role in shaping the sustainable future. Differential electrochemical mass spectrometry (DEMS) offers an operando and cost-effective tool to monitor the evolution of gaseous/volatile intermediates and products during these processes. It can deliver potential-, time-, mass- and space-resolved signals which facilitate the understanding of reaction kinetics. In this review, we show the latest developments and applications of DEMS in various energy-related electrochemical reactions from three distinct perspectives. (I) What is DEMS addresses the working principles and key components of DEMS, highlighting the new and distinct instrumental configurations for different applications. (II) How to use DEMS tackles practical matters including the electrochemical test protocols, quantification of both potential and mass signals, and error analysis. (III) Where to apply DEMS is the focus of this review, dealing with concrete examples and unique values of DEMS studies in both energy conversion applications (CO2 reduction, water electrolysis, carbon corrosion, N-related catalysis, electrosynthesis, fuel cells, photo-electrocatalysis and beyond) and energy storage applications (Li-ion batteries and beyond, metal-air batteries, supercapacitors and flow batteries). The recent development of DEMS-hyphenated techniques and the outlook of the DEMS technique are discussed at the end. As DEMS celebrates its 40th anniversary in 2024, we hope this review can offer electrochemistry researchers a comprehensive understanding of the latest developments of DEMS and will inspire them to tackle emerging scientific questions using DEMS.
ABSTRACT
Accurate recognition of patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) is important for the subsequent treatment and rehabilitation. Recently, with the fast development of artificial intelligence (AI), AI-assisted diagnosis has been widely used. Feature selection as a key component is very important in AI-assisted diagnosis. So far, many feature selection methods have been developed. However, few studies consider the stability of a feature selection method. Therefore, in this study, we introduce a frequency-based criterion to evaluate the stability of feature selection and design a pipeline to select feature selection methods considering both stability and discriminability. There are two main contributions of this study: (1) It designs a bootstrap sampling-based workflow to simulate real-world scenario of feature selection. (2) It develops a decision graph to determine the optimal combination of supervised and unsupervised feature selection both considering feature stability and discriminability. Experimental results on the ADNI dataset have demonstrated the feasibility of our method.
ABSTRACT
Machine learning-based models are widely used for neuroimage-based dementia recognition and achieve great success. However, most models omit the interpretability that is a very important factor regarding the confidence of a model. Takagi-Sugeno-Kang (TSK) fuzzy classifiers as the high interpretability and promising classification performance have widely used in many scenarios. TSK fuzzy classifier can generate interpretable fuzzy rules showing the reasoning process. However, when facing high-dimensional data, the antecedent become complex which may reduce the interpretability. In this study, to keep the antecedent of fuzzy rule concise, we introduce the subspace clustering technique and use it for antecedent learning. Experimental results show that the used model can generate promising recognition performance as well as concise fuzzy rules.
