Annual Review of Analytical Chemistry - Early Publication

Mass spectrometry (MS) has become an indispensable tool for the detailed chemical analysis of materials used in energy production, spanning both traditional fossil fuels and modern renewable alternatives. This review explores advanced ionization sources and ultrahigh-resolution MS technologies in analyzing energy materials such as petroleum, biomass, biofuels, and bio-oil. Highlighted ionization techniques include electrospray ionization, atmospheric pressure chemical ionization, atmospheric pressure photoionization, laser desorption/ionization, and matrix-assisted laser desorption/ionization, all crucial for qualitative and quantitative assessments, as well as ultrahigh-resolution Fourier transform ion cyclotron resonance and Orbitrap mass analyzers. This review underscores the remarkable compositional detail achievable with state-of-the-art MS systems, providing molecular-level insights vital for advancing energy sectors. Introducing the concept of harvesting MS, we illustrate how these techniques can overcome challenges and optimize energy operations. Through case studies, this article highlights how these insights enhance energy production efficiency and sustainability, paving the way for future innovations.

Tandem mass spectrometry (MS/MS) is crucial for small-molecule analysis; however, traditional computational methods are limited by incomplete reference libraries and complex data processing. Machine learning (ML) is transforming small-molecule mass spectrometry in three key directions: (a) predicting MS/MS spectra and related physicochemical properties to expand reference libraries, (b) improving spectral matching through automated pattern extraction, and (c) predicting molecular structures of compounds directly from their MS/MS spectra. We review ML approaches for molecular representations [descriptors, simplified molecular-input line-entry (SMILE) strings, and graphs] and MS/MS spectra representations (using binned vectors and peak lists) along with recent advances in spectra prediction, retention time, collision cross sections, and spectral matching. Finally, we discuss ML-integrated workflows for chemical formula identification. By addressing the limitations of current methods for compound identification, these ML approaches can greatly enhance the understanding of biological processes and the development of diagnostic and therapeutic tools.

Forensic analytical chemistry has evolved significantly, embracing a myriad of methodological and technological advancements to expand the frontiers of evidence analysis. Beyond technology, modern forensic scientists face challenges working within the criminal justice system where scientific operational and research choices are directed by law enforcement agencies. This review examines issues surrounding the accuracy of presumptive tests, the use of portable instrumentation, and sample contamination, as exemplified by field drug testing. Data management and preservation are discussed, including the integration of machine learning into forensic workflows and the critical need for transparency to stakeholders. Finally, the operational interpretation and translation of analytical results and the role of forensic laboratories as high-reliability organizations are explored. Addressing the disparities and ensuring the credibility of forensic methods are essential for promoting reliability and equity within the justice system.

Understanding the biophysical properties of cells is essential for biological research, diagnostics, and therapeutics. Microfluidics enhances biophysical cell characterization by enabling precise manipulation and real-time measurement at the microscale. However, the high-throughput nature of microfluidic systems generates vast amounts of data, complicating analysis. Integrating artificial intelligence (AI) methods, including machine learning and deep learning, with microfluidic technologies addresses these challenges. AI excels at analyzing large, complex datasets, improving the accuracy and efficiency of microfluidic experiments and facilitating new biological discoveries. This review examines the synergy between microfluidics and machine learning for biophysical cell characterization, categorizing existing methods based on the types of input data used for machine learning analysis, highlighting recent advancements, and discussing challenges and future directions in this interdisciplinary field.

The design and implementation of biomedical devices for both diagnostic and direct medical applications have revolutionized patient care, paving the way for improved patient outcomes. Understanding the characteristics of materials used in the design of new devices is essential for their advancement. In this review, our goal is to assist biomedical researchers in appreciating the importance of these properties and the role of selecting the proper measurement. We discuss how the nanoscopic molecular composition, arrangement, and interactions generate the properties of liquids, solids, viscoelastic materials, and colloids and discuss the measurement techniques that can be used to assess these properties from the nanoscale to the macroscale. We explore the linear and nonlinear mechanical responses of materials, elucidate their behaviors under varying conditions, and discuss corresponding measurement techniques. Finally, we highlight the importance of tailoring measurements to the underlying biological processes and applications being investigated.

Nanoparticles have broad applications in medical diagnosis, bioengineering, and various other domains. Engineered nanoparticles are pivotal in enhancing in vitro detection performance. Utilizing signal readouts such as bioluminescence, fluorescence, and others, nanoparticles enable precise, real-time monitoring of a wide range of analytes. Furthermore, nanoparticles are ingeniously designed to improve the efficiency of in vivo imaging. Nanoparticles are integral to both deep-body imaging, which utilizes advanced techniques such as magnetic resonance imaging or computed tomography (CT), and high-resolution, real-time optical imaging, which are essential for applications such as fluorescence-guided surgery. Here, we highlight these impactful diagnostic assays enabled by engineered nanoparticles, comparing their advantages and disadvantages extensively. For the introduction of each method, we compare the most classic and latest research as much as possible to provide a comprehensive perspective. Finally, we summarize the current limitations and challenges of nanoparticles for diagnostic analysis while also exploring future trends and prospects.

Mass spectrometry (MS)-based top-down proteomics (TDP) characterizes proteoforms in cells, tissues, and biological fluids (e.g., human plasma) to better our understanding of protein function and to discover new protein biomarkers for disease diagnosis and therapeutic development. Separations of proteoforms with high peak capacity are needed due to the high complexity of biological samples. Capillary electrophoresis (CE)-MS has been recognized as a powerful analytical tool for protein analysis since the 1980s owing to its high separation efficiency and sensitivity of CE-MS for proteoforms. Here, we review benefits of CE-MS for advancing TDP, challenges and solutions of the method, and the main research areas in which CE-MS-based TDP can make significant contributions. We provide a brief perspective of CE-MS-based TDP moving forward.

Mass spectrometry–based proteomics and metaproteomics have long been used in the study of human microbiomes, with the potential of metaproteomics only recently being fully harnessed. This progress is due to the advancements of high-performance mass spectrometers, innovative proteomics strategies, and the development of dedicated bioinformatics tools. In this review, we critically examine the recent technological developments that enhance the application of metaproteomics in clinical microbiome analysis. We also summarize significant advancements in the application of metaproteomics to study human microbiomes across various body sites under disease conditions. Despite these, the potential of metaproteomics remains underutilized due to typically small sample sizes and insufficient data mining. We thereby highlight some key aspects that could facilitate the broader and more effective application of mass spectrometry–based metaproteomics in clinical microbiome analysis, including the development of microbiome assays for translational research and application.