Automated Mixture Analysis via Structural Evaluation.

The determination of chemical mixture components is vital to a multitude of scientific fields. Oftentimes spectroscopic methods are employed to decipher the composition of these mixtures. However, the sheer density of spectral features present in spectroscopic databases can make unambiguous assignment to individual species challenging. Yet, components of a mixture are commonly chemically related due to environmental processes or shared precursor molecules. Therefore, analysis of the chemical relevance of a molecule is important when determining which species are present in a mixture. In this paper, we combine machine-learning molecular embedding methods with a graph-based ranking system to determine the likelihood of a molecule being present in a mixture based on the other known species and/or chemical priors. By incorporating this metric in a rotational spectroscopy mixture analysis algorithm, we demonstrate that the mixture components can be identified with extremely high accuracy (≥97%) in an efficient manner.

Evaluating generative AI responses to real-world drug-related questions.

Generative Artificial Intelligence (AI) systems such as OpenAI's ChatGPT, capable of an unprecedented ability to generate human-like text and converse in real time, hold potential for large-scale deployment in clinical settings such as substance use treatment. Treatment for substance use disorders (SUDs) is particularly high stakes, requiring evidence-based clinical treatment, mental health expertise, and peer support. Thus, promises of AI systems addressing deficient healthcare resources and structural bias are relevant within this domain, especially in an anonymous setting. This study explores the effectiveness of generative AI in answering real-world substance use and recovery questions. We collect questions from online recovery forums, use ChatGPT and Meta's LLaMA-2 for responses, and have SUD clinicians rate these AI responses. While clinicians rated the AI-generated responses as high quality, we discovered instances of dangerous disinformation, including disregard for suicidal ideation, incorrect emergency helplines, and endorsement of home detox. Moreover, the AI systems produced inconsistent advice depending on question phrasing. These findings indicate a risky mix of seemingly high-quality, accurate responses upon initial inspection that contain inaccurate and potentially deadly medical advice. Consequently, while generative AI shows promise, its real-world application in sensitive healthcare domains necessitates further safeguards and clinical validation.

Ozonolysis can produce long-lived greenhouse gases from commercial refrigerants.

Hydrofluoroolefins are being adopted as sustainable alternatives to long-lived fluorine- and chlorine-containing gases and are finding current or potential mass-market applications as refrigerants, among a myriad of other uses. Their olefinic bond affords relatively rapid reaction with hydroxyl radicals present in the atmosphere, leading to short lifetimes and proportionally small global warming potentials. However, this type of functionality also allows reaction with ozone, and whilst these reactions are slow, we show that the products of these reactions can be extremely long-lived. Our chamber measurements show that several industrially important hydrofluoroolefins produce CHF3 (fluoroform, HFC-23), a potent, long-lived greenhouse gas. When this process is accounted for in atmospheric chemical and transport modeling simulations, we find that the total radiative effect of certain compounds can be several times that of the direct radiative effect currently recommended by the World Meteorological Organization. Our supporting quantum chemical calculations indicate that a large range of exothermicity is exhibited in the initial stages of ozonolysis, which has a powerful influence on the CHF3 yield. Furthermore, we identify certain molecular configurations that preclude the formation of long-lived greenhouse gases. This demonstrates the importance of product quantification and ozonolysis kinetics in determining the overall environmental impact of hydrofluoroolefin emissions.