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Emma Stanley

Publications and source records attributed to Emma Stanley.

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A gas-phase "top-down" chemical link between aldehydes and alcohols

Alcohols and aldehydes represent two key classes of interstellar complex organic molecules (COMs). This work seeks to better understand their possible chemical connections, with a focus on such molecules in the sources of the star-forming region Sgr B2 (N). The gas-phase reaction between ethanol (CH3CH2OH) and the halogens fluorine and chlorine was investigated via DFT calculations, with the goal of determining whether astrochemically viable chemical pathways leading to acetaldehyde (CH3CHO) exist. The studied reactions were then included in an astrochemical model of Sgr B2 (N) to determine their significance under real interstellar conditions. Our DFT calculations revealed that both chlorine and fluorine can react barrierlessly with ethanol to abstract a hydrogen atom. We further found that, following this initial step, the resulting ethanol radicals can undergo further reactions with atomic hydrogen, with some routes leading to acetaldehyde. Incorporation of these novel reactions in astrochemical models of hot cores suggest that they are indeed efficient under those conditions, and can lead to modest increases in the abundance of CH3CHO during model times where gas-phase ethanol is abundant. Of the ethanol radicals included in our chemical network, we found CH3CHOH to have the highest abundances in our simulations comparable to that of ethanol at some model times. Overall, this work reveals a novel gas-phase ``top-down'' link from alcohols to aldehydes that compliments the better studied ``bottom-up'' routes involving grain-surface H-addition reactions yielding alcohols from aldehydes. Moreover, results from our astrochemical models suggest that the ethanol radical CH3CHOH may be detectable in the interstellar medium.

astro-ph.GA

Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems

Artificial Intelligence (AI) has paved the way for revolutionary decision-making processes, which if harnessed appropriately, can contribute to advancements in various sectors, from healthcare to economics. However, its black box nature presents significant ethical challenges related to bias and transparency. AI applications are hugely impacted by biases, presenting inconsistent and unreliable findings, leading to significant costs and consequences, highlighting and perpetuating inequalities and unequal access to resources. Hence, developing safe, reliable, ethical, and Trustworthy AI systems is essential. Our team of researchers working with Trustworthy and Responsible AI, part of the Transdisciplinary Scholarship Initiative within the University of Calgary, conducts research on Trustworthy and Responsible AI, including fairness, bias mitigation, reproducibility, generalization, interpretability, and authenticity. In this paper, we review and discuss the intricacies of AI biases, definitions, methods of detection and mitigation, and metrics for evaluating bias. We also discuss open challenges with regard to the trustworthiness and widespread application of AI across diverse domains of human-centric decision making, as well as guidelines to foster Responsible and Trustworthy AI models.

cs.AI