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1. Dynamic Covalent Optical Chirality Sensing with a Sterically Encumbered Aminoborane

   https://doi.org/10.1002/chem.202202028  

   De_los_Santos, Zeus_A; Lynch, Ciarán_C; Wolf, Christian (August 2022, Chemistry – A European Journal)

   Abstract A sterically encumbered aminoborane sensor is introduced and used for quantitative stereochemical analysis of monoalcohols, diols and amino alcohols. The small‐molecule probe exhibits a rigid ortho‐substituted arene scaffold with a proximate boron binding site and a triarylamine circular dichroism (CD) reporter unit which proved to be crucial for the observed chiroptical signal induction. Coordination of the chiral target molecule produces strong Cotton effects and UV changes that are readily correlated to its absolute configuration, enantiomeric composition and concentration to achieve comprehensive stereochemical analysis within a 5 % absolute error margin. The sensing method was successfully applied in the chromatography‐free analysis of less than one milligram of a crude asymmetric reaction mixture and the advantages of this chiroptical sensing approach, which is amenable to high‐throughput experimentation equipment and automation, over traditional methods is discussed. 

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2. Using deep learning to design high aspect ratio fusion devices

   https://doi.org/10.1017/S002237782400165X  

   Curvo, P; Ferreira, DR; Jorge, R (February 2025, Journal of Plasma Physics)

   The design of fusion devices is typically based on computationally expensive simulations. This can be alleviated using high aspect ratio models that employ a reduced number of free parameters, especially in the case of stellarator optimization where non-axisymmetric magnetic fields with a large parameter space are optimized to satisfy certain performance criteria. However, optimization is still required to find configurations with properties such as low elongation, high rotational transform, finite beta and good fast particle confinement. In this work, we train a machine learning model to construct configurations with favourable confinement properties by finding a solution to the inverse design problem, that is, obtaining a set of model input parameters for given desired properties. Since the solution of the inverse problem is non-unique, a probabilistic approach, based on mixture density networks, is used. It is shown that optimized configurations can be generated reliably using this method. 

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3. Catalytic intramolecular hydroamination of aminoallenes using titanium and tantalum complexes of sterically encumbered chiral sulfonamides

   https://doi.org/10.1039/D0DT02557G  

   Sha, Fanrui; Shimizu, Emily A.; Slocumb, Hannah S.; Towell, Sydney E.; Zhen, Yi; Porter, Hanna Z.; Takase, Michael; Johnson, Adam (January 2020, Dalton Transactions)

   Catalysis using earth abundant metals is an important goal due to the relative scarcity and expense of precious metal catalysts. It would be even more beneficial to use earth abundant catalysts for the synthesis of common pharmaceutical structural motifs such as pyrrolidine and pyridine. Thus, developing titanium catalysts for asymmetric ring closing hydroamination is a valuable goal. In this work, four sterically encumbered chiral sulfonamides derived from naturally occurring amino acids were prepared. These compounds undergo protonolysis reactions with Ti(NMe 2 ) 4 or Ta(NMe 2 ) 5 to give monomeric complexes as determined by both DOSY NMR and X-ray crystallography. The resulting complexes are active for the ring closing hydroamination hepta-4,5-dienylamine to give a mixture of tetrahydropyridine and pyrrolidine products. However, the titanium complexes convert 6-methylhepta-4,5-dienylamine exclusively to 2-(2-methylpropenyl)pyrrolidine in higher enantioselectivity than those previously reported, with enantiomeric excesses ranging from 18-24%. The corresponding tantalum complexes were more selective with enantiomeric excesses ranging from 33-39%. 

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4. Assessing the performance of rotational spectroscopy in chiral analysis

   https://doi.org/10.1039/D0SC03752D  

   Domingos, Sérgio R.; Pérez, Cristóbal; Marshall, Mark D.; Leung, Helen O.; Schnell, Melanie (October 2020, Chemical Science)

   null (Ed.)

   The capabilities of rotational spectroscopy-based methods as tools to deliver accurate and precise chirality-sensitive information are still breaking ground, but their applicability in the challenging field of analytical chemistry is already clear. In this mini review, we explore the current abilities and challenges of two emergent techniques for chiral analysis based on rotational spectroscopy. For that, we will showcase the two methods (microwave 3-wave mixing and chiral tag rotational spectroscopy) while testing their performance to solve the absolute configuration and the enantiomeric excess of a blind sample containing a mixture of enantiomers of styrene oxide. 

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5. Exploring Class Enumeration in Bayesian Growth Mixture Modeling Based on Conditional Medians

   https://doi.org/10.3389/feduc.2021.624149  

   Kim, Seohyun; Tong, Xin; Ke, Zijun (February 2021, Frontiers in Education)

   null (Ed.)

   Growth mixture modeling is a popular analytic tool for longitudinal data analysis. It detects latent groups based on the shapes of growth trajectories. Traditional growth mixture modeling assumes that outcome variables are normally distributed within each class. When data violate this normality assumption, however, it is well documented that the traditional growth mixture modeling mislead researchers in determining the number of latent classes as well as in estimating parameters. To address nonnormal data in growth mixture modeling, robust methods based on various nonnormal distributions have been developed. As a new robust approach, growth mixture modeling based on conditional medians has been proposed. In this article, we present the results of two simulation studies that evaluate the performance of the median-based growth mixture modeling in identifying the correct number of latent classes when data follow the normality assumption or have outliers. We also compared the performance of the median-based growth mixture modeling to the performance of traditional growth mixture modeling as well as robust growth mixture modeling based on t distributions. For identifying the number of latent classes in growth mixture modeling, the following three Bayesian model comparison criteria were considered: deviance information criterion, Watanabe-Akaike information criterion, and leave-one-out cross validation. For the median-based growth mixture modeling and t -based growth mixture modeling, our results showed that they maintained quite high model selection accuracy across all conditions in this study (ranged from 87 to 100%). In the traditional growth mixture modeling, however, the model selection accuracy was greatly influenced by the proportion of outliers. When sample size was 500 and the proportion of outliers was 0.05, the correct model was preferred in about 90% of the replications, but the percentage dropped to about 40% as the proportion of outliers increased to 0.15. 

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