Incorporating Domain Knowledge and Structure-Based Descriptors for Machine Learning: A Case Study of Pd-Catalyzed Sonogashira Reactions.

Machine learning has revolutionized information processing for large datasets across various fields. However, its limited interpretability poses a significant challenge when applied to chemistry. In this study, we developed a set of simple molecular representations to capture the structural information of ligands in palladium-catalyzed Sonogashira coupling reactions of aryl bromides. Drawing inspiration from human understanding of catalytic cycles, we used a graph neural network to extract structural details of the phosphine ligand, a major contributor to the overall activation energy. We combined these simple molecular representations with an electronic descriptor of aryl bromide as inputs for a fully connected neural network unit. The results allowed us to predict rate constants and gain mechanistic insights into the rate-limiting oxidative addition process using a relatively small dataset. This study highlights the importance of incorporating domain knowledge in machine learning and presents an alternative approach to data analysis.

NiH-catalyzed anti-Markovnikov hydroamidation of unactivated alkenes with 1,4,2-dioxazol-5-ones for the direct synthesis of N-alkyl amides.

The addition of a nitrogen-based functional group to alkenes via a direct catalytic method is an attractive way of synthesizing value-added amides. The regioselective hydroamidation of unactivated alkenes is considered one of the easiest ways to achieve this goal. Herein, we report the NiH-catalyzed anti-Markovnikov intermolecular hydroamidation of unactivated alkenes enabled by using 2,9-dibutylphenathroline (diBuphen) as the ligand. This protocol provides a platform for the direct synthesis of over 90 structurally diverse N-alkyl amides using dioxazolones, which can be easily derived from abundant carboxylic acid feedstocks. This method succeeds for both terminal and internal unactivated alkenes and some natural products. Mechanistic studies including DFT calculations reveal an initial reversible insertion/elimination of the [NiH] to the alkene, followed by the irreversible amidation to furnish the N-alkyl amides. By crossover experiments and deuterium labeling studies, the observed anti-Markovnikov regioselectivities are suggested to be controlled by the sterical environment of the coupling reaction.

The role of ESG performance during times of financial crisis: Evidence from COVID-19 in China.

We examine the role of ESG performance during market-wide financial crisis, triggered in response to the COVID-19 global pandemic. The unique circumstances create an inimitable opportunity to question if investors interpret ESG performance as a signal of future stock performance and/or risk mitigation. Using a novel dataset covering China's CSI300 constituents, we show (i) high-ESG portfolios generally outperform low-ESG portfolios (ii) ESG performance mitigates financial risk during financial crisis and (iii) the role of ESG performance is attenuated in 'normal' times, confirming its incremental importance during crisis. We phrase the results in the context of ESG investment practices.

Optical Sensors and Sensing, 2019: introduction to the joint feature issue.

This joint feature issue of Optics Express and Applied Optics highlights contributions from authors who presented their latest research at the OSA Optical Sensors and Sensing Congress, held in San Jose, California, USA from 25-27 June 2019. The joint feature issue comprises 6 contributed papers, which expand upon their respective conference proceedings. The published papers introduced here cover a range of timely research topics in optics and photonics for active open-path sensing, radiometry, and adaptive optics and fiber devices.

Optical Sensors and Sensing 2019: introduction to the joint feature issue.

This joint feature issue of Optics Express and Applied Optics highlights contributions from authors who presented their latest research at the OSA Optical Sensors and Sensing Congress, held in San Jose, California, USA, from 25-27 June 2019. The joint feature issue comprises six contributed papers, which expand upon their respective conference proceedings. The published papers introduced here cover a range of timely research topics in optics and photonics for active open-path sensing, radiometry, and adaptive optics and fiber devices.

Characterization of urban CO2 column abundance with a portable low resolution spectrometer (PLRS): Comparisons with GOSAT and GEOS-Chem model data.

We presented the characterization of urban CO2 column abundance (XCO2) in Hefei, China using a portable low resolution spectrometer (PLRS). An optimized correction spectrum was introduced in the spectral fitting to improve CO2 retrieval. A pronounced seasonal cycle and diurnal variation were observed with a precision of ~0.12%. The CO2 concentrations in winter are about 5-10ppm higher than those in summer. Most diurnal variations exhibited downward trends. The measurement in the early morning is about 2-5ppm higher than the late afternoon observation. The causes of the seasonal and diurnal trends were systematic analyzed. The coincident CO2 time series were compared with the Greenhouse Gases Observing SATellite (GOSAT) data and the GEOS-Chem global 3-D tropospheric chemistry model data. We found the ground based (g-b) PLRS data are systematically higher than the GOSAT and the GEOS-Chem data. Compared to the GOSAT data, the g-b PLRS data are 0.26ppm (0.07%) higher with a standard deviation of 1.70ppm (0.43%). Compared to the smoothed GEOS-Chem model data, the g-b PLRS data shows a 1.31ppm (0.33%) higher with a standard deviation of 5.30ppm (0.87%). The g-b PLRS generally reproduced the seasonal cycle observed by GOSAT and GEOS-Chem model with correlation coefficients (r) of 0.82 and 0.64, respectively.