Catalyst Aggregation Matters for Immobilized Molecular CO2RR Electrocatalysts.

Here, we detail how the catalytic behavior of immobilized molecular electrocatalysts for the CO2 reduction reaction (CO2RR) can be impacted by catalyst aggregation. Operando Raman spectroscopy was used to study the CO2RR mediated by a layer of cobalt phthalocyanine (CoPc) immobilized on the cathode of an electrochemical flow reactor. We demonstrate that during electrolysis, the oxidation state of CoPc in the catalyst layer is dependent upon the degree of catalyst aggregation. Our data indicate that immobilized molecular catalysts must be dispersed on conductive supports to mitigate the formation of aggregates and produce meaningful performance data. We leveraged insights from this mechanistic study to engineer an improved CO-forming immobilized molecular catalyst─cobalt octaethoxyphthalocyanine (EtO8-CoPc)─that exhibited high selectivity (FECO ≥ 95%), high partial current density (JCO ≥ 300 mA/cm2), and high durability (ΔFECO < 0.1%/h at 150 mA/cm2) in a flow cell. This work demonstrates how to accurately identify CO2RR active species of molecular catalysts using operando Raman spectroscopy and how to use this information to implement improved molecular electrocatalysts into flow cells. This work also shows that the active site of CoPc during CO2RR catalysis in a flow cell is the metal center.

Machine Learning Analysis of Raman Spectra To Quantify the Organic Constituents in Complex Organic-Mineral Mixtures.

Important decisions in local agricultural policy and practice often hinge on the soil's chemical composition. Raman spectroscopy offers a rapid noninvasive means to quantify the constituents of complex organic systems. But the application of Raman spectroscopy to soils presents a multifaceted challenge due to organic/mineral compositional complexity and spectral interference arising from overwhelming fluorescence. The present work compares methodologies with the capacity to help overcome common obstacles that arise in the analysis of soils. We created conditions representative of these challenges by combining varying proportions of six amino acids commonly found in soils with fluorescent bentonite clay and coarse mineral components. Referring to an extensive data set of Raman spectra, we compare the performance of the convolutional neural network (CNN) and partial least-squares regression (PLSR) multivariate models for amino acid composition. Strategies employing volume-averaged spectral sampling and data preprocessing algorithms improve the predictive power of these models. Our average test R2 for PLSR models exceeds 0.89 and approaches 0.98, depending on the complexity of the matrix, whereas CNN yields an R2 range from 0.91 to 0.97, demonstrating that classic PLSR and CNN perform comparably, except in cases where the signal-to-noise ratio of the organic component is very low, whereupon CNN models outperform. Artificially isolating two of the most prevalent obstacles in evaluating the Raman spectra of soils, we have characterized the effect of each obstacle on the performance of machine learning models in the absence of other complexities. These results highlight important considerations and modeling strategies necessary to improve the Raman analysis of organic compounds in complex mixtures in the presence of mineral spectral components and significant fluorescence.

Electrocatalysts Derived from Copper Complexes Transform CO into C2+ Products Effectively in a Flow Cell.

Electrochemical reactors that electrolytically convert CO2 into higher-value chemicals and fuels often pass a concentrated hydroxide electrolyte across the cathode. This strongly alkaline medium converts the majority of CO2 into unreactive HCO3 - and CO3 2- byproducts rather than into CO2 reduction reaction (CO2RR) products. The electrolysis of CO (instead of CO2 ) does not suffer from this undesirable reaction chemistry because CO does not react with OH- . Moreover, CO can be more readily reduced into products containing two or more carbon atoms (i. e., C2+ products) compared to CO2 . We demonstrate here that an electrocatalyst layer derived from copper phthalocyanine (CuPc) mediates this conversion effectively in a flow cell. This catalyst achieved a 25 % higher selectivity for acetate formation at 200 mA/cm2 than a known state-of-art oxide-derived Cu catalyst tested in the same flow cell. A gas diffusion electrode coated with CuPc electrolyzed CO into C2+ products at high rates of product formation (i. e., current densities ≥200 mA/cm2 ), and at high faradaic efficiencies for C2+ production (FEC2+ ; >70 % at 200 mA/cm2 ). While operando Raman spectroscopy did not reveal evidence of structural changes to the copper molecular complex, X-ray photoelectron spectroscopy suggests that the catalyst undergoes conversion to a metallic copper species during catalysis. Notwithstanding, the ligand environment about the metal still impacts catalysis, which we demonstrated through the study of a homologous CuPc bearing ethoxy substituents. These findings reveal new strategies for using metal complexes for the formation of carbon-neutral chemicals and fuels at industrially relevant conditions.

Deep learning unmasks the ECG signature of Brugada syndrome.

One in 10 cases of sudden cardiac death strikes without warning as the result of an inherited arrhythmic cardiomyopathy, such as Brugada Syndrome (BrS). Normal physiological variations often obscure visible signs of this and related life-threatening channelopathies in conventional electrocardiograms (ECGs). Sodium channel blockers can reveal previously hidden diagnostic ECG features, however, their use carries the risk of life-threatening proarrhythmic side effects. The absence of a nonintrusive test places a grossly underestimated fraction of the population at risk of SCD. Here, we present a machine-learning algorithm that extracts, aligns, and classifies ECG waveforms for the presence of BrS. This protocol, which succeeds without the use of a sodium channel blocker (88.4% accuracy, 0.934 AUC in validation), can aid clinicians in identifying the presence of this potentially life-threatening heart disease.

Correction: Concentrations and properties of ice nucleating substances in exudates from Antarctic sea-ice diatoms.

Correction for 'Concentrations and properties of ice nucleating substances in exudates from Antarctic sea-ice diatoms' by Yu Xi et al., Environ. Sci.: Processes Impacts, 2021, 23, 323-334, DOI: 10.1039/D0EM00398K.

Size Distributions of Gold Nanoparticles in Solution Measured by Single-Particle Mass Photometry.

Specialized applications of nanoparticles often call for particular, well-characterized particle size distributions in solution, but this property can prove difficult to measure. High-throughput methods, such as dynamic light scattering, detect nanoparticles in solution with an efficiency that scales with diameter to the sixth power. This diminishes the accuracy of any determination that must span a range of particle sizes. The accurate classification of broadly distributed systems thus requires very large numbers of measurements. Mass-filtered particle-sensing techniques offer a better dynamic range but are labor-intensive and so have low throughput. Progress in many areas of nanotechnology requires a faster, lower-cost, and more accurate measure of particle size distributions, particularly for diameters smaller than 20 nm. Here, we present a tailored interferometric microscope system, combined with a high-speed image-processing strategy, optimized for real-time particle tracking that determines accurate size distributions in nominal 5, 10, and 15 nm colloidal gold nanoparticle systems by automatically sensing and classifying thousands of single particles sampled from solution at rates as high as 4000 particles per minute. We demonstrate this method by sensing the irreversible binding of gold nanoparticles to poly-d-lysine functionalized coverslips. Variations in the single-particle signal as a function of time and mass, calibrated by TEM, show clear evidence for the presence of diffusion-limited transport that most affects larger particles in solution.

Concentrations and properties of ice nucleating substances in exudates from Antarctic sea-ice diatoms.

The ocean contains ice nucleating substances (INSs), some of which can be emitted to the atmosphere where they can influence the formation and properties of clouds. A possible source of INSs in the ocean is exudates from sea-ice diatoms. Here we examine the concentrations and properties of INSs in supernatant samples from dense sea-ice diatom communities collected from Ross Sea and McMurdo Sound in the Antarctic. The median freezing temperatures of the samples ranged from approximately -17 to -22 °C. Based on our results and a comparison with results reported in the literature, the ice nucleating ability of exudates from sea-ice diatoms is likely not drastically different from the ice nucleating ability of exudates from temperate diatoms. The number of INSs per mass of DOC for the supernatant samples were lower than those reported previously for the sea surface microlayer and bulk sea water collected in the Arctic and Atlantic. The INSs in the supernatant sample collected from Ross Sea were not sensitive to temperatures up to 100 °C, were larger than 300 kDa, and were different from ice shaping and recrystallization inhibiting molecules present in the same sample. Possible candidates for these INSs include polysaccharide containing nanogels. The INSs in the supernatant sample collected from McMurdo Sound were sensitive to temperatures of 80 and 100 °C and were larger than 1000 kDa. Possible candidates for these INSs include protein containing nanogels.

Correction to "Assessing Physicochemical Properties of Drug Molecules via Microsolvation Measurements with Differential Mobility Spectrometry".

[This corrects the article DOI: 10.1021/acscentsci.6b00297.].

Assessing Physicochemical Properties of Drug Molecules via Microsolvation Measurements with Differential Mobility Spectrometry.

The microsolvated state of a molecule, represented by its interactions with only a small number of solvent molecules, can play a key role in determining the observable bulk properties of the molecule. This is especially true in cases where strong local hydrogen bonding exists between the molecule and the solvent. One method that can probe the microsolvated states of charged molecules is differential mobility spectrometry (DMS), which rapidly interrogates an ion's transitions between a solvated and desolvated state in the gas phase (i.e., few solvent molecules present). However, can the results of DMS analyses of a class of molecules reveal information about the bulk physicochemical properties of those species? Our findings presented here show that DMS behaviors correlate strongly with the measured solution phase pKa and pKb values, and cell permeabilities of a set of structurally related drug molecules, even yielding high-resolution discrimination between isomeric forms of these drugs. This is due to DMS's ability to separate species based upon only subtle (yet predictable) changes in structure: the same subtle changes that can influence isomers' different bulk properties. Using 2-methylquinolin-8-ol as the core structure, we demonstrate how DMS shows promise for rapidly and sensitively probing the physicochemical properties of molecules, with particular attention paid to drug candidates at the early stage of drug development. This study serves as a foundation upon which future drug molecules of different structural classes could be examined.

Studying Gas-Phase Interconversion of Tautomers Using Differential Mobility Spectrometry.

In this study, we report on the use of differential mobility spectrometry (DMS) as a tool for studying tautomeric species, allowing a more in-depth interrogation of these elusive isomers using ion/molecule reactions and tandem mass spectrometry. As an example, we revisit a case study in which gas-phase hydrogen-deuterium exchange (HDX)-a probe of ion structure in mass spectrometry-actually altered analyte ion structure by tautomerization. For the N- and O-protonated tautomers of 4-aminobenzoic acid, when separated using DMS and subjected to subsequent HDX with trace levels of D2O, the anticipated difference between the exchange rates of the two tautomers is observed. However, when using higher levels of D2O or a more basic reagent, equivalent and almost complete exchange of all labile protons is observed. This second observation is a result of the interconversion of the N-protonated tautomer to the O-protonated form during HDX. We can monitor this transformation experimentally, with support from detailed molecular dynamics and electronic structure calculations. In fact, calculations suggest the onset of bulk solution phase properties for 4-aminobenzoic acid upon solvation with eight CH3OH molecules. These findings also underscore the need for choosing HDX reagents and conditions judiciously when separating interconvertible isomers using DMS. Graphical Abstract ᅟ.