ACS Central Science最新文献

[This corrects the article DOI: 10.1021/acscentsci.4c01822.].

Reactions of Cl^–, Br^–, and I^– ions in seawater with incoming molecules from the gas phase are of major atmospheric importance, but their mechanisms are mostly unknown. In this study, using ab initio molecular dynamics (AIMD) simulations, the microscopic mechanism of the halogen exchange reaction in water, HOCl + I^– → HOI + Cl^–, is unraveled. The main findings are as follows: (1) The reaction proceeds through a halogen-bonded isomer of the complex of HOCl with I^–, which is present in water and has a significant lifetime. The hydrogen-bonded isomer of the complex seems to play no role in the reaction. (2) Several water molecules act to catalyze the reaction through a Grotthuss-like mechanism that is totally different from that of halogen exchange in the gas phase. These results may have important implications for the chemistry of seawater, in particular for other reactions involving halogenated species.

Halogen exchange between iodide ions and hypochlorous acid molecules in seawater is catalyzed by the medium through the formation and participation of hydroxyl ions.

Triplet-triplet annihilation photon upconversion (TTA-UC) systems hold great promise for applications in energy, 3D printing, and photopharmacology. However, their optimization remains challenging due to the need for precise tuning of sensitizer and annihilator concentrations under oxygen-free conditions. This study presents an automated, high-throughput platform for the discovery and optimization of TTA-UC systems. Capable of performing 100 concentration scans in just two hours, the platform generates comprehensive concentration maps of critical parameters, including quantum yield, triplet energy transfer efficiency, and threshold intensity. Using this approach, we identify key loss mechanisms in both the established and novel TTA-UC systems. At high porphyrin-based sensitizer concentrations, upconversion quantum yield losses are attributed to sensitizer triplet self-quenching via aggregation and sensitizer triplet-triplet annihilation (sensitizer-TTA). Additionally, reverse triplet energy transfer (RTET) at elevated sensitizer levels increases the upconversion losses and excitation thresholds. Testing novel sensitizer-annihilator pairs confirms these loss mechanisms, highlighting opportunities for molecular design improvements. This automated platform offers a powerful tool for advancing TTA-UC research and other photochemical studies requiring low oxygen levels, intense laser excitation, and minimal material use.

Triplet–triplet annihilation photon upconversion (TTA-UC) systems hold great promise for applications in energy, 3D printing, and photopharmacology. However, their optimization remains challenging due to the need for precise tuning of sensitizer and annihilator concentrations under oxygen-free conditions. This study presents an automated, high-throughput platform for the discovery and optimization of TTA-UC systems. Capable of performing 100 concentration scans in just two hours, the platform generates comprehensive concentration maps of critical parameters, including quantum yield, triplet energy transfer efficiency, and threshold intensity. Using this approach, we identify key loss mechanisms in both the established and novel TTA-UC systems. At high porphyrin-based sensitizer concentrations, upconversion quantum yield losses are attributed to sensitizer triplet self-quenching via aggregation and sensitizer triplet–triplet annihilation (sensitizer-TTA). Additionally, reverse triplet energy transfer (RTET) at elevated sensitizer levels increases the upconversion losses and excitation thresholds. Testing novel sensitizer–annihilator pairs confirms these loss mechanisms, highlighting opportunities for molecular design improvements. This automated platform offers a powerful tool for advancing TTA-UC research and other photochemical studies requiring low oxygen levels, intense laser excitation, and minimal material use.

A high-throughput concentration screening method built from commercial components for determining upconversion quantum yield and excitation threshold in sensitized triplet−triplet annihilation systems.

The relationship between macroscopic stress relaxation and molecular-level chain exchange in triblock copolymer micelles has been explored using rheology and time-resolved small-angle neutron scattering (TR-SANS), marking the first measurements of chain exchange in concentrated triblock networks. It has long been assumed in models of transient or thermoreversible networks that the time scales for these two processes are equal. Experimentally, we find that stress relaxation occurs many orders-of-magnitude faster than chain exchange. This difference is quantitatively explained by modest dispersity in the core block that results in a slight asymmetry within any given nominally symmetric triblock. For stress relaxation to occur, only the shorter chain must pull out, while chain exchange is slowed due to the requirement of the eventual pullout of the longer block. The pullout time is extremely sensitive to the length of the core block. This mechanism is supported by measurements with an intentionally asymmetric triblock copolymer, which displays an even larger difference between the stress relaxation and chain exchange rates. These results establish a quantitative molecular-level picture of the chain dynamics associated with stress relaxation in triblock copolymer networks.

A quantitative relationship is established between the flow time of a BAB triblock copolymer network, from rheology, and the time for a single B end block to escape from its micellar cross-link, determined by neutron scattering.

The relationship between macroscopic stress relaxation and molecular-level chain exchange in triblock copolymer micelles has been explored using rheology and time-resolved small-angle neutron scattering (TR-SANS), marking the first measurements of chain exchange in concentrated triblock networks. It has long been assumed in models of transient or thermoreversible networks that the time scales for these two processes are equal. Experimentally, we find that stress relaxation occurs many orders-of-magnitude faster than chain exchange. This difference is quantitatively explained by modest dispersity in the core block that results in a slight asymmetry within any given nominally symmetric triblock. For stress relaxation to occur, only the shorter chain must pull out, while chain exchange is slowed due to the requirement of the eventual pullout of the longer block. The pullout time is extremely sensitive to the length of the core block. This mechanism is supported by measurements with an intentionally asymmetric triblock copolymer, which displays an even larger difference between the stress relaxation and chain exchange rates. These results establish a quantitative molecular-level picture of the chain dynamics associated with stress relaxation in triblock copolymer networks.

A recyclable solid-state electrolyte enabled by a novel aluminum fluoride framework enhances aluminum-ion battery longevity, safety, and cost-efficiency.

C-H functionalization of complex substrates is highly enabling in total synthesis and in the development of late-stage drug candidates. Much work has been dedicated to developing new methods as well as predictive modeling to accelerate route scouting. However, workflows to identify regioisomeric products are arduous, typically requiring chromatographic separation and/or nuclear magnetic resonance spectroscopy analysis. In addition, most reports focus on major products or do not assign regioisomeric products, which biases predictive models constructed from such data. Herein, we present a novel approach to complex reaction analysis utilizing partial deuterium labels, which enables direct product identification via liquid chromatography-mass spectrometry. When combined with spectral deconvolution, the method generates product ratios while circumventing chromatography altogether. Competitive kinetic isotope effects can also be determined. The resultant data are expected to be useful in the construction of predictive models across several dimensions including reaction selectivity, the impact of structure on mechanism, and mass spectral ionization patterns and expedite the identification of drug metabolites.