Search for: All records

Mounting concerns regarding per‐/poly‐fluoroalkyl substances (PFAS) on human health are focusing attention on trace‐level PFAS detection in aqueous environments. Here, we report a readily prepared small molecule, 2,6‐bis(3,5‐diethyl‐1H‐pyrrol‐2‐yl)pyridine (receptor 1), that displays high binding affinities (logKa< = 4.9–6.2) and produces a strong “turn‐on” emission response when exposed to representative PFAS in hexanes. The hydrophobic nature of 1 , and its strong affinity for various PFAS, allowed hexanes solutions of 1 to be used as “turn‐on” emission sensors for dilute aqueous solutions of long‐chain (≥C8) PFAS under acidic conditions (pH 2) by liquid‐phase extraction (LPE). In the case of perfluorooctanoic acid (PFOA), the response was rapid (under 10 min) and sensitive. Limits of detection (LOD) as low as 250 ppt were readily achievable by direct naked‐eye observation. LOD as low as 40 and 100 ppt, respectively, could be reached for deionized and tap water solutions of PFOA using a smartphone color‐scanning application. Little change in the sensitivity was seen in the presence of a range of inorganic and organic species that could act as potential interferants. Support for the present findings came from UV–vis absorbance, fluorescence, 1 

Abstract Herein, aqueous nitrate (NO₃⁻) reduction is used to explore composition‐selectivity relationships of randomly alloyed ruthenium‐palladium nanoparticle catalysts to provide insights into the factors affecting selectivity during this and other industrially relevant catalytic reactions. NO₃⁻reduction proceeds through nitrite (NO₂⁻) and then nitric oxide (NO), before diverging to form either dinitrogen (N₂) or ammonium (NH₄⁺) as final products, with N₂preferred in potable water treatment but NH₄⁺preferred for nitrogen recovery. It is shown that the NO₃⁻and NO starting feedstocks favor NH₄⁺formation using Ru‐rich catalysts, while Pd‐rich catalysts favor N₂formation. Conversely, a NO₂⁻starting feedstock favors NH₄⁺at ≈50 atomic‐% Ru and selectivity decreases with higher Ru content. Mechanistic differences have been probed using density functional theory (DFT). Results show that, for NO₃⁻and NO feedstocks, the thermodynamics of the competing pathways for N–H and N–N formation lead to preferential NH₄⁺ or N₂production, respectively, while Ru‐rich surfaces are susceptible to poisoning by NO₂⁻feedstock, which displaces H atoms. This leads to a decrease in overall reduction activity and an increase in selectivity toward N₂production. Together, these results demonstrate the importance of tailoring both the reaction pathway thermodynamics and initial reactant binding energies to control overall reaction selectivity. 

Machine learning potentials (MLPs) have attracted significant attention in computational chemistry and materials science due to their high accuracy and computational efficiency. The proper selection of atomic structures is crucial for developing reliable MLPs. Insufficient or redundant atomic structures can impede the training process and potentially result in a poor quality MLP. Here, we propose a local-environment-guided screening algorithm for efficient dataset selection in MLP development. The algorithm utilizes a local environment bank to store unique local environments of atoms. The dissimilarity between a particular local environment and those stored in the bank is evaluated using the Euclidean distance. A new structure is selected only if its local environment is significantly different from those already present in the bank. Consequently, the bank is then updated with all the new local environments found in the selected structure. To demonstrate the effectiveness of our algorithm, we applied it to select structures for a Ge system and a Pd13H2 particle system. The algorithm reduced the training data size by around 80% for both without compromising the performance of the MLP models. We verified that the results were independent of the selection and ordering of the initial structures. We also compared the performance of our method with the farthest point sampling algorithm, and the results show that our algorithm is superior in both robustness and computational efficiency. Furthermore, the generated local environment bank can be continuously updated and can potentially serve as a growing database of feature local environments, aiding in efficient dataset maintenance for constructing accurate MLPs. 

Abstract Aqueous zinc‐ion batteries are promising alternatives to lithium‐ion batteries due to their cost‐effectiveness and improved safety. However, several challenges, including corrosion, dendrites, and water decomposition at the Zn anode, hinder their performance. Herein, an approach is proposed, that deviates from the conventional design by adding water into a propylene carbonate‐based organic electrolyte to prepare a non‐flammable “water‐in‐organic” electrolyte. The chaotropic salt Zn(ClO₄)₂exploits the Hofmeister effect to promote the miscibility of immiscible liquid phases. Interactions between propylene carbonate and water restrict water activity and mitigate unfavorable reactions. This electrolyte facilitates preferential Zn (002) deposition and the formation of solid electrolyte interphase. Consequently, the “water‐in‐organic” electrolyte achieves a 99.5% Coulombic efficiency at 1 mA cm⁻²over 1000 cycles in Zn/Cu cells, and constant cycling over 1000 h in Zn/Zn symmetric cells. A Na_0.33V₂O₅/Zn battery exhibits impressive cycling stability with a capacity of 175 mAh g⁻¹for 800 cycles at 2 A g⁻¹. Additionally, this electrolyte enables sustainable cycling across a wide temperature range from −20 to 50 °C. The design of a “water‐in‐organic” electrolyte employing a chaotropic salt presents a potential strategy for high‐performance electrolytes in zinc‐ion batteries with a large stability window and a wide temperature range.