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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 

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. 

Sn clusters have been grown on highly oriented pyrolytic graphite (HOPG) surfaces and investigated by scanning tunneling microscopy (STM), X-ray photoelectron spectroscopy (XPS), and density functional theory (DFT) calculations. At low Sn coverages ranging from 0.02-0.25 ML, Sn grows as small clusters that nucleate uniformly on the terraces. This behavior is in contrast with the growth of transition metals such as Pd, Pt, and Re on HOPG, given that these metals form large clusters with preferential nucleation for Pd and Pt at the favored low-coordination step edges. XPS experiments show no evidence of Sn-HOPG interactions, and the activation energy barrier for diffusion calculated for Sn on HOPG (0.06 eV) is lower or comparable to those of Pd, Pt and Re (0.04, 0.22, and 0.61 eV, respectively), indicating that the growth of the Sn clusters is not kinetically limited by diffusion on the surface. DFT calculations of the binding energy/atom as a function of cluster size demonstrate that the energies of the Sn clusters on HOPG are similar to that of Sn atoms in the bulk for Sn clusters larger than 10 atoms, whereas the Pt, Pd, and Re clusters on HOPG have energies that are 1-2 eV higher than in the bulk. Thus, there is no thermodynamic driving force for Sn atoms to form clusters larger than 10 atoms on HOPG, unlike for Pd, Pt, and Re atoms, which minimize their energy by aggregating into larger, more bulk-like clusters. In addition, annealing the Sn/HOPG clusters to 800 K and 950 K does not increase the cluster size but instead removes the larger clusters, while Sn deposition at 810 K induces the appearance of protrusions that are believed to be from subsurface Sn. DFT studies indicate that it is energetically favorable for a Sn atom to exist in the subsurface layer only when the Sn atom is located at a subsurface vacancy. 

The impact ofin situelectrooxidation in a Ni₃N electrocatalyst on its hydrogen evolution activity and electrochemical stability was investigated in alkaline media.