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We report an experimental demonstration of anti-parity-time symmetric optical four-wave mixing in thermal rubidium vapor, where the propagation of probe and stokes fields in a double-Λ scheme is governed by a non-Hermitian Hamiltonian. We are particularly interested in studying quantum intensity correlations between the two fields near the exceptional point, taking into account loss and accompanied Langevin noise. Our experimental measurements of classical four-wave mixing gain and the associated two-mode relative-intensity squeezing are in reasonable agreement with the theoretical predictions.more » « lessFree, publicly-accessible full text available January 22, 2025
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Synergistic effects of mixing and strain in high entropy spinel oxides for oxygen evolution reactionmore » « less
Abstract Developing stable and efficient electrocatalysts is vital for boosting oxygen evolution reaction (OER) rates in sustainable hydrogen production. High-entropy oxides (HEOs) consist of five or more metal cations, providing opportunities to tune their catalytic properties toward high OER efficiency. This work combines theoretical and experimental studies to scrutinize the OER activity and stability for spinel-type HEOs. Density functional theory confirms that randomly mixed metal sites show thermodynamic stability, with intermediate adsorption energies displaying wider distributions due to mixing-induced equatorial strain in active metal-oxygen bonds. The rapid sol-flame method is employed to synthesize HEO, comprising five 3d-transition metal cations, which exhibits superior OER activity and durability under alkaline conditions, outperforming lower-entropy oxides, even with partial surface oxidations. The study highlights that the enhanced activity of HEO is primarily attributed to the mixing of multiple elements, leading to strain effects near the active site, as well as surface composition and coverage.
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Abstract Background Protein–protein interaction (PPI) is vital for life processes, disease treatment, and drug discovery. The computational prediction of PPI is relatively inexpensive and efficient when compared to traditional wet-lab experiments. Given a new protein, one may wish to find whether the protein has any PPI relationship with other existing proteins. Current computational PPI prediction methods usually compare the new protein to existing proteins one by one in a pairwise manner. This is time consuming.
Results In this work, we propose a more efficient model, called deep hash learning protein-and-protein interaction (DHL-PPI), to predict all-against-all PPI relationships in a database of proteins. First, DHL-PPI encodes a protein sequence into a binary hash code based on deep features extracted from the protein sequences using deep learning techniques. This encoding scheme enables us to turn the PPI discrimination problem into a much simpler searching problem. The binary hash code for a protein sequence can be regarded as a number. Thus, in the pre-screening stage of DHL-PPI, the string matching problem of comparing a protein sequence against a database with
M proteins can be transformed into a much more simpler problem: to find a number inside a sorted array of lengthM . This pre-screening process narrows down the search to a much smaller set of candidate proteins for further confirmation. As a final step, DHL-PPI uses the Hamming distance to verify the final PPI relationship.Conclusions The experimental results confirmed that DHL-PPI is feasible and effective. Using a dataset with strictly negative PPI examples of four species, DHL-PPI is shown to be superior or competitive when compared to the other state-of-the-art methods in terms of precision, recall or F1 score. Furthermore, in the prediction stage, the proposed DHL-PPI reduced the time complexity from
$$O(M^2)$$ $$O(M\log M)$$ M proteins. With the proposed approach, a protein database can be preprocessed and stored for later search using the proposed encoding scheme. This can provide a more efficient way to cope with the rapidly increasing volume of protein datasets. -
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Abstract Electrochemical two-electron water oxidation reaction (2e-WOR) has drawn significant attention as a promising process to achieve the continuous on-site production of hydrogen peroxide (H2O2). However, compared to the cathodic H2O2generation, the anodic 2e-WOR is more challenging to establish catalysts due to the severe oxidizing environment. In this study, we combine density functional theory (DFT) calculations with experiments to discover a stable and efficient perovskite catalyst for the anodic 2e-WOR. Our theoretical screening efforts identify LaAlO3perovskite as a stable, active, and selective candidate for catalyzing 2e-WOR. Our experimental results verify that LaAlO3achieves an overpotential of 510 mV at 10 mA cm−2in 4 M K2CO3/KHCO3, lower than those of many reported metal oxide catalysts. In addition, LaAlO3maintains a stable H2O2Faradaic efficiency with only a 3% decrease after 3 h at 2.7 V vs. RHE. This computation-experiment synergistic approach introduces another effective direction to discover promising catalysts for the harsh anodic 2e-WOR towards H2O2.
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Electrochemical oxidation of water and electrolyte ions is a sustainable method for producing energy carriers and valuable chemicals. Among known materials for catalyzing oxidation reactions, titanium dioxide (TiO 2 ) offers excellent electrochemical stability but is less active than many other metal oxides. Herein, we used density functional theory calculations to predict an increase in catalytic activity by doping anatase TiO 2 with manganese atoms (Mn). We synthesized Mn-doped TiO 2 and then utilized X-ray absorption spectroscopy to study the chemical environment around the Mn site in the TiO 2 crystal structure. Our electrochemical experiments confirmed that TiO 2 , with the optimal amount of Mn, reduces the onset potential by 260 mV in a 2 M KHCO 3 (pH = ∼8) electrolyte and 370 mV in a 0.5 M H 2 SO 4 (pH = ∼0.5) electrolyte. Moreover, in 0.5 M H 2 SO 4 , we observed that the amount of Mn doping greatly impacts the selectivity towards oxygen production versus peroxysulfate formation. In 2 M KHCO 3 , the Mn doping of TiO 2 slightly decreases the selectivity towards oxygen production and increases the hydrogen peroxide formation. The Mn-doped TiO 2 shows good electrochemical stability for over 24 hours in both electrolytes.more » « less
