Effect of cinnamon powder on quality attributes and off-flavor in fried chicken drumsticks made from long-term thawed Korean native chicken.

The effect of cinnamon powder on the quality and mitigation of off-flavor in fried chicken drumsticks made from long-term thawed Korean native chicken (Woorimatdag No. 1, WRMD1) was investigated. The WRMD1 drumsticks were categorized into 5 groups: conventional thawing (16 h, CT), long-term thawing (48 h, LT), cinnamon powder added into 'LT' as marinade (0.03%, CM) or incorporated into the batter (1.35%, CB), and long-term thawing with cinnamon powder incorporated both in the marinade and batter (0.03% + 1.35%, CMB). The crude fat content was significantly higher in the CT and CMB than that of the CB. The CM, CB, and CMB showed significantly lower levels of 2-thiobarbituric acid reactive substance compared with the CT and LT. The predominant fatty acids in all treatments were C18:1n9, C18:2n6, and C16:0. The LT displayed lower total unsaturated fatty acid content than the CT (P < 0.05). The CM effectively decreased lipid oxidative volatiles, such as 1-octanol, 1-octen-3-ol, and 2-octen-1-ol, (E), in the LT (P < 0.05). Both the CM and CB showed an inclination to increase specific pyrazines associated with pleasant notes compared with the LT, and showed higher levels of pyrazines, such as pyrazine, 2-ethyl-6-methyl-, and pyrazine, 3-ethyl-2,5-dimethyl-, than those of the CMB (P < 0.05). The CM contained higher levels of 2,3-butanedione when compared with the other groups (P < 0.05). Multivariate analysis demonstrated that cinnamon had an effect in discriminating the treatment groups with cinnamon addition from both the CT and LT, whereas the CM, CB, and CMB formed distinct clusters. The CM and CMB received significantly higher aroma scores from panelists in comparison to the other groups. These findings suggest that the CM (0.03% cinnamon powder) can be used to enhance the aroma in fried WRMD1 drumsticks by reducing or masking the off-flavor volatiles associated with long-term thawing.

Precise atom-to-atom mapping for organic reactions via human-in-the-loop machine learning.

Atom-to-atom mapping (AAM) is a task of identifying the position of each atom in the molecules before and after a chemical reaction, which is important for understanding the reaction mechanism. As more machine learning (ML) models were developed for retrosynthesis and reaction outcome prediction recently, the quality of these models is highly dependent on the quality of the AAM in reaction datasets. Although there are algorithms using graph theory or unsupervised learning to label the AAM for reaction datasets, existing methods map the atoms based on substructure alignments instead of chemistry knowledge. Here, we present LocalMapper, an ML model that learns correct AAM from chemist-labeled reactions via human-in-the-loop machine learning. We show that LocalMapper can predict the AAM for 50 K reactions with 98.5% calibrated accuracy by learning from only 2% of the human-labeled reactions from the entire dataset. More importantly, the confident predictions given by LocalMapper, which cover 97% of 50 K reactions, show 100% accuracy for 3,000 randomly sampled reactions. In an out-of-distribution experiment, LocalMapper shows favorable performance over other existing methods. We expect LocalMapper can be used to generate more precise reaction AAM and improve the quality of future ML-based reaction prediction models.

Identifying the active sites and intermediates on copper surfaces for electrochemical nitrate reduction to ammonia.

Copper (Cu) is a widely used catalyst for the nitrate reduction reaction (NO3RR), but its susceptibility to surface oxidation and complex electrochemical conditions hinders the identification of active sites. Here, we employed electropolished metallic Cu with a predominant (100) surface and compared it to native oxide-covered Cu. The electropolished Cu surface rapidly oxidized after exposure to either air or electrolyte solutions. However, this oxide was reduced below 0.1 V vs. RHE, thus returning to the metallic Cu before NO3RR. It was distinguished from the native oxide on Cu, which remained during NO3RR. Fast NO3- and NO reduction on the metallic Cu delivered 91.5 ± 3.7% faradaic efficiency for NH3 at -0.4 V vs. RHE. In contrast, the native oxide on Cu formed undesired products and low NH3 yield. Operando shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) analysis revealed the adsorbed NO3-, NO2, and NO species on the electropolished Cu as the intermediates of NH3. Low overpotential NO3- and NO adsorptions and favorable NO reduction are key to increased NH3 productivity over Cu samples, which was consistent with the DFT calculation on Cu(100).

Variational quantum eigensolver for closed-shell molecules with non-bosonic corrections.

The realization of quantum advantage with noisy-intermediate-scale quantum (NISQ) machines has become one of the major challenges in computational sciences. Maintaining coherence of a physical system with more than ten qubits is a critical challenge that motivates research on compact system representations to reduce algorithm complexity. Toward this end, the variational quantum eigensolver (VQE) used to perform quantum simulations is considered to be one of the most promising algorithms for quantum chemistry in the NISQ era. We investigate reduced mapping of one spatial orbital to a single qubit to analyze the ground state energy in a way that the Pauli operators of qubits are mapped to the creation/annihilation of singlet pairs of electrons. To include the effect of non-bosonic (or non-paired) excitations, we introduce a simple correction scheme in the electron correlation model approximated by the geometrical mean of the bosonic (or paired) terms. Employing it in a VQE algorithm, we assess ground state energies of H2O, N2, and Li2O in good agreement with full configuration interaction (FCI) models respectively, using only 6, 8, and 12 qubits with quantum gate depths proportional to the squares of the qubit counts. With the adopted seniority-zero approximation that uses only one half of the qubit counts of a conventional VQE algorithm, we find that our non-bosonic correction method reaches reliable quantum chemistry simulations at least for the tested systems.

Physicochemical Features and Volatile Organic Compounds of Horse Loin Subjected to Sous-Vide Cooking.

The purpose of this study was to evaluate the effect of temperature and time of sous-vide cooking method on the characteristics of Thoroughbred horse loin. Sliced portions (200 ± 50 g) were cooked by boiling (control) and sous-vide (65 and 70 °C for 12, 18, and 24 h). The samples were analyzed for proximate composition, pH, color, texture, microstructure, sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), microbiology, volatile organic compounds (VOCs), nucleotide content, and fatty acids composition. The color analysis showed decreased redness at elevated temperatures. Improved tenderness, demonstrated by reduced shear force values (36.36 N at 65 °C for 24 h and 35.70 N at 70 °C for 24 h). The micrographs indicated dense fiber arrangements at 70 °C. The SDS-PAGE revealed muscle protein degradation with extended sous-vide cooking. The VOC analysis identified specific compounds, potentially distinctive markers for sous-vide cooking of horse meat including 1-octen-3-ol, decanal, n-caproic acid vinyl ester, cyclotetrasiloxane, octamethyl, and 3,3-dimethyl-1,2-epoxybutane. This study highlights the cooking time's primary role in sous vide-cooked horse meat tenderness and proposes specific VOCs as potential markers. Further research should explore the exclusivity of these VOCs to sous-vide cooking.

Predicting synthesis recipes of inorganic crystal materials using elementwise template formulation.

While advances in computational techniques have accelerated virtual materials design, the actual synthesis of predicted candidate materials is still an expensive and slow process. While a few initial studies attempted to predict the synthesis routes for inorganic crystals, the existing models do not yield the priority of predictions and could produce thermodynamically unrealistic precursor chemicals. Here, we propose an element-wise graph neural network to predict inorganic synthesis recipes. The trained model outperforms the popularity-based statistical baseline model for the top-k exact match accuracy test, showing the validity of our approach for inorganic solid-state synthesis. We further validate our model by the publication-year-split test, where the model trained based on the materials data until the year 2016 is shown to successfully predict synthetic precursors for the materials synthesized after 2016. The high correlation between the probability score and prediction accuracy suggests that the probability score can be interpreted as a measure of confidence levels, which can offer the priority of the predictions.

Accelerated chemical science with AI.

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of 'Accelerated Chemical Science with AI' at Gangneung, Republic of Korea. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: 'Data', 'New applications', 'Machine learning algorithms', and 'Education'. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions.

Identification and comparison of aroma and taste-related compounds from breast meat of three breeds of Korean native chickens.

This study was aimed to identify and compare the taste-related compounds (nucleotide-related compounds, free amino acid contents, and fatty acid composition) and aroma (volatile organic compounds [VOC]) compounds in the chicken breast meat from 3 kinds of Korean native chicken (KNC), namely Hanhyup 3 (HH3), Woorimatdag 1 (WRMD1) and Woorimatdag 2 (WRMD2). Among the 3 breeds, WRMD1 had significantly higher IMP and AMP contents than HH3. WRMD2 exhibited higher levels of umami and sweet-taste amino acids and oleic acid composition compared to HH3 (P < 0.05). HH3 showed a higher composition of unsaturated fatty acids than WRMD2 (P < 0.05). On their discrimination by flavor composition, some compounds including aspartic acid were analyzed as important compounds. Regarding aroma compounds, unique aroma compounds were detected for each breed and some compounds such as isopropyl myristate, p-cresol, (S)-(+)-3-Methyl-1-pentanol, and cyclic octa-atomic sulfur were expected to be utilized as key compounds in discrimination of the 3 breeds. From the result of this study, the differences on the flavor compounds of three breeds were elucidated and key compounds for their discrimination were presented.

Physicochemical Properties and Volatile Organic Compounds of Dairy Beef Round Subjected to Various Cooking Methods.

To evaluate the effect of different cooking methods on the physicochemical quality and volatile organic compounds (VOC) of dairy beef round, twelve beef round pieces were divided into four groups: raw, boiling, microwave, and sous-vide. The sous-vide group had a higher pH than the boiling or microwave groups. The boiling group exhibited the highest shear force and CIE L*, followed by the microwave and sous-vide groups (p<0.05). The sous-vide group received higher taste and tenderness scores from panelists (p<0.05) and showed significantly higher levels of aspartic and glutamic acids than the other groups. The sous-vide and microwave groups had the highest oleic acid and polyunsaturated fatty acid levels, respectively. The sous-vide group had significantly higher hypoxanthine and inosine levels than the other groups. However, the microwave group had higher inosine monophosphate levels than the other groups. The sous-vide group had a higher alcohol content, including 1-octen-3-ol, than the other groups. Octanal and nonanal were the most abundant aldehydes in all groups. (R)-(-)-14-methyl-8-hexadecyn-1-ol, p-cresol, and 1-tridecyne were used to distinguish the VOC for each group in the multivariate analysis. Sous-vide could be effective in increasing meat tenderness as well as taste-related free amino acid (aspartic acid and glutamic acid) and fatty acid (oleic acid) levels. Furthermore, specific VOC, including 1-octen-3-ol, 2-ethylhexanal ethylene glycol acetal, and 2-octen-1-ol, (E)-, could be potential markers for distinguishing sous-vide from other cooking methods. Further studies are required to understand the mechanisms underlying the predominant association of these VOC with the sous-vide cooking method.

Exploring the Diverse Landscape of Biaryl-Containing Peptides Generated by Cytochrome P450 Macrocyclases.

Cytochrome P450 enzymes (P450s) catalyze diverse oxidative cross-coupling reactions between aromatic substrates in the natural product biosynthesis. Specifically, P450s install distinct biaryl macrocyclic linkages in three families of ribosomally synthesized and post-translationally modified peptides (RiPPs). However, the chemical diversity of biaryl-containing macrocyclic RiPPs remains largely unexplored. Here, we demonstrate that P450s have the capability to generate diverse biaryl linkages on RiPPs, collectively named "cyptides". Homology-based genome mining for P450 macrocyclases revealed 19 novel groups of homologous biosynthetic gene clusters (BGCs) with distinct aromatic residue patterns in the precursor peptides. Using the P450-modified precursor peptides heterologously coexpressed with corresponding P450s in Escherichia coli, we determined the NMR structures of three novel biaryl-containing peptides─the enzymatic products, roseovertin (1), rubrin (2), and lapparbin (3)─and confirmed the formation of three unprecedented or rare biaryl linkages: Trp C-7'-to-His N-τ in 1, Trp C-7'-to-Tyr C-6 in 2, and Tyr C-6-to-Trp N-1' in 3. Biochemical characterization indicated that certain P450s in these pathways have a relaxed substrate specificity. Overall, our studies suggest that P450 macrocyclases have evolved to create diverse biaryl linkages in RiPPs, promoting the exploration of a broader chemical space for biaryl-containing peptides encoded in bacterial genomes.

Single-Atom Molybdenum-N3 Sites for Selective Hydrogenation of CO2 to CO.

The design of efficient non-noble metal catalysts for CO2 hydrogenation to fuels and chemicals is desired yet remains a challenge. Herein, we report that single Mo atoms with a MoN3 (pyrrolic) moiety enable remarkable CO2 adsorption and hydrogenation to CO, as predicted by density functional theory studies and evidenced by a high and stable conversion of CO2 reaching about 30.4 % with a CO selectivity of almost 100 % at 500 °C and very low H2 partial pressure. Atomically dispersed MoN3 is calculated to facilitate CO2 activation and reduces CO2 to CO* via the direct dissociation path. Furthermore, the highest transition state energy in CO formation is 0.82 eV, which is substantially lower than that of CH4 formation (2.16 eV) and accounts for the dominant yield of CO. The enhanced catalytic performances of Mo/NC originate from facile CO desorption with the help of dispersed Mo on nitrogen-doped carbon (Mo/NC), and in the absence of Mo nanoparticles. The resulting catalyst preserves good stability without degradation of CO2 conversion rate even after 68 hours of continuous reaction. This finding provides a promising route for the construction of highly active, selective, and robust single-atom non-precious metal catalysts for reverse water-gas shift reaction.

Correlated Local Fluctuations in the Hydrogen Bond Network of Liquid Water.

The hypothesis that liquid water can separate into two phases in the supercooled state has been supported by recent experimental and theoretical studies. However, whether such structural inhomogeneity extends to ambient conditions is under intense debate. Due to the dynamic nature of the hydrogen bond network of liquid water, exploring its structure requires detailed insight into the collective motion of neighboring water molecules, a missing link that has not been examined so far. Here, highly sensitive quantum mechanical calculations detect that the time evolution of nearby hydrogen bonds is strongly correlated, revealing a direct mechanism for the appearance of short-range structural fluctuations in the hydrogen bond network of liquid water for the first time. This correlated dynamics is found to be closely connected to the static structural picture. The distortions from the tetrahedral structure do not occur independently but are correlated due to the preference of nearby donors and acceptors to be in similar environments. The existence of such cooperative fluctuations is further supported by the temperature dependence of the local structural evolution and explained by conventional analysis of localized orbitals. It was found that such correlated structural fluctuations are only observed on a short length scale in simulations at ambient conditions. The correlations of the nearby hydrogen bond pairs of liquid water unveiled here are expected to offer a new insight into connecting the dynamics of individual water molecules and the local structure of the hydrogen bond network.

Atomic-Scale Homogeneous RuCu Alloy Nanoparticles for Highly Efficient Electrocatalytic Nitrogen Reduction.

Ruthenium (Ru) is the most widely used metal as an electrocatalyst for nitrogen (N2 ) reduction reaction (NRR) because of the relatively high N2 adsorption strength for successive reaction. Recently, it has been well reported that the homogeneous Ru-based metal alloys such as RuRh, RuPt, and RuCo significantly enhance the selectivity and formation rate of ammonia (NH3 ). However, the metal combinations for NRR have been limited to several miscible combinations of metals with Ru, although various immiscible combinations have immense potential to show high NRR performance. In this study, an immiscible combination of Ru and copper (Cu) is first utilized, and homogeneous alloy nanoparticles (RuCu NPs) are fabricated by the carbothermal shock method. The RuCu homogeneous NP alloys on cellulose/carbon nanotube sponge exhibit the highest selectivity and NH3 formation rate of ≈31% and -73 µmol h-1 cm-2 , respectively. These are the highest values of the selectivity and NH3 formation rates among existing Ru-based alloy metal combinations.

Automated exploitation of the big configuration space of large adsorbates on transition metals reveals chemistry feasibility.

Mechanistic understanding of large molecule conversion and the discovery of suitable heterogeneous catalysts have been lagging due to the combinatorial inventory of intermediates and the inability of humans to enumerate all structures. Here, we introduce an automated framework to predict stable configurations on transition metal surfaces and demonstrate its validity for adsorbates with up to 6 carbon and oxygen atoms on 11 metals, enabling the exploration of ~108 potential configurations. It combines a graph enumeration platform, force field, multi-fidelity DFT calculations, and first-principles trained machine learning. Clusters in the data reveal groups of catalysts stabilizing different structures and expose selective catalysts for showcase transformations, such as the ethylene epoxidation on Ag and Cu and the lack of C-C scission chemistry on Au. Deviations from the commonly assumed atom valency rule of small adsorbates are also manifested. This library can be leveraged to identify catalysts for converting large molecules computationally.

Correction to "Generative Adversarial Networks for Crystal Structure Prediction".

[This corrects the article DOI: 10.1021/acscentsci.0c00426.].

Engineering vacancy and hydrophobicity of two-dimensional TaTe2 for efficient and stable electrocatalytic N2 reduction.

Demand for ammonia continues to increase to sustain the growing global population. The direct electrochemical N2 reduction reaction (NRR) powered by renewable electricity offers a promising carbon-neutral and sustainable strategy for manufacturing NH3, yet achieving this remains a grand challenge. Here, we report a synergistic strategy to promote ambient NRR for ammonia production by tuning the Te vacancies (VTe) and surface hydrophobicity of two-dimensional TaTe2 nanosheets. Remarkable NH3 faradic efficiency of up to 32.2% is attained at a mild overpotential, which is largely maintained even after 100 h of consecutive electrolysis. Isotopic labeling validates that the N atoms of formed NH4 + originate from N2. In situ X-ray diffraction indicates preservation of the crystalline structure of TaTe2 during NRR. Further density functional theory calculations reveal that the potential-determining step (PDS) is ∗NH2 + (H+ + e-) → NH3 on VTe-TaTe2 compared with that of ∗ + N2 + (H+ + e-) → ∗N-NH on TaTe2. We identify that the edge plane of TaTe2 and VTe serve as the main active sites for NRR. The free energy change at PDS on VTe-TaTe2 is comparable with the values at the top of the NRR volcano plots on various transition metal surfaces.

Bimetallic Gold-Silver Nanostructures Drive Low Overpotentials for Electrochemical Carbon Dioxide Reduction.

Alloy formation is an advanced approach to improve desired properties that the monoelements cannot achieve. Alloys are usually designed to tailor intrinsic natures or induce synergistic effects by combining materials with distinct properties. Indeed, unprecedented properties have emerged in many cases, superior to a simple sum of pure elements. Here, we present Au-Ag alloy nanostructures with prominent catalytic properties in an electrochemical carbon dioxide reduction reaction (eCO2RR). The Au-Ag hollow nanocubes are prepared by galvanic replacement of Au on Ag nanocubes. When the Au-to-Ag ratio is 1:1 (Au1Ag1), the alloy hollow nanocubes exhibit maximum Faradaic efficiencies of CO production in a wide potential range and high mass activity and CO current density superior to those of the bare metals. In particular, overpotentials are estimated to be similar to or lower than that of the Au catalyst under various standard metrics. Density functional theory calculations, machine learning, and a statistical consideration demonstrate that the optimal configuration of the *COOH intermediate is a bidentate coordination structure where C binds to Au and O binds to Ag. This active Au-Ag neighboring configuration has a maximum population and enhanced intrinsic catalytic activity on the Au1Ag1 surface among other Au-to-Ag compositions, in good agreement with the experimental results. Further application of Au1Ag1 to a membrane electrode assembly cell at neutral conditions shows enhanced CO Faradaic efficiency and current densities compared to Au or Ag nanocubes, indicating the possible extension of Au-Ag alloys to larger electrochemical systems. These results give a new insight into the synergistic roles of Au and Ag in the eCO2RR and offer a fresh direction toward a rational design of bimetallic catalysts at a practical scale.

Pd3 Pb Nanosponges for Selective Conversion of Furfural to Furfuryl Alcohol under Mild Condition.

Alloy structures with high catalytic surface areas and tunable surface energies can lead to high catalytic selectivity and activities. Herein, the synthesis of sponge-like Pd3 Pb multiframes (Pd3 Pb MFs) is reported by using the thermodynamically driven phase segregation, which exhibit high selectivity (93%) for the conversion of furfural to furfuryl alcohol (FOL) under mild conditions. The excellent catalytic performance of the Pd3 Pb MF catalysts is attributed to the high surface area and optimized surface energy of the catalyst, which is associated with the introduction of Pb to Pd. Density functional theory calculations show that the binding energy of FOL to the surface energy-tuned Pd3 Pb MF is sufficiently lowered to prevent side reactions such as over-hydrogenation of FOL.

Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention.

As a fundamental problem in chemistry, retrosynthesis aims at designing reaction pathways and intermediates for a target compound. The goal of artificial intelligence (AI)-aided retrosynthesis is to automate this process by learning from the previous chemical reactions to make new predictions. Although several models have demonstrated their potentials for automated retrosynthesis, there is still a significant need to further enhance the prediction accuracy to a more practical level. Here we propose a local retrosynthesis framework called LocalRetro, motivated by the chemical intuition that the molecular changes occur mostly locally during the chemical reactions. This differs from nearly all existing retrosynthesis methods that suggest reactants based on the global structures of the molecules, often containing fine details not directly relevant to the reactions. This local concept yields local reaction templates involving the atom and bond edits. Because the remote functional groups can also affect the overall reaction path as a secondary aspect, the proposed locally encoded retrosynthesis model is then further refined to account for the nonlocal effects of chemical reaction through a global attention mechanism. Our model shows a promising 89.5 and 99.2% round-trip accuracy at top-1 and top-5 predictions for the USPTO-50K dataset containing 50 016 reactions. We further demonstrate the validity of LocalRetro on a large dataset containing 479 035 reactions (UTPTO-MIT) with comparable round-trip top-1 and top-5 accuracy of 87.0 and 97.4%, respectively. The practical application of the model is also demonstrated by correctly predicting the synthesis pathways of five drug candidate molecules from various literature.