Label-Free Data Mining of Scientific Literature by Unsupervised Syntactic Distance Analysis.

Label-free data mining can efficiently feed large amounts of data from the vast scientific literature into artificial intelligence (AI) processing systems. Here, we demonstrate an unsupervised syntactic distance analysis (SDA) approach that is capable of mining chemical substances, functions, properties, and operations without annotation. This SDA approach was evaluated in several areas of research from the physical sciences and achieved performance in information mining comparable to that of supervised learning, as shown by its satisfactory scores of 0.62-0.72, 0.60-0.82, and 0.86-0.95 in precision, recall, and accuracy, respectively. We also showcase how our approach can assist robotic chemists programmed to perform research focused on double-perovskite colloidal nanocrystals, gold colloidal nanocrystals, oxygen evolution reaction catalysts, and enzyme-like catalysts by designing materials, formulations, and synthesis parameters based on data mined from 1.1 million literature references.

Spectroscopy-Guided Deep Learning Predicts Solid-Liquid Surface Adsorbate Properties in Unseen Solvents.

Accurately and rapidly acquiring the microscopic properties of a material is crucial for catalysis and electrochemistry. Characterization tools, such as spectroscopy, can be a valuable tool to infer these properties, and when combined with machine learning tools, they can theoretically achieve fast and accurate prediction results. However, on the path to practical applications, training a reliable machine learning model is faced with the challenge of uneven data distribution in a vast array of non-negligible solvent types. Herein, we employ a combination of the first-principles-based approach and data-driven model. Specifically, we utilize density functional theory (DFT) to calculate theoretical spectral data of CO-Ag adsorption in 23 different solvent systems as a data source. Subsequently, we propose a hierarchical knowledge extraction multiexpert neural network (HMNN) to bridge the knowledge gaps among different solvent systems. HMNN undergoes two training tiers: in tier I, it learns fundamental quantitative spectra-property relationships (QSPRs), and in tier II, it inherits the fundamental QSPR knowledge from previous steps through a dynamic integration of expert modules and subsequently captures the solvent differences. The results demonstrate HMNN's superiority in estimating a range of molecular adsorbate properties, with an error range of less than 0.008 eV for zero-shot predictions on unseen solvents. The findings underscore the usability, reliability, and convenience of HMNN and could pave the way for real-time access to microscopic properties by exploiting QSPR.

Machine Learning Descriptors for Data-Driven Catalysis Study.

Traditional trial-and-error experiments and theoretical simulations have difficulty optimizing catalytic processes and developing new, better-performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful learning and predictive abilities. The selection of appropriate input features (descriptors) plays a decisive role in improving the predictive accuracy of ML models and uncovering the key factors that influence catalytic activity and selectivity. This review introduces tactics for the utilization and extraction of catalytic descriptors in ML-assisted experimental and theoretical research. In addition to the effectiveness and advantages of various descriptors, their limitations are also discussed. Highlighted are both 1) newly developed spectral descriptors for catalytic performance prediction and 2) a novel research paradigm combining computational and experimental ML models through suitable intermediate descriptors. Current challenges and future perspectives on the application of descriptors and ML techniques to catalysis are also presented.

Catalytic Descriptor Exploration for Ru-Based Fischer-Tropsch Catalysts: Effect of Chlorine and Sulfur Addition.

As an important factor in the design of catalysts, catalytic descriptor exploration has emerged as a novel frontier in heterogeneous catalysis. Here, the underlying structure-activity relationships of Ru-based catalysts are theoretically studied to shed light on this area. Calculations of different competing reaction paths suggest that the HCO*-mediated path─because of two synergistic active sites─is more favorable than others. In addition, compared to unadulterated Ru catalysts, the presence of Cl enhances the hydrocarbon production, whereas the presence of S decreases it. After a systematic examination of a series of structure-activity relationships (42 in total), we found that both charge transfer and average charge difference of active Ru atoms are good descriptors for the binding stability of reactants. However, for reactivity the Gibbs free energy of the reactants performs better. More interestingly, due to the quite different catalytic processes of the dissociation and hydrogenation steps, their correlations have opposite slopes.

Blocking mitochondrial Zn2+ accumulation after ischemia reduces mitochondrial dysfunction and neuronal injury.

Zn2+ is an important contributor to ischemic brain injury and recent studies support the hypothesis that mitochondria are key sites of its injurious effects. In murine hippocampal slices (both sexes) subjected to oxygen glucose deprivation (OGD), we found that Zn2+ accumulation and its entry into mitochondria precedes and contributes to the induction of acute neuronal death. In addition, if the ischemic episode is short (and sublethal), there is ongoing Zn2+ accumulation in CA1 mitochondria after OGD that may contribute to their delayed dysfunction. Using this slice model of sublethal OGD, we have now examined Zn2+ contributions to the progression of changes evoked by OGD and occurring over 4-5 hours. We detected progressive mitochondrial depolarization occurring from ∼ 2 hours after ischemia, a large increase in spontaneous synaptic activity between 2-3 hours, and mitochondrial swelling and fragmentation at 4 hours. Blockade of the primary route for Zn2+ entry, the mitochondrial Ca2+ uniporter (MCU; with ruthenium red, RR) or Zn2+ chelation shortly after OGD withdrawal substantially attenuated the mitochondrial depolarization and the changes in synaptic activity. RR also largely reversed the mitochondrial swelling. Finally, using an in vivo rat (male) asphyxial cardiac arrest (CA) model of transient global ischemia, we found that ∼8 min asphyxia induces considerable injury of CA1 neurons 4 hours later that is associated with strong Zn2+ accumulation within many damaged mitochondria. These effects were substantially attenuated by infusion of RR upon reperfusion. Our findings highlight mitochondrial Zn2+ accumulation after ischemia as a possible target for neuroprotective therapy.SIGNIFICANCE STATEMENT:Brain ischemia is a leading cause of mortality and long-term disability that still lacks effective treatment. After transient ischemia delayed death of neurons occurs in vulnerable brain regions. There is a critical need to understand mechanisms of this delayed neurodegeneration which can be targeted for neuroprotection. We found progressive and long-lasting mitochondrial Zn2+ accumulation to occur in highly vulnerable CA1 neurons after ischemia. Here we demonstrate that this Zn2+ accumulation contributes strongly to deleterious events occurring after ischemia including mitochondrial dysfunction, swelling and structural changes. We suggest that this mitochondrial Zn2+ entry may constitute a promising target for development of therapeutic interventions to be delivered after termination of an episode of transient global ischemia.

Realizing a Not-Strong-Not-Weak Polarization Electric Field in Single-Atom Catalysts Sandwiched by Boron Nitride and Graphene Sheets for Efficient Nitrogen Fixation.

Developing efficient single-atom catalysts (SACs) for nitrogen fixation is of great importance while remaining a great challenge. The lack of an effective strategy to control the polarization electric field of SACs limits their activity and selectivity. Here, using first-principles calculations, we report that a single transition metal (TM) atom sandwiched between hexagonal boron nitride (h-BN) and graphene sheets (namely, BN/TM/G) acts as an efficient SAC for the electrochemical nitrogen reduction reaction (NRR). These sandwich structures realize stable and tunable interfacial polarization fields that enable the TM atom to donate electrons to a neighboring B atom as the active site. As a result, the partially occupied pz orbital of a B atom can form B-to-N π-back bonding with the antibonding state of N2, thus weakening the N≡N bond. The not-strong-not-weak electric field on the h-BN surface further promotes N2 adsorption and activation. The NRR catalytic activity of the BN/TM/G system is highly correlated with the degree of positively polarized charges on the TM atom. In particular, BN/Ti/G and BN/V/G are identified as promising NRR catalysts with high stability, offering excellent energy efficiency and suppression of the competing hydrogen evolution reaction.

Synergistic Effect of Surface-Terminated Oxygen Vacancy and Single-Atom Catalysts on Defective MXenes for Efficient Nitrogen Fixation.

The production of ammonia (NH3) from molecular dinitrogen (N2) under ambient conditions is of great significance but remains as a great challenge. Using first-principles calculations, we have investigated the potential of using a transition metal (TM) atom embedded on defective MXene nanosheets (Ti3-xC2Oy and Ti2-xCOy with a Ti vacancy) as a single-atom electrocatalyst (SAC) for the nitrogen reduction reaction (NRR). The Ti3-xC2Oy nanosheet with Mo and W embedded, and the Ti2-xC2Oy nanosheet with Cr, Mo, and W embedded, can significantly promote the NRR while suppressing the competitive hydrogen evolution reaction, with the low limiting potential of -0.11 V for W/Ti2-xC2Oy. The outstanding performance is attributed to the synergistic effect of the exposed Ti atom and the TM atom around an extra oxygen vacancy. The polarization charges of the active center are reasonably tuned by the embedded TM atoms, which can optimize the binding strength of key intermediate *N2H. The good feasibility of preparing such TM SACs on defective MXenes and the high NRR selectivity with regard to the competitive HER suggest new opportunities for driving NH3 production by MXene-based SAC electrocatalysts under ambient conditions.

Using Machine Learning to Predict the Dissociation Energy of Organic Carbonyls.

Bond dissociation energy (BDE), an indicator of the strength of chemical bonds, exhibits great potential for evaluating and screening high-performance materials and catalysts, which are of critical importance in industrial applications. However, the measurement or computation of BDE via conventional experimental or theoretical methods is usually costly and involved, substantially preventing the BDE from being applied to large-scale and high-throughput studies. Therefore, a potentially more efficient approach for estimating BDE is highly desirable. To this end, we combined first-principles calculations and machine learning techniques, including neural networks and random forest, to explore the inner relationships between carbonyl structure and its BDE. Results show that machine learning can not only effectively reproduce the computed BDEs of carbonyls but also in turn serve as guidance for the rational design of carbonyl structure aimed at optimizing performance.

Electric Dipole Descriptor for Machine Learning Prediction of Catalyst Surface-Molecular Adsorbate Interactions.

The challenge of evaluating catalyst surface-molecular adsorbate interactions holds the key for rational design of catalysts. Finding an experimentally measurable and theoretically computable descriptor for evaluating surface-adsorbate interactions is a significant step toward achieving this goal. Here we show that the electric dipole moment can serve as a convenient yet accurate descriptor for establishing structure-property relationships for molecular adsorbates on metal catalyst surfaces. By training a machine learning neural network with a large data set of first-principles calculations, we achieve quick and accurate predictions of molecular adsorption energy and transferred charge. The training model using NO/CO@Au(111) can be extended to study additional substrates such as Au(001) or Ag(111), thus exhibiting extraordinary transferability. These findings validate the effectiveness of the electric dipole descriptor, providing an efficient modality for future catalyst design.

Enabling Efficient Charge Separation for Optoelectronic Conversion via an Energy-Dependent Z-Scheme n-Semiconductor-Metal-p-Semiconductor Schottky Heterojunction.

Achieving good charge separation while maintaining energetic electronic states in heterostructures is a challenge in designing efficient photocatalyst materials. Using first-principles calculations, we propose a Z-scheme Sn-m-Sp (n-semiconductor-metal-p-semiconductor) heterojunction as a viable avenue for achieving broad-spectrum sunlight absorption and, importantly, energy-dependent charge separation. As a proof-of-concept investigation, we investigated two ternary heterostructures, CdS-Au-PdO and SnO2-W-Ag2O, in which the electronic Fermi levels line up by virtue of the presence of an intermediate metal layer. A cascade of work functions in the relative order Wn < Wm < Wp drives electrons flowing from Sn to m and from m to Sp. The inner electric fields established at the Sn-m and m-Sp Schottky junctions selectively guide low-energy photoexcited electrons from Sn (CdS/SnO2) and low-energy holes from Sp (PdO/Ag2O) to the interposing Au or W metal, respectively. Importantly, relatively low Schottky barriers enforce charge separation by constraining high-energy photogenerated charges to the individual semiconductor layers. Operating together, these two mechanisms enable the achievement of highly efficient optoelectronic conversion.

A Hydrogenated Metal Oxide with Full Solar Spectrum Absorption for Highly Efficient Photothermal Water Evaporation.

Searching for cost-effective photothermal material that can harvest the full solar spectrum is critically important for solar-driven water evaporation. Metal oxides are cheap materials but cannot cover the full solar spectrum. Here we prepared a hydrogenated metal oxide (H1.68MoO3) material, in which H-doping causes the insulator-to-metal phase transition of the originally semiconductive MoO3. It offers a blackbody-like solar absorption of ≥95% over the entire visible-to-near-infrared solar spectrum, owing to its unusual quasi-metallic energy band, and high solar-to-heat conversion rate due to quick relaxation of excited electrons. Using a self-floating H1.68MoO3/airlaid paper photothermal film, we achieved a stable and high water vapor generation rate of 1.37 kg m-2 h-1, a superb solar-to-vapor efficiency of 84.8% under 1 sun illumination, and daily production of 12.4 L of sanitary water/m2 from seawater under natural sunlight. This thus opens a new avenue of designing cost-effective photothermal materials based on metal oxides.

Carbon Monoxide Oxidation Promoted by Surface Polarization Charges in a CuO/Ag Hybrid Catalyst.

Composite structures have been widely utilized to improve material performance. Here we report a semiconductor-metal hybrid structure (CuO/Ag) for CO oxidation that possesses very promising activity. Our first-principles calculations demonstrate that the significant improvement in this system's catalytic performance mainly comes from the polarized charge injection that results from the Schottky barrier formed at the CuO/Ag interface due to the work function differential there. Moreover, we propose a synergistic mechanism underlying the recovery process of this catalyst, which could significantly promote the recovery of oxygen vacancy created via the M-vK mechanism. These findings provide a new strategy for designing high performance heterogeneous catalysts.

Tuning Phase Transitions in Metal Oxides by Hydrogen Doping: A First-Principles Study.

Optimizing physical or chemical properties via electronic phase transitions is important for developing high-performance semiconductor materials. Using first-principles calculations, we established hydrogen doping (H-doping) as an alternative strategy for modulating phase transitions in metal oxide semiconductors. We found that H-doping can induce an insulator-to-metal phase transition in rutile TiO2 or wurtzite ZnO, a metal-to-insulator phase transition in rutile VO2, and sequential insulator-metal-insulator phase transitions in SnO2. H-doping not only creates defect-induced states inside the original bandgap but also induces new occupancy patterns in conduction band edge states. A linear relationship between occupation of the conduction band edge and H-doping concentration was found, offering the possibility of precisely tuning the material's free-carrier concentration, electrical conductance, and photoabsorption ability. We envision that this work will provide a promising way to tune electrical phase transitions in metal oxides.

Role of Hydrogen Bonding in Green Fluorescent Protein-like Chromophore Emission.

The fluorescence emission from green fluorescent protein (GFP) is known to be heavily influenced by hydrogen bonding between the core fluorophore and the surrounding side chains or water molecules. Yet how to utilize this feature for modulating the fluorescence of GFP chromophore or GFP-like fluorophore still remains elusive. Here we present theoretical calculations to predict how hydrogen bonding could influence the excited states of the GFP-like fluorophores. These studies provide both a new perspective for understanding the photophysical properties of GFP as well as a solid basis for the rational design of GFP-based fluorophores.

Structure-dependent luminescence of tetra-(4-pyridylphenyl)ethylene: a first-principles study.

Luminescent materials with tunable wavelengths have been attracting much attention due to their many promising applications. In the present work, the recently reported supramolecular coordination complexes of tetra-(4-pyridylphenyl)ethylene (TPPE) that produce variable-wavelength light emissions were investigated by time-dependent density functional theory (TDDFT) calculations. We discovered that variations in the luminescent wavelength of TPPE mainly depend on the structural deviations from molecular planarity, which affect the molecular orbital wavefunction distribution. An interesting trigonometric-functional relationship between the emission wavelength (the emission energy) and the dihedral angle defining deviations from molecular planarity was uncovered. The solvent effect was also considered to reveal the mechanism of the solvent-dependent fluorescence color. These findings may be helpful to rational molecular design for high-performance luminescent materials with tunable color.

Alterations in gene array patterns in dendritic cells from aged humans.

Dendritic cells (DCs) are major antigen-presenting cells that play a key role in initiating and regulating innate and adaptive immune responses. DCs are critical mediators of tolerance and immunity. The functional properties of DCs decline with age. The purpose of this study was to define the age-associated molecular changes in DCs by gene array analysis using Affymatrix GeneChips. The expression levels of a total of 260 genes (1.8%) were significantly different (144 down-regulated and 116 upregulated) in monocyte-derived DCs (MoDCs) from aged compared to young human donors. Of the 260 differentially expressed genes, 24% were down-regulated by more than 3-fold, suggesting that a large reduction in expression occurred for a notable number of genes in the aged. Our results suggest that the genes involved in immune response to pathogens, cell migration and T cell priming display significant age-related changes. Furthermore, downregulated genes involved in cell cycle arrest and DNA replication may play a critical role in aging-associated genetic instability. These changes in gene expression provide molecular based evidence for age-associated functional abnormalities in human DCs that may be responsible for the defects in adaptive immunity observed in the elderly.

Age-related differences in the response of the brain to dietary melatonin.

The aged brain is prone to excessive levels of immune activity, not initiated by an acute response to an extrinsic agent. While dietary melatonin is reported to attenuate the extent of expression of proinflammatory genes, little is known about the extent to which these changes can be translated into altered levels of corresponding proteins. The baseline levels of the proinflammatory cytokines, tumor necrosis factor alpha (TNF-α) and interleukin-1 alpha, were greater in older (~29 months old) compared to younger (~7 months old) mouse brains. Acute (3 h) exposure to lipopolysaccharide (LPS) induced activation of nuclear factor kappa B (NF-κB), but not inflammatory cytokines in the brain. The serum level of TNF-α was increased after LPS injection, indicating a systemic immune response to the bacterial cell wall component. Dietary melatonin (40 ppm for 9.3 weeks) did not prevent LPS-induced changes in younger animals but caused an increased systemic TNF-α response in older mice. Melatonin did reduce markers of carbonyl formation in brain proteins of young animals and nitrosylative damage to peptide-bound amino acid residues, in the brains of older animals. Acute LPS challenge did not significantly affect these oxidative markers. Thus, despite lack of clear evidence of attenuation of the NF-κB-cytokine inflammatory trajectory within the CNS by melatonin, this agent did show a protective effect against free radical-initiated injury to amino acid residues within proteins. The results illustrate that previously reported changes in gene expression following melatonin treatment need not be closely paralleled by corresponding changes in protein content.

Mitochondrial dysfunction in the liver of type 2 diabetic Goto-Kakizaki rats: improvement by a combination of nutrients.

Treatment with a combination of four nutrients, i.e. R-α-lipoic acid, acetyl-l-carnitine, nicotinamide and biotin, just as with pioglitazone, significantly improves glucose tolerance, insulin release, plasma NEFA, skeletal muscle mitochondrial biogenesis and oxidative stress in Goto-Kakizaki (GK) rats. However, it is not known whether treatment with these nutrients can improve mitochondrial function and reduce oxidative stress in GK rats. The effects of a combination of these four nutrients on mitochondrial function, oxidative stress and apoptosis in GK rat liver were investigated. Livers of untreated GK rats showed (1) abnormal changes in the activities of mitochondrial complexes (decreases in I, III and IV and increases in II and V), (2) increases in protein oxidation, (3) decreases in antioxidant enzymes (superoxide dismutase, glutathione S-transferase, NADH-quinone oxidoreductase-1), (4) a decrease in total antioxidant capacity but increases in reduced glutathione level and glyceraldehyde 3-phosphate dehydrogenase expression and (5) significant increases in apoptosis biomarkers, including expression of p21 and p53. A 3-month treatment with the four nutrients significantly improved most of these abnormalities in GK rats, and the effects of the nutrient combination were greater than those of pioglitazone for most of these indices. These results suggest that dietary supplementation with nutrients that are thought to influence mitochondrial function may be an effective strategy for improving liver dysfunction in GK diabetic rats.

Extended exposure to dietary melatonin reduces tumor number and size in aged male mice.

Several sets of male mice were given dietary melatonin over a series of experiments performed during a nine year period. Overall, melatonin-supplemented mice aged ≥26 months at sacrifice had significantly fewer tumors with lower severity than similarly aged control animals. The studies were originally designed to explore the potential of this agent for reducing the rate of onset of some genetic indices of brain aging. When these animals were sacrificed they were routinely examined for overt evidence of tumors and when these were found, a note was made of their occurrence, and of their size. Tumors are commonly found during senescence of several strains of mice. Since tumorigenesis was not the original intent of the study, these observations were recorded but not pursued in greater detail. In this report, these data have now been collated and summarized. This analysis has the disadvantage that tumor origin and morphology were not recorded. However, the study also has the advantage of being conducted over an extended period of time with many groups of animals. In consequence, many extraneous factors, which could be potential confounders, such as seasonal or dietary variations, are unlikely to have interfered with the analysis. The use of more than one mouse strain strengthens the possibility that the findings may have general relevance. Both aged and young animals were included in the original experiments but the tumor incidence in animals younger than 25 months was very low.

Hydroxytyrosol protects against oxidative damage by simultaneous activation of mitochondrial biogenesis and phase II detoxifying enzyme systems in retinal pigment epithelial cells.

Studies in this laboratory have previously shown that hydroxytyrosol, the major antioxidant polyphenol in olives, protects ARPE-19 human retinal pigment epithelial cells from oxidative damage induced by acrolein, an environmental toxin and endogenous end product of lipid oxidation, that occurs at increased levels in age-related macular degeneration lesions. A proposed mechanism for this is that protection by hydroxytyrosol against oxidative stress is conferred by the simultaneous activation of two critically important pathways, viz., induction of phase II detoxifying enzymes and stimulation of mitochondrial biogenesis. Cultured ARPE-19 cells were pretreated with hydroxytyrosol and challenged with acrolein. The protective effects of hydroxytyrosol on key factors of mitochondrial biogenesis and phase II detoxifying enzyme systems were examined. Hydroxytyrosol treatment simultaneously protected against acrolein-induced inhibition of nuclear factor-E2-related factor 2 (Nrf2) and peroxisome proliferator-activated receptor coactivator 1 alpha (PPARGC1α) in ARPE-19 cells. The activation of Nrf2 led to activation of phase II detoxifying enzymes, including γ-glutamyl-cysteinyl-ligase, NADPH (nicotinamide adenine dinucleotide phosphate)-quinone-oxidoreductase 1, heme-oxygenase-1, superoxide dismutase, peroxiredoxin and thioredoxin as well as other antioxidant enzymes, while the activation of PPARGC1α led to increased protein expression of mitochondrial transcription factor A, uncoupling protein 2 and mitochondrial complexes. These results suggest that hydroxytyrosol is a potent inducer of phase II detoxifying enzymes and an enhancer of mitochondrial biogenesis. Dietary supplementation of hydroxytyrosol may contribute to eye health by preventing the degeneration of retinal pigment epithelial cells induced by oxidative stress.