Recent Advances in Mechanistic Understanding of Metal-Free Carbon Thermocatalysis and Electrocatalysis with Model Molecules.

Metal-free carbon, as the most representative heterogeneous metal-free catalysts, have received considerable interests in electro- and thermo-catalytic reactions due to their impressive performance and sustainability. Over the past decade, well-designed carbon catalysts with tunable structures and heteroatom groups coupled with various characterization techniques have proposed numerous reaction mechanisms. However, active sites, key intermediate species, precise structure-activity relationships and dynamic evolution processes of carbon catalysts are still rife with controversies due to the monotony and limitation of used experimental methods. In this Review, we summarize the extensive efforts on model catalysts since the 2000s, particularly in the past decade, to overcome the influences of material and structure limitations in metal-free carbon catalysis. Using both nanomolecule model and bulk model, the real contribution of each alien species, defect and edge configuration to a series of fundamentally important reactions, such as thermocatalytic reactions, electrocatalytic reactions, were systematically studied. Combined with in situ techniques, isotope labeling and size control, the detailed reaction mechanisms, the precise 2D structure-activity relationships and the rate-determining steps were revealed at a molecular level. Furthermore, the outlook of model carbon catalysis has also been proposed in this work.

Leveraging machine learning approaches for predicting potential Lyme disease cases and incidence rates in the United States using Twitter.

BACKGROUND: Lyme disease is one of the most commonly reported infectious diseases in the United States (US), accounting for more than [Formula: see text] of all vector-borne diseases in North America. OBJECTIVE: In this paper, self-reported tweets on Twitter were analyzed in order to predict potential Lyme disease cases and accurately assess incidence rates in the US. METHODS: The study was done in three stages: (1) Approximately 1.3 million tweets were collected and pre-processed to extract the most relevant Lyme disease tweets with geolocations. A subset of tweets were semi-automatically labelled as relevant or irrelevant to Lyme disease using a set of precise keywords, and the remaining portion were manually labelled, yielding a curated labelled dataset of 77, 500 tweets. (2) This labelled data set was used to train, validate, and test various combinations of NLP word embedding methods and prominent ML classification models, such as TF-IDF and logistic regression, Word2vec and XGboost, and BERTweet, among others, to identify potential Lyme disease tweets. (3) Lastly, the presence of spatio-temporal patterns in the US over a 10-year period were studied. RESULTS: Preliminary results showed that BERTweet outperformed all tested NLP classifiers for identifying Lyme disease tweets, achieving the highest classification accuracy and F1-score of [Formula: see text]. There was also a consistent pattern indicating that the West and Northeast regions of the US had a higher tweet rate over time. CONCLUSIONS: We focused on the less-studied problem of using Twitter data as a surveillance tool for Lyme disease in the US. Several crucial findings have emerged from the study. First, there is a fairly strong correlation between classified tweet counts and Lyme disease counts, with both following similar trends. Second, in 2015 and early 2016, the social media network like Twitter was essential in raising popular awareness of Lyme disease. Third, counties with a high incidence rate were not necessarily related with a high tweet rate, and vice versa. Fourth, BERTweet can be used as a reliable NLP classifier for detecting relevant Lyme disease tweets.

Space-Time Distribution Characteristics of Tuberculosis and Its Socioeconomic Factors in Southern China from 2015 to 2019.

Purpose: Guangxi is a high prevalence area of tuberculosis (TB) in China, urgent needing of further TB reduction. Our purpose is to analyze the epidemiological characteristics of TB in Guangxi and analyze the relationship between socioeconomic factors and TB from the dimensions of time and space to provide evidence to effectively prevent and control TB. Patients and Methods: We performed a retrospective analysis of the epidemiology of TB. Moran's index (I) was used for spatial autocorrelation analysis, and space-time scanning was used to detect temporal, space, and space-time clusters of TB. A Bayesian space-time model was used to analyze related factors of the TB epidemic at the county level in Guangxi. Results: From 2015 to 2019, a total of 233,623 TB cases were reported in Guangxi. The majority of TB cases were in males; the reported incidence of TB was the highest in people aged ≥65 years. By occupation, farmers were the most frequently affected. The overall reported incidence of TB decreased by 4.95% during this period. Tuberculosis occurs all year round, but the annual reporting peak is usually from March to July. Spatial autocorrelation analysis showed that the reported incidence of TB in 2015-2019 was spatially clustered (Moran's I > 0, P < 0.05); Kulldorff's scan revealed that the space-time cluster (log-likelihood ratio = 2683.76, relative risk = 1.60, P < 0.001) was mainly concentrated in northern Guangxi. Using Bayesian space-time modeling, socioeconomic and healthcare factors are related to the high prevalence of TB. Conclusion: The prevalence of TB is influenced by a space-time interaction effect and is associated with socioeconomic and healthcare status. It is necessary to improve the economic development and health service in areas with a high TB prevalence.

Electron momentum density of boron-doped carbon nano-onions studied by electron energy-loss spectroscopy.

Valence Compton profiles (CPs) (electron momentum density projections) of B-doped carbon nano-onions (CNOs) as a function of the boron doping content were obtained by recording electron energy-loss spectra at large scattering angles using a transmission electron microscope, a technique known as electron Compton scattering from solids (ECOSS). The amplitude of the CPs at zero momentum increases with increasing doping content, while the shape of the CPs becomes narrower with increasing doping content. The differences between the profiles of B-doped CNOs and that of pristine CNOs have been clearly observed. These experimental results indicate substantially greater delocalization of the ground-state charge density in B-doped CNOs than in pristine CNOs. The results clearly demonstrate that the ECOSS technique is an efficient and reliable experimental method for studying electron density distributions in solids as a function of the heteroatom doping content.

Overall Oxygen Electrocatalysis on Nitrogen-Modified Carbon Catalysts: Identification of Active Sites and In†Situ Observation of Reactive Intermediates.

The recent mechanistic understanding of active sites, adsorbed intermediate products, and rate-determining steps (RDS) of nitrogen (N)-modified carbon catalysts in electrocatalytic oxygen reduction (ORR) and oxygen evolution reaction (OER) are still rife with controversy because of the inevitable coexistence of diverse N configurations and the technical limitations for the observation of formed intermediates. Herein, seven kinds of aromatic molecules with designated single N species are used as model structures to investigate the explicit role of each common N group in both ORR and OER. Specifically, dynamic evolution of active sites and key adsorbed intermediate products including O2 (ads), superoxide anion O2 - *, and OOH* are monitored with in situ spectroscopy. We propose that the formation of *OOH species from O2 - * (O2 - *+H2 O→OOH*+OH- ) is a possible RDS during the ORR process, whereas the generation of O2 from OOH* species is the most likely RDS during the OER process.

A facile strategy based on the metal-free design of carbon to deliver an insight into the active sites for liquid phase carbocatalysis.

An effective method to study the active sites for carbocatalysis is proposed based on designing a carbon catalyst in the absence of metal as the growth catalyst. The results suggest that the oxygenated groups on the aromatic carbons are mainly responsible for the catalytic reduction of nitrobenzene and some other reactions.

MS2CNN: predicting MS/MS spectrum based on protein sequence using deep convolutional neural networks.

BACKGROUND: Tandem mass spectrometry allows biologists to identify and quantify protein samples in the form of digested peptide sequences. When performing peptide identification, spectral library search is more sensitive than traditional database search but is limited to peptides that have been previously identified. An accurate tandem mass spectrum prediction tool is thus crucial in expanding the peptide space and increasing the coverage of spectral library search. RESULTS: We propose MS2CNN, a non-linear regression model based on deep convolutional neural networks, a deep learning algorithm. The features for our model are amino acid composition, predicted secondary structure, and physical-chemical features such as isoelectric point, aromaticity, helicity, hydrophobicity, and basicity. MS2CNN was trained with five-fold cross validation on a three-way data split on the large-scale human HCD MS2 dataset of Orbitrap LC-MS/MS downloaded from the National Institute of Standards and Technology. It was then evaluated on a publicly available independent test dataset of human HeLa cell lysate from LC-MS experiments. On average, our model shows better cosine similarity and Pearson correlation coefficient (0.690 and 0.632) than MS2PIP (0.647 and 0.601) and is comparable with pDeep (0.692 and 0.642). Notably, for the more complex MS2 spectra of 3+ peptides, MS2PIP is significantly better than both MS2PIP and pDeep. CONCLUSIONS: We showed that MS2CNN outperforms MS2PIP for 2+ and 3+ peptides and pDeep for 3+ peptides. This implies that MS2CNN, the proposed convolutional neural network model, generates highly accurate MS2 spectra for LC-MS/MS experiments using Orbitrap machines, which can be of great help in protein and peptide identifications. The results suggest that incorporating more data for deep learning model may improve performance.

Highly Efficient Metal-Free Nitrogen-Doped Nanocarbons with Unexpected Active Sites for Aerobic Catalytic Reactions.

Nitrogen (N)-doped nanocarbons (NDN) as metal-free catalysts have elicited considerable attention toward selective oxidation of alcohols with easily oxidizable groups to aldehydes in the past few years. However, finding a new NDN catalytic material that can meet the requirement of the feasibility on the aerobic catalytics for other complicated alcohols is a big challenge. The real active sites and the corresponding mechanisms on NDN are still unambiguous because of inevitable coexistence of diverse edge sites and N species based on recently reported doping methods. Here, four NDN catalysts with enriched pyridinic N species and without any graphitic N species are simply fabricated via a chemical-vapor-deposition-like method. The results of X-ray photoelectron spectroscopy and X-ray absorption near-edge structure spectra suggest that the dominating N species on NDN are pyridinic N. It is demonstrated that NDN catalysts perform impressive reactivity for aerobic oxidation of complicated alcohols at an atmospheric pressure. Eleven kinds of aromatic molecules with single N species and tunable π conjugation systems are used as model catalysts to experimentally identify the actual role of each N species at a real molecular level. It is suggested that pyridinic N species play an unexpected role in catalytic reactions. Neighboring carbon atoms in pyridinic N species are responsible for facilitating the rate-determining step process clarified by kinetic isotope effects, in situ nuclear magnetic resonance, in situ attenuated total reflectance infrared, and theoretical calculation. Moreover, NDN catalysts exhibit a good catalytic feasibility on the synthesis of important natural products (e.g., intermediates of vitamin E and K3) from phenol oxidation.

Oxygen Evolution Reaction at Carbon Edge Sites: Investigation of Activity Evolution and Structure-Function Relationships with Polycyclic Aromatic Hydrocarbons.

The abundance of available surface chemical information and edge structures of carbon materials have attracted tremendous interest in catalysis. For the oxygen evolution reaction (OER), the edge effects of carbon materials have rarely been studied in detail because of the complexity of various coexisting edge configurations and the controversy between carbon corrosion and carbon catalysis. Herein, the exact roles of common carbon active edge sites in the OER were interrogated using polycyclic aromatic hydrocarbons (PAHs) with designated configurations (zigzag and armchair) as model probe molecules, with a focus on structure-function relationships. Zigzag configurations of PAHs showed high activity for the OER while also showing a good stability at a reasonable potential. They show a TOF value of 0.276 s-1 in 0.1 m KOH. The catalytic activity of carbon edge sites was further effectively regulated by extending the π conjugation structure at a molecular level.

Combined study of the ground and excited states in the transformation of nanodiamonds into carbon onions by electron energy-loss spectroscopy.

The electron momentum density and sp2/sp3 ratio of carbon materials in the thermal transformation of detonation nanodiamonds (ND) into carbon nano-onions are systematically studied by electron energy-loss spectroscopy (EELS). Electron energy-loss near-edge structures of the carbon K-ionization in the electron energy-loss spectroscopy are measured to determine the sp2 content of the ND-derived samples. We use the method developed by Titantah and Lamoen, which is based on the ability to isolate the π* spectrum and has been shown to give reliable and accurate results. Compton profiles (CPs) of the ND-derived carbon materials are obtained by performing EELS on the electron Compton scattering region. The amplitude of the CPs at zero momentum increases with increasing annealing temperature above 500 °C. The dramatic changes occur in the temperature range of 900-1300 °C, which indicates that the graphitization process mainly occurs in this annealing temperature region. Our results complement the previous work on the thermal transformation of ND-derived carbon onions and provide deeper insight into the evolution of the electronic properties in the graphitization process.

A comparative study of nitrobenzene reduction using model catalysts.

A zigzag-type quinone performs better than an armchair-type quinone in the reduction of nitrobenzene. When different kinds of functionalities co-exist, the reaction is dominated by the most active sites, but the most negative sites should also be taken into consideration if the acitive sites have zigzag structures.

Electrocatalytic Water Oxidation at Quinone-on-Carbon: A Model System Study.

Nanocarbon can promote robust and efficient electrocatalytic water oxidation through active surface oxygen moieties. The recent mechanistic understandings (e.g., active sites) of metal-free carbon catalysts in oxygen evolution reaction (OER), however, are still rife with controversies. In this work, we describe a facile protocol in which eight kinds of aromatic molecules with designated single oxygen species were used as model structures to investigate the explicit roles of each common oxygen group in OER at a molecular level. These model structures were decorated onto typical nanocarbon surfaces like onion-like carbons (OLC) or multiwalled carbon nanotubes (MWCNT) to build aromatic molecule-modified carbon systems. We show that edge (including zigzag and armchair) quinones in a conjugated π network are the true active centers, and the roles of ether and carboxyl groups are excluded in the OER process. The plausible rate-determining step could be singled out by H/D kinetic isotope effects. The turnover frequency per C═O (∼0.323 s-1 at η = 340 mV) in 0.1 M KOH and the optimized current density (10 mA/cm2 at 1.58 V vs RHE) of quinone-modified carbon systems are comparable to those of promising metal-based catalysts.

Catalysis by hybrid sp2/sp3 nanodiamonds and their role in the design of advanced nanocarbon materials.

Hybrid sp2/sp3 nanocarbons, in particular sp3-hybridized ultra-dispersed nanodiamonds and derivative materials, such as the sp3/sp2-hybridized bucky nanodiamonds and sp2-hybridized onion-like carbons, represent a rather interesting class of catalysts still under consideration. Their characteristics, properties and catalytic reactivity are presented, with an analysis of the state-of-the-art of their use in gas- and liquid-phase reactions, including photo- and electro-catalysis. It is remarked that intrinsic differences exist between these and other nanostructured carbon catalysts. The analysis shows how different features make nanocarbons unique with respect to other types of catalysts and are the bases for an advanced design of nanocarbon-type catalysts. The aspects discussed regard the presence of hybrid sp2/sp3 configurations, nano-engineering related to the role of defects and vacancies in their catalytic behaviour, the creation of active sites by modification in the charge density at carbon atoms or C-C bonds, the generation of strained C-C bonds by curvature and other mechanisms, and the formation of semiconducting areas and defect sites at the interface with supported nanoparticles. The advanced strategies for identifying and quantifying active sites of carbon catalysts are highlighted.

Nanocarbons for Catalytic Desulfurization.

Nanocarbon catalysts are green and sustainable alternatives to metal-based catalysts for numerous catalytic transformations. The application of nanocarbons for environmental catalysis is an emerging research discipline and has undergone rapid development in recent years. In this focus review, we provide a critical analysis of state-of-the-art nanocarbon catalysts for three different catalytic desulfurization processes. In particular, we focus on the advantages and limitations as well as the reaction mechanisms of the nanocarbon catalysts at the molecular level.

Efficient and Highly Selective Solvent-Free Oxidation of Primary Alcohols to Aldehydes Using Bucky Nanodiamond.

Selective oxidation of alcohols to aldehydes is widely applicable to the synthesis of various green chemicals. The poor chemoselectivity for complicated primary aldehydes over state-of-the-art metal-free or metal-based catalysts represents a major obstacle for industrial application. Bucky nanodiamond is a potential green catalyst that exhibits excellent chemoselectivity and cycling stability for the selective oxidation of primary alcohols in diverse structures (22 examples, including aromatic, substituted aromatic, unsaturated, heterocyclic, and linear chain alcohols) to their corresponding aldehydes. The results are comparable to reported transition-metal catalysts including conventional Pt/C and Ru/C catalysts for certain substrates under solvent-free conditions. The possible activation process of the oxidant and substrates by the surface oxygen groups and defect species are revealed with model catalysts, ex situ electrochemical measurements, and ex situ attenuated total reflectance. The zigzag edges of sp2 carbon planes are shown to play a key role in these reactions.

Insights into the surface chemistry and electronic properties of sp2 and sp3-hybridized nanocarbon materials for catalysis.

Ultra-dispersed nanodiamond and its derivatives (UNDDs), including bucky nanodiamond and onion-like carbon, offer superior catalytic behavior relative to other nanocarbons. However, a systematic study of their unique properties has been rarely achieved. Their surface chemistry and electronic properties are therefore studied to reveal the essential differences of UNDDs compared to other nanocarbons for catalysis.

Revealing the Origin of Activity in Nitrogen-Doped Nanocarbons towards Electrocatalytic Reduction of Carbon Dioxide.

Carbon nanotubes (CNTs) are functionalized with nitrogen atoms for reduction of carbon dioxide (CO2 ). The investigation explores the origin of the catalyst's activity and the role of nitrogen chemical states therein. The catalysts show excellent performances, with about 90 % current efficiency for CO formation and stability over 60 hours. The Tafel analyses and density functional theory calculations suggest that the reduction of CO2 proceeds through an initial rate-determining transfer of one electron to CO2 , which leads to the formation of carbon dioxide radical anion (CO2 (.-) ). The initial reduction barrier is too high on pristine CNTs, resulting in a very high overpotentials at which the hydrogen evolution reaction dominates over CO2 reduction. The doped nitrogen atoms stabilize the radical anion, thereby lowering the initial reduction barrier and improving the intrinsic activity. The most efficient nitrogen chemical state for this reaction is quaternary nitrogen, followed by pyridinic and pyrrolic nitrogen.

Communication: Investigation of the electron momentum density distribution of nanodiamonds by electron energy-loss spectroscopy.

The electron momentum distribution of detonation nanodiamonds (DND) was investigated by recording electron energy-loss spectra at large momentum transfer in the transmission electron microscope (TEM), which is known as electron Compton scattering from solid (ECOSS). Compton profile of diamond film obtained by ECOSS was found in good agreement with prior photon experimental measurement and theoretical calculation that for bulk diamond. Compared to the diamond film, the valence Compton profile of DND was found to be narrower, which indicates a more delocalization of the ground-state charge density for the latter. Combining with other TEM characterizations such as high-resolution transmission electron spectroscopy, diffraction, and energy dispersive X-ray spectroscopy measurements, ECOSS was shown to be a great potential technique to study ground-state electronic properties of nanomaterials.

Facile Synthesis of Au Nanoparticles Embedded in an Ultrathin Hollow Graphene Nanoshell with Robust Catalytic Performance.

Au nanoparticles (NPs) uniformly embedded into an ultrathin hollow graphene nanoshell (Au@HGN) are synthesized using a facile template-based procedure. The obtained Au@HGN catalyst exhibits robust and stable catalytic performance in the reduction of 4-nitrophenol to 4-aminophenol, compared with that of traditional Au/TiO2 and previously reported Au- and Ag-based catalysts.

Efficient and highly selective boron-doped carbon materials-catalyzed reduction of nitroarenes.

Exploring the potential catalytic applications of boron-doped carbon materials is a fascinating challenge. Here we describe that boron-doped onion-like carbon and carbon nanotubes as metal-free catalysts exhibit excellent catalytic activity and stability in nitroarene reduction under a stoichiometric amount of reductant.