Decision Forest was used to train ERβ binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ERβ binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ERβ binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ERβ binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ERα prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ERβ or ERα.We developed an ER The NTD and LBD contain ligand responsive transcriptional activation function 1 (AF1) and activation function 2 (AF2) domains. The AF1 and AF2 domains are responsible for the regulation of the transcriptional activity of ERβ [β DBD and LBD had shown more than 95% and 55% sequence similarity with ERα DBD and LBD, respectively. The ERβ NTD is shorter than the ERα NTD and shows a very low sequence homology [β presents in both the nucleus and cytoplasm of the normal and cancer cells while ERα which presents in nuclei of benign and cancer cells. The cytoplasmic ERβ binds with the estradiol or agonist and moves into the nucleus to form homo-dimer and then binds with the specific estrogen response elements (EREs) to activate the transcription process through the interaction between the transcriptional modulators and recruitment of the general transcriptional machinery [β may act as a marker in various types of cancers and a significant predictor in the breast cancer treated with tamoxifen [β is a potential cancer target, highly expressed in various cancers which are reported as a negative for ERα [β is different from ERα which plays an important role in the breast and uterine cancers. Due to the high similarity between the two isoforms, it is highly challenging to design or identify compounds which specifically target one subtype. Emerging data for ER indicates that the identification of selective agonist or antagonist for ERβ will help treat various cancers such as colon, breast, prostate, and lungs with lower side effects. Hence ERβ is considered as one of the emerging oncogene target.Estrogen receptor (ER) is the ligand-dependent transcriptional factor. ER belongs to the nuclear receptor family. ERd for ER –3. The tribution . ERβ hasy of ERβ . ERβ DBDhomology . ERβ preachinery . ERβ mayamoxifen . ERβ is α or ERβ. The main mechanism of SERMs is to alter the estrogenic activity in the target tissues specificity. SERMs can selectively block the estrogens action in the breast cells and activate the estrogens action in bone, liver, and uterine cells. Hence, identification of selective estrogenic activity compound is an important task in drug discovery. Experimental identification of selective estrogenic activity compound is doable but very expensive and time consuming from a large pool of chemicals. Hence, in silico approaches for screening potential selective estrogenic activity compounds are in need. Some prediction models were developed using different in silico techniques such as pharmacophore model and molecular docking [Selective estrogen receptor modulator (SERM) is a drug or small molecule that acts as an agonist or antagonist by specifically binding to one of these two ER isoforms in the target tissues based on its specificity. The difference in binding activity to the two ER isoforms of a chemical is the metric for determination of chemicals that specifically bind for ER docking , 11.α binding activity using large sets of chemicals to ensure prediction reliability [β binding activity based on small sizes of chemicals of the particular scaffolds that cover a small chemical space [β binding activity need to be developed using large sets of diverse chemicals covering a large chemical space. Hence, in this study, we developed a QSAR model for predicting ERβ binding activity using large sets of chemicals which covers a wide range of chemical space.Multiple quantitative structure activity relationship (QSAR) models have been developed for predicting ERiability , 13. Preal space –22. Manyβ binding activity prediction model. The large set of chemicals and their ERβ binding activity data were collected from EADB [β model predictions by traditional assay and the high-throughput screening results. The important molecular descriptors for ERβ binding were identified using the cross validations. Prediction confidence was analyzed to provide an additional metric for application of the ERβ QSAR model.DF (Decision Forest) algorithm –25 was urom EADB , 27 and β predictive models constructed from the training set. The results of the cross validations were summarized in Figure β predictive models performed well. Moreover, the standard deviations of prediction accuracy, sensitivity, and specificity in the 1000 cross validations were 0.6%, 0.7%, and 6.1%, respectively. The small fluctuation in the performance of the models in the cross validations demonstrated the robustness of the ERβ predictive models generated in the cross validations. Not surprisingly, the average specificity was low because much fewer ERβ non-binders than binders were included in the training data set. It is expected that specificity could be improved when more ERβ non-binders were identified for training the model.One thousand 5-fold cross validations were conducted to assess the goodness and robustness of the ERβ prediction models constructed in the 1000 permutation tests and were plotted as the red bars in Figure β predictive models constructed from the training set had a good predictive power and were obtained unlikely solely by chance.The same performance metrics  were calculated for the ERβ predictive model developed in this study.The confidence analysis was performed on the predictions from the 5-fold cross validations. The predictions near evenly distributed in the 10 prediction confidence bins as shown by the black diamonds in Figure β models generated in the 5-fold cross validations. The frequency (number of models) of each descriptor in the cross validations were calculated. These 447 molecular descriptors were then ranked by their frequency values. The top 18 descriptors were shown in β binding of chemicals. The findings are consistent with our previously developed ERα model that revealed molecular weight, van der Waals volume, polarizability, and aromatic rings are important for a chemical to bind ERα [β and ERα. Off the 23 residues in the binding pockets, only two (ERβ M336 and I373 versus ERα L384 and M421) are different .The 447 molecular descriptors in the training set were used in different predictive ERbind ERα . Comparaifferent , confirmβ dimerization assays activity and EADB ERβ binding activity. The 21 common compounds were all binders in EADB. ToxCast OT_ER_ERβERβ_0480 ERβ dimerization assay showed 15 actives and 6 inactives  and non-binders (1.2%). The high imbalance in samples is a challenging issue in machine learning. Even with the robust algorithm DF, the performance of the developed model is expected to be different between ERβ binders and non-binders. The analysis of performance of the 5-fold cross validations yielded a very high sensitivity (93.6%) and a low specificity (55.2%) are consistent with the expectation. The permutation tests results also confirmed that the bias to prediction on ERβ binders because of imbalanced samples even there were no signals in the data sets (randomly permuted samples). In addition to the difference in numbers of ERβ binders and non-binders, different chemical spaces covered by ERβ binders and non-binders were observed  value which covers a wide range of ERd Figure . The ERββ binding prediction model but also conducted prediction confidence analysis to provide an additional metric that can be used in real applications of the model. The confidence analysis of the cross validation results  on DNA. Binding assays and dimerization assays measure a chemical activity on two different events in the function mechanism. Therefore, extrapolation of the developed model to prediction of inactives from other ERβ related endpoints such as dimerization should be very cautious. The model could be improved by adding more inactive compounds in training to increase chemical space for inactivs.We applied the ERs Tables  and 4. Hβ dimerization assay data yield less accurate predictions than the cross validations, especially for the predictions on the inactives. The reasons for that could be (i) all the ERβ binders play a role in the dimerization of ERβ,(ii) the EADB reported binders are not necessary to lead a dimerization signal to be observed in the ERβ dimerization assays, and (iii) the traditional ERβ binding assays are different from the high-throughput screening assays.Comparison between the cross validations and the applications revealed that extrapolation to ERβ binding activity prediction model was developed and its prediction performance and extrapolation to other ERβ related endpoints data were demonstrated. Comparison of ERβ binding activity predicted by the model developed in this study with ERα binding activity estimated using our previous ERα model [α or ERβ, facilitating drug discovery and development.The ERRα model  could idβ predictive model using DF. The ERβ binding affinity data from EADB ) were used to determine ERβ binders or non-binders for the training compounds. The activity values for 1812 compounds from two ERβ related assays in the ToxCast were used to assess concordance between EADB and ToxCast data as well as to estimate the performance of the QSAR model for predicting ToxCast results. The common compounds contained in both EADB and ToxCast databases were used for assessing the concordance. The compounds only assayed in ToxCast were used for estimating the goodness of extrapolation of the QSAR model to high-through screening assays. The Mold2 software [β predictive model using DF [β predictive models were estimated through 5-fold cross-validations. Prediction confidence of the 5-fold cross-validation results was analyzed. The critical molecular descriptors to ERβ binding were identified by the analysis of their frequency in the models during the cross validations. Predictive power and chance correlation of the models were assessed using permutation tests. The developed model was then used to predict ERβ activity of chemical assayed in ToxCast.The study design was depicted in Figure software  was usedusing DF , 31–33. β binding activity (binder or non-binder) for 2492 compounds were determined based on their logRBA (Relative Binding Affinity) values. The binding assays measure ERβ binding affinity of chemicals. Most of the ERβ binding activity data in EADB are determined using competitive binding assays. A competitive binding assay works by measuring how well a chemical competes with the radiolabeled ligand for the receptor ERβ and the binding affinity of the chemical is given as an IC50 value. For the experiments with the same reference chemical tested, the binding affinity IC50 values were converted into logRBA values. A compound was assigned as an ERβ binder if its logRBA value is equal to or greater than -5; otherwise the compound was determined as a non-binder  [βERβ_0480 and OT_ER_ERβERβ_1440. These two assays use a yellow fluorescent protein reporter that is dissected into two fragments and fused to two ERβ to interact within a signaling complex. The reporter is fully assembled when ERβ dimerize. Therefore, when the ERβ molecules are physically separated, no signal is detected; however, when two ERβ are in close physical proximity, the two fragments from the reporter interact to elicit a signal. Changes in signal intensity and location are used to measure dimerization activity of a chemical. Twenty-five compounds which do not have CAS numbers were removed. Twenty one compounds were contained in both the training set and ToxCast. They were used for assessing the concordance between the ERβ binding assays in EADB and the high-throughput screening ERβ dimerization assays in ToxCast. After removed the 21 compounds, the remaining compounds and their ERβ dimerization assay data [β binding activity prediction model. Among the 1812 compounds that were not included in the training set, 17 and 12 compounds were reported as not determined in OT_ER_ERβERβ_0480 and OT_ER_ERβERβ_1440 assays, respectively. The compounds in the application data sets were classified as active if their AC50 values are recorded in the ToxCast database; otherwise they were determined as inactive. Finally, 1805 (175 binders and 1630 non-binders) compounds for OT_ER_ERβERβ_0480 and 1800 (150 binders and 1650 non-binders) compounds for OT_ER_ERβERβ_1440 were used in the application data sets.There are 1858 compounds in the ToxCast database that were tested by ER2, 2016) , 37, 38 say data  from Tox2 (http://www.fda.gov/ScienceResearch/BioinformaticsTools/Mold2) software [2. Totally, 777 molecular descriptors were generated for each compound. Mold2 generates one-dimensional  and two dimensional  descriptors. The generated molecular descriptors were pre-processed to remove the less informative descriptors . Out of 777 molecular descriptors, 330 descriptors are removed as they were considered as less informative , and balanced accuracy were used in this study. The above mentioned metrics are computed using the following equations:The performance of the ERBalanced accuracy = (Sensitivity + Specificity) / 2In the above equations, TP indicates true positives ; TN represents true negatives ; FP means false positives ; and FN is false negatives .β predictive models. In a 5-fold cross validation, the training set was randomly divided into five equal subsets. Four subsets were used to generate a DF model and the remaining one subset was used to test the generated DF model. This process was repeated for each of the five subsets once and only once. The prediction results from the five subsets were then average to assess the prediction performance of the five DF models. To avoid possible chance correlation in the random division of a data set into five subsets, the 5-fold validation was repeated for 1000 times using different random divisions to reach a statistically reliable validation.Five-fold (5-fold) cross validations were used to evaluate the performance of ERβ binding activity data in the training data set were randomly scrambled to generate a permuted data set. A 5-fold cross validation test was performed on the permuted data set. The accuracy, specificity, and sensitivity of the 5-fold cross validation test were calculated to measure the chance correlation. To reach a statistically reliable estimation, this process was repeated for 1000 times to generate the 1000 permuted data sets for running 5-fold cross validations.Permutation tests were used to estimate the predictive power of the generated models. In a permutation test, the ERβ binder and non-binder but also to estimate the probability of the compounds to be an ERβ binder or non-binder. Hence, the models were assessed based on not only the overall prediction performance but also the relationship between the prediction confidence levels and their performance. The prediction confidence value was calculated using the below equation.Prediction confidence analysis was carried out not only to classify the compounds as ERβ binder (0.5 <= P <=1) or non-binder (0 < P < 0.5). Prediction confidence is a value between 0 and 1. The larger the prediction confidence is, the more confident the prediction is. To conduct confidence analysis, the prediction confidence was calculated for each of the predictions from the 1000 iterations of 5-fold cross validations at first. The predictions were then put into 10 even bins based on their prediction confidence values. Prediction performance metrics were finally calculated for the predictions in each of the prediction confidence bins according to their actual and predicted ERβ binding activity.Where P represents the probability of a chemical being an ERβ binding was estimated based on the frequency distribution of the descriptors used in the models in the 5-fold cross validations. A frequency value in the models from 5-fold cross validations was calculated for each of the molecular descriptors. The molecular descriptors were then ranked based on their frequency values. The descriptors that were used in more than 90% of the models were finally selected as the informative descriptors for the ERβ models.The importance of molecular descriptors in ERβ dimerization assays from ToxCast reported the estrogenic activity for >1800 compounds. Among the >1800 compounds, 21 had ERβ binding data in EADB. The ERβ binding data in EADB and the estrogenic activity data from high-throughput screening assays in ToxCast (β binding predictive model would be applied to data from different assays. The balanced accuracy was calculate for the two different ERβ dimerization assays in ToxCast relative to EADB, where EADB was used as a primary metrics to measure the activity data agreement between EADB and ToxCast assays.The two ER ToxCast  were comβ binding activity prediction model in extrapolation to predicting chemicals for their high-throughput screening ERβ dimerization assays activity. The application data sets contain 1805 and 1800 compounds for OT_ER_ERβERβ_0480 and OT_ER_ERβERβ_1440 ERβ dimerization assays from ToxCast. The constructed ERβ binding activity predictive model was used to predict ERβ binders and non-binders for the chemicals in the application data sets. The accuracy, sensitive, selectivity, and balanced accuracy were calculated to assess the goodness of the ERβ binding activity predictive model, when the model was extrapolated to predicting outcome from high-throughput screening ERβ dimerization assays.The application data sets were used to assess the goodness of the ERThe views expressed in this manuscript do not necessarily represent those of the U.S. Food and Drug Administration.β predictive model was developed and cross validated using a large data set from EADB. The cross validations demonstrated the predictive power of the ERβ predictive model. The prediction confidence provides an additional metric for application of the ERβ predictive model. The important molecular descriptors linked to the knowledge of ligand binding to ER were identified. Applications of the developed model to ERβ dimerization assays data in ToxCast demonstrated the capability of extrapolation to other ERβ related endpoints data. Combination of the model developed in this study with our previous ERα binding activity prediction model could help to design or identify the selective compounds for ERβ and ERα which would be crucial in drug discovery and safety evaluation.The ER


First‐principles calculations show that the surface oxygen vacancies can facilitate the electrochemical charge transfer by creating midgap electronic states. The specific capacitance of the UCNG electrode reaches 978.1 F g−1 (135.8 mA h g−1) at the current densities of 1 A g−1 and retains a high capacitance retention of 916.5 F g−1 (127.3 mA h g−1) even at current density up to 10 A g−1, showing remarkable rate capability (more than 93.7% capacitance retention). Additionally, 99.3% of the initial capacitance is maintained after consecutive 20 000 cycles, demonstrating enhanced cycling stability. Moreover, this proposed laser‐assisted growth strategy is demonstrated to be universal for other metal oxide/graphene composites with tuned electrical conductivity and electrochemical activity.The metal oxides/graphene composites are one of the most promising supercapacitors (SCs) electrode materials. However, rational synthesis of such electrode materials with controllable conductivity and electrochemical activity is the topical challenge for high‐performance SCs. Here, the Co Many approaches are available for fabricating the Co3O4/graphene composites for the charge‐storage performance. Dong et al. and Liao et al. reported that the Co3O4/graphene hybrids were in situ synthesized by a simple hydrothermal procedure, which delivers a high‐specific capacitance and exhibits an excellent cycling stability.3O4 nanosheets on graphene foam through the electrodeposition and annealing process, in which the 3D hierarchical structure and the synergetic effect of Co3O4 and graphene enhance the rate of ion and electron transport in electrodes.3O4/graphene nanostructures by the microwave irradiation method, which is used as the electrode for lithium ion batteries and shows an excellent rate capability and highly reversible large capacity.3O4 nanoparticles on graphene,3O4) and a short transport length of electrolyte ions and electrons in the composites.3O4 nanoparticles, high conductive graphene, and tight coupling between those two remains challenging, and there are still somewhat limited . What's more, controlling the intrinsic conductivity of Co3O4 nanoparticles and their electrochemical activity is another great challenge in promoting the SCs performance.The CoVO) has been explored to improve the intrinsic conductivity and electrochemical activity of transition metal oxides, and thus to increase their SCs performance,VO) can change the geometric and electronic structures as well as the chemical properties of transition metal oxides. However, owing to the limitations of synthetic methods ,In recent years, intentional creation of oxygen vacancies (3O4 nanoparticles/graphene (UCNG) composites with high rate capability (93.7%) for the first time. Without introducing any reducing agents and organic additives, ultrafine Co3O4 nanoparticles (≈10 nm) anchoring on graphene were obtained. Under laser irradiation, the graphene oxide (GO) was reduced to high conductive graphene through photothermal effects, accompanied the fragmentation (size reduction) of the porous Co3O4 nanorods (P‐Co3O4). More importantly, abundant oxygen vacancies were created on the ultrafine Co3O4 nanoparticles surface and the Co3O4 nanoparticles tightly anchored on the graphene, which significantly modifies the electric conductivity and the electrochemical activity of Co3O4 nanoparticles and hence enables the UCNG electrode with fast and reversible Faradaic reactions. The ultrafine Co3O4 nanoparticles/graphene composites are the promising candidates as the supercapacitor electrode. The capacitance of the composites reached 978.1 F g−1 (135.8 mA h g−1) at the current densities of 1 A g−1. It shows high rate capability, with more than 93.7% capacitance retention at even 10 A g−1. The superior performance also benefits from the surface midgap electronic states, which are enhanced by oxygen vacancies (VO) and facilitate the electrochemical charge transfer, as shown by our first‐principles calculations. The discovery in our paper not only establish a new route to prepare the Co3O4/graphene composites and shed light on their formation mechanism, but also opens a new avenue for realizing high‐performance SCs electrodes, especially for rate capability.Herein, we proposed a novel one‐step laser irradiation route through simultaneous laser‐induced reduction and fragmentation to prepare oxygen‐vacancy abundant and ultrafine Co22.1Figure3O4 nanorods (P‐Co3O4) and GO as the mixed precursor, and ultrapure water as solvent. During krypton difluoride (KrF) excimer laser  irradiation process, the fragmentation (size reduction) of the P‐Co3O4 nanorods occurs in the liquid, which may be driven by the laser–nanorods interaction‐induced plasma3O4 can also absorb this laser. Meanwhile, GO was transformed into reduced graphene oxide  for clarity) after the laser irradiation, mainly because its large surface area with strong optical absorption can result in a rapid temperature rise in GO  and subsequently lead to a significant temperature rise of water, which has been demonstrated to be a facile reduction method for the synthesis of graphene by recent reports.3O4 nanoparticles. As a result, the coupling of the fragmentation of the P‐Co3O4 with the reduction of GO to LG through laser irradiation could provide a novel method for the synthesis of the UCNG composites. For the yield and production rate of the laser processing, it has been demonstrated that the laser fluence dominates nanomaterials throughput for many materials.The proposed laser‐assisted growth strategy for the UCNG composites is illustrated in 2.23O4 nanoparticles/graphene composites prepared under various laser parameters were surveyed to prove the above strategy and optimize the preparation condition associated with excellent electrochemical properties for SCs electrodes applications . The optimized product (UCNG composites) with ultrafine Co3O4 nanoparticles and high conductive graphene was selected and discussed below. The morphology and microstructure of the UCNG were examined by the scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The SEM images  and Fourier transform infrared (FTIR) spectroscopy .A number of Co3O4 and LP‐Co3O4, was characterized by X‐ray diffraction (XRD) in a scan range of 10°–80° , their diffraction lines are in line with the UCNG composites, indicating that the laser irradiation and the graphene introduction have no effect on the Co3O4 crystal structure.The crystal structure of the UCNG composites, P‐Co3O4 and the UCNG composites  can be assigned to the Co 2p3/2 and Co 2p1/2 spin‐orbital peaks of Co3O4, respectively.3O4, the Co 2p peaks of the UCNG composites show two more obvious satellite peaks centered at about 786.3 and 802.5 eV, which are attributed to the Co2+ oxidation state, indicating that a part of the Co3+ ions was transformed to Co2+ and formed oxygen vacancies. A similar phenomenon of the transformation from higher valence metal ions to lower valence metal ions under laser irradiation is also found in our previous work,3O4 in the UCNG composites is reduced by laser irradiation and plentiful oxygen vacancies on the surface are produced, while the main body of the nanoparticles is still the Co3O4 crystal. The peaks in the XPS spectrum of LP‐Co3O4 are similar to those of the UCNG composites , indicating the oxygen vacancies are induced through laser irradiation and are not affected by the introduction of GO.To further investigate the oxidation status of cobalt, XPS measurements were carried out to examine both the P‐Co3O4 among the UCNG composites . The prominent resonance line of paramagnetic phase in the EPR absorption spectrum for the UCNG composites demonstrates that there are more Co2+ ions in the Co3O4 crystal, which further manifest the presence of oxygen vacancies.3O4 nanoparticles, the Co3O4 phase has not been altered as indicated by XRD  was furthermore employed to investigate the oxygen vacancies in the Co3O4, GO, and LG were analyzed by Raman spectroscopy. As observed in Figure E2g vibrational mode within aromatic carbon rings and the degree of graphitization for the area of excitation, while the D peak is ascribed to the small and isolated domains of aromaticity and the disorder of graphene. The weak 2D band (≈2700 cm−1) confirms the few‐layer graphene structures obtained by laser irradiation.3O4, three characteristic peaks at 470.0, 512.1, and 676.8 cm−1 were showed, which corresponds to the Eg, F12g, and A1g modes of the crystalline Co3O4. It should be noted that the peaks at 480.6 and 603 cm−1 from Co3O4 can be observed in the UCNG composites. Compared to LP‐Co3O4, the obvious peak shift and disappearance for the Co3O4 particles in the UCNG composites could be attributed to the effective bonding between Co3O4 and graphene,3O4 nanoparticles on the graphene sheets. In addition, after the laser irradiation, the D/G ratio associated with both LG and UCNG composites is increased (from 0.93 for GO to 0.97 for LG and 0.95 for UCNG composites), indicating a high concentration of structure defects in the graphene lattice induced by laser irradiation.3O4 nanoparticles, which restricts their mobility and prevents the Ostwald ripening process, i.e., the formation of large Co3O4 particles and the aggregation of the nanoparticles during the synthesis processes. These results are consistent with the SEM and SEM results. In order to discover the effect of GO on the formation of the ultrafine Co3O4 nanoparticles, we conducted the control experiments with the same experimental conditions except in the absence of GO. It can be seen that the porous Co3O4 nanorods completely fragmented after laser irradiation, but most of ultrafine Co3O4 nanoparticles agglomerate and even fused into larger spherical particles , which confirms that GO is necessary for the formation of the ultrafine Co3O4 nanoparticles.The local structures of the UCNG composites, LP‐Co2.3m KOH aqueous solution. Figure−1 in a potential range of −0.05–0.45 V. Two obvious redox peaks are observed in every curve, indicating that the capacitances are mainly from pseudocapacitance characteristics due to Faradaic redox reactions. The anodic and cathodic peaks exhibit a symmetrical shape, demonstrating the high redox reversibility of the UCNG composites.3O4 and CoOOH (P1/P2) and between CoOOH and CoO2 (P3/P4). The surface redox reactions are presented as follows (Equations We carried out cyclic voltammetry (CV), galvanostatic charge/discharge (GCD), and electrochemical impedance spectroscopy (EIS) measurements in a three‐electrode electrochemical cell with a platinum foil (1 cm × 1 cm) counterelectrode and an Ag/AgCl reference electrode in 2 −1 are 978.1, 955.6, 941.3, 929.2, 921.8, and 916.5 F g−1 , respectively. Figure −1. It was observed that the specific capacitance of the UCNG composites is highest among all samples  and most of other reported Co3O4‐based electrodes and even superior to some advanced carbon materials for high‐rate supercapacitors . The reason can be explained by the high electrical conductivity of the graphene sheets and the ultrafine Co3O4 nanoparticles. More importantly, the oxygen vacancies on the ultrafine Co3O4 nanoparticles surface in the UCNG composites induced by laser irradiation may play a key role in enhancing the rate capability. This result is confirmed by the fact that LP‐Co3O4 also shows high capacitance retention (85.8%) with increasing the current density from 1 to 10 A g−1.Figure RS, RCT, CDL, CF, and Wo in the circuit represent solution resistance, charge‐transfer resistance, double‐layer capacitance, pseudocapacitance, and the finite‐length Warburg diffusion element, respectively. The impedance spectra can be classified into high and low frequency regions. The equivalent series resistance  of the UCNG composites electrode is obtained from high‐frequency region of Nyquist plots , indicating a decreased electric resistance of the UCNG composites and interface resistance due to the incorporation of graphene. The enhanced conductivity of UCNG composites can be attributed to the combination of the unique synthesis procedures without using any surface modifying agent and organic additives, the plentiful oxygen vacancies created on the ultrafine Co3O4 nanoparticles surface, and the tightly anchoring of Co3O4 nanoparticles on the graphene. In the lower frequency region, the straight line was determined by ion diffusion. Obviously, the near‐zero slope for the UCNG composites and LP‐Co3O4 electrodes shows a lower diffusion resistance and an ideal polarizable capacitance in comparison with the P‐Co3O4 electrode, indicating an enhanced ion transmission properties from the laser‐induced effect (laser‐induced reduction and fragmentation). On the basis of above discussion, we might conclude that the combination of higher electrical conductivity and faster ion diffusion is responsible for the excellent rate capability of the UCNG composites.The electrochemical performance of the UCNG composites was further investigated by using EIS Figure d. The im−1 for 20 000 cycles, which is a very significant requirement for the practical applications of supercapacitors. Figure3O4 and graphene . Besides, Figure S18 (Supporting Information) represents the Co 2p XPS spectrum for the UCNG composites after 20 000 cycles. Although the intensity of the satellite peaks related to the Co2+ oxidation state decreases, it still can be clearly observed, demonstrating that abundant oxygen vacancies are still retained on the ultrafine Co3O4 nanoparticles surface. These results further indicate the excellent cyclability of the UCNG composites. The cycle stability of the UCNG composites is remarkably superior to those of P‐Co3O4, and LP‐Co3O4 electrodes. After 20 000 cycles, the P‐Co3O4 and LP‐Co3O4 electrodes retain about 84.7% and 92.8% of its original capacitance, respectively. The enhanced cycle stability of the UCNG composites is mainly owing to the synergistic effect derived from ultrafine Co3O4 nanoparticles with surface oxygen vacancies and the graphene with high conductivity. As we know, a significant number of defect sites on the graphene surface induced by laser irradiation of GO can provide more active sites to anchor the Co3O4 nanoparticles and strengthen the binding energy between Co3O4 and graphene sheets, which leads to a more stable structure and a little loss of the capacitance during the cycling test. In addition, the UCNG exhibited high power density (4000 W kg−1) and energy density (43.1 Wh kg−1), as shown in Figure S19b (Supporting Information).The cycle stability of the UCNG composites is investigated by repeating the charge/discharge test between −0.05 and 0.45 V at a high current density of 10 A g2.4VO) enhanced performance, we perform spin‐polarized density functional theory (DFT) calculations with the local density approximation + Hubbard U correction (LDA+U) approach. The calculation details are shown in the Supporting Information. Co3O4 has a spinel structure, with Co2+ ions in tetrahedral interstices and Co3+ ions in the octahedral interstices of the cubic close‐packed lattice of oxide anions. We choose the (001) surface to study because it is the most common surface observed in experiments and has the lowest energy.FigureB. For comparison, the Co atoms in the bulk octahedral sites (Co3+) have μ = 0, while those in the tetrahedral sites (Co2+) has μ ≈ 2.6 μB. The surface contributes new electronic states inside the band gap of bulk Co3O4, as shown in Figure To understand the origin of the oxygen vacancies VO enhanceVO). The VO further reduces the coordination of some of the surface Oh Co atoms to from 5 to 4. This is accompanied by the change of μ to ≈2.6 μB, similar to the Co2+ atoms in the bulk tetrahedral sites. Hence, the valance state of those Oh atoms is reduced to 2+, in agreement with the experiments. The VO also reduces the coordination of some of the Co atoms in the subsurface octahedral sites from 6 to 5, enhancing their contribution to the band gap states  The oxygen vacancies by laser irradiation dramatically increase the active sites on the Co3O4 surface and the conductivity of Co3O4, which promotes the electrochemical activity and facilitates the electrochemical reaction. (iii) The defect sites created by the laser on graphene and the large surface area of graphene efficaciously prevent the aggregation of Co3O4 nanoparticles, thus enhancing the rate performance and cycling stability.From the above characterization results, it should be noted that UCNG composites would be highly suitable as a high‐rate electrode material for SCs, primarily due to the following factors. (i) Well‐dispersed ultrafine Co2O3 and MnO2. To demonstrate the generality of this laser synthesis method, MnO2/graphene and Fe2O3/graphene composites were also fabricated under laser irradiation . MnO2/graphene and Fe2O3/graphene composites fabricated under laser irradiation also exhibit excellent electrochemical performance, especially for high‐rate capability .Researches for the generation of appropriate oxygen vacancies in metal oxide and the formation of reduced graphene oxide have long focused on traditional reductive method by reducing agent, reducing gas, etc.,33O4/graphene composite as a typical example and have developed a universal in situ laser‐assisted strategy to grow ultrafine metal oxides/graphene composites via simple laser irradiation in solution at ambient conditions. Under laser irradiation, the GO is reduced to LG with a low transfer resistance for efficient charge storage and delivery, while the porous Co3O4 nanorods completely smash into ultrafine Co3O4 nanoparticles with short ionic diffusion distance for fast ion transport. The as‐obtained UCNG composites exhibit high‐specific capacitance and excellent cycling stability. In particular, when the current density is increased from 1 to 10 A g−1, the capacitance of UCNG composites still retains 93.7%, exhibiting outstanding rate capability. ESR measurements and DFT calculations also reveal that new oxygen vacancies defect states are generated after laser irradiation, which serve as electron reservoir during the electrochemical process. We demonstrate that the unique UCNG composite nanostructures with the abundant oxygen vacancies on the ultrafine Co3O4 nanoparticles surfaces and the tightly anchoring of Co3O4 nanoparticles onto the graphene sheets could provide good electrical conductivity, high electrochemical activity, and effective pathways for rapid ionic/electronic transport and fast reversible Faradaic reactions. Combining the remarkable rate capability, outstanding capacitance and excellent cycle stability, we conclude that the UCNG composites are good electrode materials for supercapacitor compared to previous samples in literature. In addition, this novel synthetic method can be extended to other metal oxide/graphene composites, which will have great potentials in not only the energy storage field, but also in numerous other frontiers.In summary, we take the Co43O4 Nanorods (P‐Co3O4)Synthesis of Porous Co: All chemicals were of analytical grade and used without further purification. In a typical synthesis, 0.996 g of cobalt acetate tetrahydrate (Co(CH3COO)2·4H2O) and 1.0 g of polyvinyl‐pyrrolidone (PVP K‐30) were added in 40 mL of polyethylene glycol (PEG‐400). The mixture was kept vigorous stirring at 60 °C until Co(CH3COO)2·4H2O and PVP K‐30 were completely dissolved. Then, 1.0 g of urea (CO(NH2)2) was added to the resultant homogeneous solution. After stirring for 15 min, the reaction mixture from the first step was transferred to a Teflon‐lined stainless autoclave for solvothermal reaction at 160 °C for 20 h. After that, the product was washed with deionized water and ethanol for several times, and then dried in a vacuum oven at 60 °C for 12 h. The as‐prepared powder was annealed at 300 °C for 2 h in air with a heating rate of 1 °C min−1 to acquire porous Co3O4 nanorods (denoted as P‐Co3O4).Synthesis of UCNG Composites: GO was synthesized from natural flake graphite  according to the modified Hummer's method.3O4 nanorods (0.25 g) were dispersed into 10 mL of GO solution (1.5 mg mL−1). Then the mixture solution was irradiated with the beam of a KrF excimer laser  under a continuous magnetic stirring. The pulsed laser beam was guided by a reflection mirror and focused by a quartz lens on the mixture solution. The laser fluence was measured by a laser FieldMaxII Meter (Coherent). The optimal laser parameters are selected as following: 400 mJ pulse−1 cm−2 of laser fluence and 30 min of irradiating time. After laser irradiation, the products were collected by freeze drying for further characterization. The yield of ultrafine Co3O4 nanoparticles/graphene (UCNG) composites is about 94.3%. The maximal experimental throughput is ≈0.5 g h−1 for this typical ns laser system. For comparison, the laser‐reduced graphene was synthesized under the same experimental conditions in the absence of Co3O4 nanorods. Also, LP‐Co3O4 were also obtained in the absence of GO for controlled SCs measurement under similar laser conditions as described above.Characterization: UV–vis absorption spectra were recorded on a Shimadzu UV‐3600 spectrophotometer in the 200–1000 nm wavelength range at room temperature. The powder XRD measurements were performed using a Bruker D8‐Advance diffractometer with Cu Kα radiation (λ = 1.5418 Å). The powder samples were mounted flat and scanned over the range 10°–80°. The microstructures of the samples were observed by SEM (FEI Quanta 250 FEG) and TEM (JEOL JEM‐2010F). Raman spectra were taken on a Thermo Scientific Raman Microscope DXR with a 532 nm laser excitation length. The XPS was measured on a Kratos AXIS Ultra DLD spectrometer with a monochromatic AlKa X‐ray source. The EPR spectra were obtained using a Bruker A300 EPR spectrometer at X‐band (≈9 GHz) at room temperatures. FTIR spectra were recorded using a Nicolet 6700 FTIR spectrometer.Electrochemical Measurements: All electrochemical measurements were performed on a Zahner/Zennium electrochemical workstation at room temperature. The working electrodes were fabricated as follows. Briefly, the as‐prepared composites, acetylene black, and polyvinylidene difluoride were mixed in a mass ratio of 85:10:5 and dispersed in N‐methyl pyrrolidone solvent. Then, the slurry was pasted on the nickel foam substrate (1 cm × 1 cm) with a spatula and dried at 60 °C for 24 h. Finally, the electrode plate was pressed under a pressure of 10 MPa. The mass of active materials of (ultrafine Co3O4 nanoparticles/graphene composites) on current collector (nickel foam) was 6.5 mg cm−2. The monolithic electrode was tested in three‐electrode configuration assembled with a platinum foil (1 cm × 1 cm) auxiliary electrode and the Ag/AgCl reference electrode by CV, GCD, and EIS in an aqueous KOH electrolyte (2.0 m). The specific capacitance of electrode material was calculated from the charge and discharge curves, according to C = IΔt/ΔVm, where C is the specific capacitance (F g−1), Δt is the discharge time (s), I is the discharge current (A), ΔV is the operating potential window (V) during the discharge, and m is the mass (g) of active materials (UCNG composites) in as‐prepared electrode. EIS measurements were conducted with an AC amplitude of 5 mV in the 100 mHz–100 kHz frequency range.Spin‐Polarized DFT Computation: Spin‐polarized DFT calculations were performed using the Vienna Ab‐initio Simulation Package (VASP)U = 3 eV for Co d‐electrons, which has been shown to reproduce well the experimental structural parameters, heat of formation, and the band gap.−1. 5 × 5 × 1 k‐points with Monkhorst–Pack samplingThe authors declare no conflict of interest.SupplementaryClick here for additional data file.


A range of isoxazole‐containing amino acids was synthesized that displaced acetyl‐lysine‐containing peptides from the BAZ2A, BRD4(1), and BRD9 bromodomains. Three of these amino acids were incorporated into a histone H4‐mimicking peptide and their affinity for BRD4(1) was assessed. Affinities of the isoxazole‐containing peptides are comparable to those of a hyperacetylated histone H4‐mimicking cognate peptide, and demonstrated a dependence on the position at which the unnatural residue was incorporated. An isoxazole‐based alkylating agent was developed to selectively alkylate cysteine residues in situ. Selective monoalkylation of a histone H4‐mimicking peptide, containing a lysine to cysteine residue substitution (K12C), resulted in acetyl‐lysine mimic incorporation, with high affinity for the BRD4 bromodomain. The same technology was used to alkylate a K18C mutant of histone H3. N‐methanesulfonyl‐lysine  which plays a fundamental role in regulating protein function.https://www.ebi.ac.uk/chembl/) revealed that 4‐phenyl‐3,5‐dimethylisoxazole  family of bromodomain‐containing proteins (BCPs), we identified the 3,5‐dimethylisoxazole (DMI) moiety as an effective KAc mimic.2; Figure 1‐12KAc5KAc8  in complex with BRD4(1) . Inclusion of an amide on the linkage might allow formation of further hydrogen bonds with the bromodomain (5 and 10–14). The phenyl ring of the phenylalanine derivatives 8 and 9 was predicted to interact with the hydrophobic residues in the ZA channel region of the binding pocket. As the aim was to identify general KAc mimics, the structures of these compounds were kept flexible to facilitate binding to the maximum number of bromodomains. Although some isoxazole‐containing amino acids have been reported previously,Analysis of the X‐ray crystal structure of OXFBD02 , and BRD9 bromodomains (Table 8 showing the highest affinity for BRD4(1), but no binding to BRD9. The compound 5 showed the highest affinity for both BAZ2A and BRD9. These data indicate that isoxazole‐containing amino acids can act as KAc mimics in the context of bromodomain recognition. The data also suggest that ligand selectivity between bromodomains can be achieved by altering interactions in the KAc binding pocket, in addition to the peptide‐binding region (see the Supporting Information).KAc has a low affinity for bromodomains.Z2A, BRD4, and BRD3–5, based on a combination of their bromodomain affinity and structural flexibility. We decided to focus on BRD4(1) and an H4‐mimicking peptide as a model system, as the binding of variously acetylated histone H4‐mimicking peptides, and a large number of small‐molecule ligands, to BRD4(1) have been well characterized.Having established that isoxazole‐containing amino acids can bind to diverse bromodomains, we next sought to determine how effectively these amino acids could emulate KAc when incorporated into a histone‐mimicking peptide. For these studies we selected amino‐acids 1–20KAc5KAc8KAc12KAc16 [H4(KAc)4] shows the highest affinity for BRD4(1), with reported KD values of 3.1 μm (ITC)m (ITC).3–5 were sequentially introduced into H4(KAc)4 in place of KAc at either position 5, 8, 12, or 16. IC50 values for the resulting peptides were determined using an AlphaScreen assay 4 to determine whether the KAc mimics could interact more strongly than KAc with bromodomains.The histone H4 tail possesses four lysine residues which can be acetylated, and are located at positions 5, 8, 12, and 16. The tetra‐acetylated histone H4‐mimicking peptide H44, with these peptides showing IC50 values in the 12.6–37.8 μm range. The alkyl‐based amino‐acid 3 had the least detrimental effect on affinity when incorporated at these positions. However, substitution at either K12 or K16 resulted in peptides with similar affinities compared to that of the reference peptide (IC50=6.4–10.1 μm). When incorporated at these positions, the amide‐linked isoxazole 5 bound more strongly than the control peptide, with IC50 values of (6.8±0.9) and (8.4±0.8) μm for the K12 and K16 position, respectively. Positional sensitivity was consistent with studies on a histone H4‐mimicking peptide, which demonstrated that dual acetylation at K5 and K8 was almost as potent as H4(KAc)4.3–5 are able to mimic KAc when incorporated into a peptide.Affinities of the modified peptides for BRD4(1) were dependent on the position at which the unnatural residue was incorporated. Substitution at either K5 or K8 reduced the affinity compared to H4(KAc)4 (see Scheme S8), we investigated whether 5‐(chloromethyl)‐3‐methylisoxazole (Cl‐DMI) could be used to modify a cysteine within a peptide in situ. Trials were first performed with this alkylating agent on a model tripeptide (YCK), thus indicating that selective alkylation on a cysteine residue could be achieved . The reaction was then carried out using an H4‐mimicking peptide with a cysteine residue replacing lysine 12, H41‐20KAc5KAc8K12CKAc16 , as our data suggested that an isoxazole‐containing amino acid would be well tolerated at this position.We next sought to develop a complementary, tag‐and‐modify, approach in which a cysteine residue could be alkylated by an isoxazole‐containing electrophile. Based on the alkylation procedure used in the synthesis of the serine derivative m/z 1148.75 (expected m/z 1148.58), but was not present in the starting peptide.The degree of alkylation was determined using analytical HPLC, which allowed separation of the reactant and product, and identification of by‐products. In addition, the reaction could be followed using MALDI‐TOF‐MS Figure . HPLC in3K12C(DMI) was lyophilized and its affinity to BRD4(1) assessed in an AlphaScreen assay, and compared to the affinities of H4(KAc)3 peptides containing the amino‐acids 3–5 at position 12  μm, which is the lowest observed for any of the reported peptides in this assay. The decreased affinity of the H4(KAc)3K12C starting peptide, relative to H4(KAc)4, was confirmed by ITC . This result corroborates the ability of this isoxazole‐containing amino acid to mimic the interactions of KAc with bromodomains.Only one equivalent of Cl‐DMI was required for almost complete alkylation after 1 hour, whereas excess ≥5 equiv) reagent gave rise to unidentified by‐products . Once the alkylation had been optimized (see Table S2) the desired product was purified using HPLC. Isolated H4(KAc) equiv reTo determine whether an isoxazole‐derived KAc mimic could be incorporated into a protein, a K18C mutant of human histone H3, a position that is endogenously acetylated, was treated with Cl‐DMI under the optimized alkylation conditions Figure . After 33K12C gave an isoxazole‐containing peptide with an IC50 value, against BRD4(1), which was approximately a third of the cognate H4(KAc)4 peptide. Mild conditions enabled this method to be extended to modify a mutant of human histone H3 containing a single cysteine, thus demonstrating that this strategy is applicable to whole proteins. The complementary approaches that we report, which enable incorporation of effective and stable KAc mimics in a site‐selective manner into both peptides and proteins, will allow precise activation of KAc‐mediated protein–protein interactions, thus facilitating study of their downstream effects in an unprecedented manner.In conclusion, ten structurally diverse isoxazole‐containing amino acids were synthesized. They are the first unprotected monomeric amino acids able to prevent a histone‐mimicking peptide binding to the bromodomains of BAZ2A, BRD4(1), and BRD9. Incorporation into histone H4‐mimicking peptides confirmed that these isoxazole‐containing amino acids have the ability to mimic the interactions of KAc with bromodomains. Peptides bearing these modifications demonstrated increased bromodomain affinity in a positionally dependent manner. Furthermore, an isoxazole‐containing amino acid has been introduced into histone H4‐mimicking peptides using selective cysteine alkylation. Notably, a high‐yielding, specific alkylation of H4(KAc)As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.SupplementaryClick here for additional data file.


In this study, liquid fuels and chemicals  were synthesized in a one‐step process from CO2 and CH4 at room temperature (30 °C) and atmospheric pressure for the first time by using a novel plasma reactor with a water electrode. The total selectivity to oxygenates was approximately 50–60 %, with acetic acid being the major component at 40.2 % selectivity, the highest value reported for acetic acid thus far. Interestingly, the direct plasma synthesis of acetic acid from CH4 and CO2 is an ideal reaction with 100 % atom economy, but it is almost impossible by thermal catalysis owing to the significant thermodynamic barrier. The combination of plasma and catalyst in this process shows great potential for manipulating the distribution of liquid chemical products in a given process.The conversion of CO A particularly significant route that is currently being developed for CO2 utilization is catalytic CO2 hydrogenation. This process can produce a range of fuels and chemicals, including CO, formic acid, methanol, hydrocarbons, and alcohols; however, high H2 consumptions (CO2+3 H2→CH3OH+H2O) and high operating pressures (ca. 30–300 bar) are major challenges associated with this process.Chemical transformations of CO2, the direct conversion of CO2 with CH4  into liquid fuels and chemicals  represents another promising route for both CO2 valorization and CH4 activation. CH4 is an ideal H supplier to replace H2 in CO2 hydrogenation as CH4 has a high H density and is available from a range of sources . Moreover, it is an inexpensive carbon source that can increase the atom utilization of CO2 hydrogenation owing to the stoichiometric ratio of C and O atoms, as well as reduce the formation of water.Instead of using H2 and CH4 to form acetic acid on a Zn‐doped ceria catalyst by density functional theory (DFT) modeling;2 and CH4 into acetic acid is a reaction with 100 % atom economy [Equation 2) at high temperatures (>700 °C), and 2) conversion of syngas into liquid fuels and chemicals at high pressures. This indirect route for CO2 valorization and CH4 activation is inefficient as the DRM process for syngas production is highly endothermic and requires high temperatures and energy input [Equation 2 and CH4) into liquid fuels or chemicals in a one‐step catalytic process bypassing the production of syngas. A stepwise method was proposed to convert CO2 and CH4 into acetic acid over Cu/Co‐based catalysts,2O3,2, and Rh/SiO24, forming CHx species on the catalyst surface. Subsequently, the feed gas was changed from CH4 to CO2, and acetic acid was formed through the reaction of CO2 with CHx over the catalyst. This indirect process is complicated by the periodic change of reactants and the product collection.Recently, Ge and co‐workers investigated the direct C−C coupling of COEquation . HoweverEquation . Catalys2 and CH4) into reactive species, including radicals, excited atoms, molecules, and ions. These energetic species are capable of initiating a variety of chemical reactions. Although much effort has been devoted to the use of NTPs for the degradation of gas pollutants, far less has been done with regard to their use in the synthesis of fuels and chemicals.4 and CO2 into liquid fuels and chemicals.2 and CH4 into oxygenates has resulted in poor selectivities and yields.Non‐thermal plasmas (NTPs) offer a unique way to enable thermodynamically unfavorable chemical reactions at low temperatures owing to the non‐equilibrium character. The overall gas temperature in an NTP remains low while the generated electrons are highly energetic with a typical electron temperature of 1–10 eV, which is sufficient to activate inert molecules  and atmospheric pressure. This setup is unique and has not been reported previously. Figure 4 and CO2 conversions amounted to approximately 18.3 % and 15.4 %, respectively  reactor with a ground water electrode  for the one‐step conversion of CO2O3 catalyst in the DBD enhanced the selectivity for acetic acid to 40.2 %, compared to the plasma‐only mode and the plasma reaction using γ‐Al2O3 only (20.2 %). Acetic acid was the major product regardless of the catalyst used, followed by methanol and ethanol , and slightly increased C2H6 production, but had a weak effect on the selectivity for CO  and other CxHy . In addition, compared to the plasma‐only mode, the conversion of CO2 and CH4 slightly decreased with packing catalysts. This phenomenon can be attributed to the change in discharge behavior induced by the catalyst, which had a negative effect on the reaction . Interestingly, C6H12O4 (CAS No. 49653‐17‐0) was found on the inner reactor wall in the plasma‐catalyst mode . These results demonstrate the feasibility of using NTPs for the direct conversion of CH4 and CO2 into higher‐value liquid fuels and chemicals in a single‐step process under ambient conditions, bypassing the formation of syngas.Combining the plasma process with a catalyst shows great potential for manipulating the production of different oxygenates under ambient conditions. Clearly, packing the Cu/γ‐All Figure  a. HCHO s Figure  b. Howev4/CO2 DBD .To understand the formation of the liquid chemicals, optical emission spectroscopy (OES) was used to investigate the species produced in the CH2 reactions produced about 95 % vibrationally excited CO2 (CO2(v)) compared to electronically excited CO2 as shown in Figure S3 and Table S4. O radicals generated from CO2 dissociation can attack CO2(v) molecules to produce CO (S1 and S2).3 derived from CH4 dissociation cannot be detected by OES, but recent simulations revealed that electron‐impact dissociation of CH4 leads to 79 % CH3 formation and only 15 % and 5 % CH2 and CH, respectively.3 is the dominant species in the CH4/CO2 DBD. In addition to electrons (S4 in Table S3), reactive species such as OH, O, and H can also react with CH4 to produce CH3 radicals (S5–S7) in the CH4/CO2 DBD. Additionally, OH is an important species, especially for alcohol formation. In the CH4/CO2 DBD, OH could be produced indirectly by reactions S8–S13, with S8 and S9 as the major channels based on the reaction rate coefficients and Ea.−29 cm3 molecule−1 s−1 and a high Ea value of 111 kJ mol−1 were determined for ground‐state CO2 reacting with an H radical to produce an OH radical; this reaction (S10) can be accelerated by using CO2(v) instead of ground‐state CO2,2(v)+H→CO+OH could be one of the major routes for OH formation under these conditions as CO2 is mainly present in vibrationally excited states .CO is mainly derived from reactions S1–S3 (Table S3) in the DBD. Our simulation showed that electron‐impact CO3, and OH radicals are the key species in the CH4/CO2 plasma reaction. Therefore, possible reaction pathways for the formation of acetic acid, methanol, and ethanol under these conditions are proposed in Scheme Based on the analysis of the gaseous and condensed liquid products and the OES results, CO, CH3 radical to form an acetyl radical (CH3CO) by reaction S14 in Table S3 with a low energy barrier of 28.77 kJ mol−1,4/CO2 ratio, with optimal acetic acid formation at a CH4/CO2 ratio of 1:1. Correspondingly, the relative intensities of the CO band head and the O atomic line increased with a decrease in the CH4/CO2 ratio from 3:1 to 1:2 while that of the CH band head increased . This suggests that decreasing the CH4/CO2 molar ratio decreases the generation of CH3 radicals, but increases OH formation. A similar mechanism of acetic acid formation has been proposed on the basis of DFT modeling3 and carboxyl radicals (COOH) could also form acetic acid by reaction S16, while COOH radicals may be formed from reactions S17 and S18 in Table S3.Two possible reaction pathways could contribute to the formation of acetic acid. CO can react with a CH4/CO2 molar ratio decreased the generation of CH3 radicals, but increased OH formation . Simultaneously, the formation of CH3OH increased initially with a decrease in the CH4/CO2 molar ratio and reached a peak at a CH4/CO2 molar ratio of 1:1. By contrast, the formation of C2H5OH decreased continuously as the CH4/CO2 molar ratio was decreased ,2H5OH formation requires several elementary reactions (S20–S24). The recombination of a CH3 radical with itself forms C2H6 (S20),2H5 radical by reactions S21–S23, with S21 as the primary reaction according to the reaction rates.2H5 radical finally recombines with OH to form C2H5OH with a high rate coefficient of 9.34×10−11 cm3 molecule−1 s−1 (S24).Decreasing the CHactions S–S24. The3OH oxidation, and methylene (CH2) oxidation can lead to the generation of HCHO over noble‐metal catalysts.3OH, C2H5OH, and CH3COOH and increased the selectivity for HCHO and C2H6  could be the primary source for HCHO formation by oxidation reactions. Namely, CHx in the gas phase could be adsorbed onto the surface of the catalyst to form HCHO by the oxidation of CH2, ad  and to produce C2H6 by self‐recombination of CH3 radicals instead of converting CH3 into CH3OH, C2H5OH, and CH3COOH. This could explain why the presence of the Au and Pt catalysts in the plasma decreased the formation of CH3OH, C2H5OH, and CH3COOH, but enhanced the production of C2H6 and HCHO , could be a good activity descriptor towards the formation of different products in DRM.Clearly, adding catalysts to the plasma reaction influences the distribution of the formed oxygenates, especially for the formation of HCHO after addition of the Pt and Au catalysts, revealing the occurrence of surface reactions in addition to plasma gas phase reactions.6 Figure  a. Consi Figures  a and b. Figures , whereas2 with CH4 has been achieved by using a novel atmospheric‐pressure DBD reactor. The total selectivity for liquid chemicals was approximately 50–60 %, with acetic acid as the major product. The CH4/CO2 molar ratio and the type of catalyst can be used to manipulate the production of different oxygenates. These results clearly show that non‐thermal plasmas can be used to overcome the thermodynamic barrier for the direct transformation of CH4 and CO2 into a range of strategically important platform chemicals, especially for the production of acetic acid with 100 % atom economy. Additionally, combining the DBD with noble‐metal catalysts produced formaldehyde, which cannot be generated in the same plasma reaction without a catalyst. This finding suggests that new research should be directed at designing a catalyst with high selectivity towards a desirable product.In conclusion, the one‐step room‐temperature synthesis of liquid fuels and chemicals from the direct reforming of COThe authors declare no conflict of interest.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.SupplementaryClick here for additional data file.


Herein, on‐surface self‐assembly is demonstrated to be an effective, facile, and damage‐free method to take full advantage of the large ratio surface of MoS2 for HER by using multiscale simulations. It is found that as supplement of edge sites of MoS2, on‐MoS2 M(abt)2  owns high HER activity, and the self‐assembled M(abt)2 monolayers on MoS2 can be obtained through a simple liquid‐deposition method. More importantly, on‐surface self‐assembly provides potential application for overall water splitting once the self‐assembled systems prove to be of both HER and oxygen evolution reaction activities, for example, on‐MoS2 Co(abt)2. This work opens up a new and promising avenue (on‐surface self‐assembly) toward the full exploitation of the basal plane of MoS2 for HER and the preparation of bifunctional catalysts for overall water splitting.The development of nonprecious electrochemical catalysts for water splitting is a key step to achieve a sustainable energy supply for the future. Molybdenum disulfide (MoS Unfortunately, the active sites of pristine MoS2 are merely located at the edge, leaving a large area of in‐plane domains useless.2 is a common strategy toward the utilization of the basal plane,2.2 from 2H to 1T (1T′) phases,2 hinders its practical applications.2 is highly demanding.With the emergence of 2D materials,2 and CO2 reductions,2 can be fully exploited for HER through on‐surface self‐assembly. To this end, we need to verify that the following two conditions can be satisfied: (i) on‐MoS2 molecule possesses high HER activity; and (ii) the molecules self‐assembled on the basal plane of MoS2 via intermolecular weak interactions are stable and will not diffuse into water. By means of multiscale simulations combined density functional theory (DFT) and classical molecular dynamics (MD), we demonstrate that both the two preconditions can be fulfilled by M(abt)2  on MoS2, showing that on‐surface self‐assembly is indeed an effective approach to take advantage of the large‐area basal plane of MoS2 for HER without damage on MoS2. Also, a facile method toward the preparation of self‐assembled M(abt)2 monolayers is proposed, that is dropping M(abt)2 solution onto MoS2.Recently, molecular self‐assembly on gold and carbon‐based materials has been successfully used to prepare electrochemical catalysts toward O2 and N sites of on‐MoS2 Co(abt)2 for HER, OER can be catalyzed at Co sites of on‐MoS2 Co(abt)2, suggesting that on‐surface self‐assembly is also able to serve for overall water splitting. Our idea, via on‐surface self‐assembly to make the best use of the large‐area in‐plane domains of MoS2 for HER and prepare bifunctional catalysts for overall water splitting, is schematically displayed in FigureOn‐surface self‐assembly has been used to prepare single‐function catalysts.22.12 crystal has been demonstrated to be of high HER activity experimentally,2, including the surface and inner positions of the crystal. The HER and OER activities are both evaluated by the binding free energy (ΔG) that was developed by Nørskov and co‐workers,G is, the better HER performance is. On the surface, the ΔG of N, S, and Ni sites are −0.07, 0.67, and 1.02 eV, respectively  also owns better HER activity in comparison with the S site (ΔG is 0.55 eV), while the Ni site is unfavorable for hydrogen adsorption due to easy migration to adjacent sites. By comparing ΔG of surface and inner N sites, it can be derived that the surface N site is superior to the inner of the bulk material in the HER activity.Ni(abt)‐workers,H+ + e− +2 crystal is placed in water. The simulation shows that water molecules distribute on the surface of Ni(abt)2 crystal and do not penetrate into the inner part of the bulk. Considering that the inner HER activity is not high and water molecules are difficult to diffuse into the bulk, it can be concluded that HER mainly concentrates on the surface N sites of Ni(abt)2 crystal. In this respect, it will be highly material‐saving if Ni(abt)2 molecules are processed into ultrathin films or even monolayer, which can be potentially realized by bottom‐up on‐surface self‐assembly.In addition, Figure S1d of the Supporting Information records the water distribution when Ni(abt)2 molecule is placed on the surface of MoS2, and the HER activity of on‐MoS2 Ni(abt)2 is evaluated accordingly. As shown in Figure G of N, S, and Ni sites of on‐MoS2 Ni(abt)2 are −0.02, 0.64, and 1.11 eV, respectively, and the N site is responsible for HER. The ΔG values are similar to the surface sites of Ni(abt)2 crystal, indicating that Ni(abt)2 is weakly coupling with MoS2 surface. Moreover, the distance between MoS2 and Ni(abt)2 is above 3.0 Å , further suggesting that Ni(abt)2 is physically self‐assembled on MoS2 surface via intermolecular weak interaction and thus will not bring damage on MoS2. Therefore, the self‐assembly of Ni(abt)2 molecules on MoS2 keeps high HER activities of both N sites from on‐MoS2 Ni(abt)2 and edge sites from MoS2. The coverage is further increased by two times, close to the coverage limit. Under such a high coverage, the N site still owns high HER activity and is considerably more superior than S and Ni sites , in good agreement with the results in Figure Next, Ni(abt)2.22 molecules physically self‐assembled on MoS2 via intermolecular weak interactions are stable on MoS2 and will not diffuse into water. Figure2 molecules on MoS2 placed in water and Figure 2 molecules still adhere to the MoS2 surface and do not diffuse into water. The average height of Ni(abt)2 molecules relative to the MoS2 substrate is recorded in Figure 2 Ni(abt)2 molecules are very stable in water. Like the case at 300 K, the system is still very stable even at a slightly higher temperature, e.g., 350 K . We also explore the opposite case, that is Ni(abt)2 molecules initially placed in water 2 prefers staying on the surface of MoS2 rather than diffusing into water.Another essential prerequisite is that Ni(abt)2 is generated from large‐area in‐plane domains of MoS2, so it is desirable to explore the effect of H2 on the system stability. Figure S4 of the Supporting Information records the dynamic evolution of 80 Ni(abt)2 and 20 H2 molecules placed on MoS2. H2 molecules fast escape from MoS2 and diffuse into water, showing that on‐MoS2 H2 is very unstable. The intrinsic reason is owing to the extremely weak interaction between H2 and MoS2, only 0.06 eV. By contrast, Ni(abt)2 molecules have not been influenced by H2 and adhere to the MoS2 surface throughout. Therefore, we conclude that the generated H2 fast diffuses into water and does not give rise to the instability of Ni(abt)2 on MoS2.H2.32 Ni(abt)2 is of high HER activity above. Next, we will explore how to prepare self‐assembled Ni(abt)2 monolayers on MoS2. Many studies have shown that ultrathin self‐assembled organic monolayers can be prepared through a facile liquid‐deposition method,2 monolayers on MoS2 can be derived: (i) an appropriate solvent that can dissolve Ni(abt)2 and (ii) dissolved Ni(abt)2 molecules that can deposit onto MoS2.We have demonstrated that on‐MoSFigure2 molecules in water. Ni(abt)2 molecules are uniformly placed in water in the beginning  till 12 ns at which all Ni(abt)2 molecules aggregate together as shown in Figure 2 is insoluble in water, in line with the experiment.2 is known to be crystallized from diethyl ether,2 molecules in diethyl ether is performed. As expected, the Ni(abt)2 molecules are fast separated from each other in diethyl ether 2 solution is dropped on MoS2. Therefore, to reflect the actual situation more reasonably, the volatilization process is considered in our simulations. Figure2 solution dropped onto MoS2. Obviously, diethyl ether molecules gradually escape from the solution, but Ni(abt)2 molecules remain in solution and gradually deposit onto MoS2 2.42 retains the merits of high HER activities from both MoS2 and Ni(abt)2. Also, the integration of various active sites from components of the complex is promising for bifunctional or multifunctional catalysts, as learned from nanoscale alloys2 and Ni(abt)2 has been demonstrated to be of high HER activity, so if on‐MoS2 Ni(abt)2 is also of high OER activity, it can be served as the bifunctional catalyst for overall water splitting. For OER, the reaction consists of four elementary processesG1, ΔG2, ΔG3, and ΔG4 are the free energy differences of the four processes mentioned above, respectively. In general, oxygen evolution or reduction occurs at transition metal atoms. As shown in Figure2 Ni(abt)2 the calculated ΔG1, ΔG2, ΔG3, and ΔG4 are 2.00, 0.04, 2.91, and −0.03 eV, respectively. The rather positive ΔG1 and ΔG3 suggest the weak bonding strength between the Ni site and OH* and OOH*. The process from O* to OOH* is the potential‐determining step and the overpotential η reaches up to 1.68 V, showing that the OER activity is very poor.We have shown that the on‐surface self‐assembly of Ni(abt)2 Co(abt)2. Like Ni(abt)2 for HER, the N site owns high HER activity (ΔG is only 0.04 eV) and its HER activity is comparable to that of on‐MoS2 Ni(abt)2 2 monolayers can be used to effectively utilize the large‐area basal plane of MoS2 for HER. In addition, self‐assembled Co(abt)2 monolayers on MoS2 can also be prepared by a simple operation similar to on‐MoS2 Ni(abt)2, as shown in Figure S6 of the Supporting Information. Unlike Ni(abt)2 for OER, each step is moderate for the Co site of on‐MoS2 Co(abt)2 2. Our calculations show that OER can be catalyzed by Co(abt)2 along the four‐electron pathway. However, this does not mean that OER is impossible to occur at Co(abt)2 along other pathways. There may even exist better pathways with lower OER overpotentials compared to the four‐electron pathway, but this will not change our conclusion that Co(abt)2 can effectively catalyze OER. Therefore, we can conclude that on‐MoS2 Co(abt)2 is of both high HER and OER activities, suggesting that on‐surface self‐assembly can serve for the preparation of efficient bifunctional catalysts toward overall water splitting.As Co‐based materials are another commonly applied and nonprecious electrocatalysts, we further investigate the catalytic behavior of on‐MoS2 and the intermediates of M(abt)2 during HER and OER  are −1.76, −1.59, −1.56, −1.43, and −1.75 eV, respectively, which is comparable to that between MoS2 and M(abt)2 (−1.63 eV). This shows that M(abt)2 on MoS2 is still stable during HER and OER. The distinct catalytic performance for the Ni site of Ni(abt)2 and the Co site of Co(abt)2 can be understood well by the theory of d band center (εd). The closer to the Fermi level the εd is, the stronger the binding of the adsorbate and the catalytic site is. As shown in Figure d of the Co atom is closer to the Fermi level; therefore the binding of the adsorbates and the Co atom is stronger, leading to lower ΔG of the adsorbate H, OH, and OOH , which strengthens the bonding of the O atom and Ni(abt)2, thus resulting in a lower ΔG.The binding energies of MoS32 for HER and prepare bifunctional catalysts for overall water splitting. We demonstrated that the weak interaction between M(abt)2 molecules and MoS2 plays an important role in the effective bottom‐up on‐surface self‐assembly from three aspects: (a) rendering a facile preparation by liquid‐deposition method possible, (b) keeping M(abt)2 molecules distributing on the MoS2 surface instead of diffusing into water, and (c) preserving high HER activities of both edge sites of MoS2 and N sites of M(abt)2 molecules. Therefore, efficient utilization of the large‐area basal plane of MoS2 for HER is achieved based on significantly increased active sites by on‐surface self‐assembly, which is also anticipated to be generally applicable to exploit the basal plane of other transition metal dichalcogenides. In addition, the bottom‐up self‐assembly of molecules onto MoS2 surface can bring other desired properties for multifunctional applications. For example, our results show that on‐MoS2 Co(abt)2 is able to catalyze OER, suggesting that on‐surface self‐assembly can also be used to prepare bifunctional catalysts for overall water splitting. This work provides an effective and facile strategy, on‐surface self‐assembly, to prepare efficient catalysts for water splitting, thereby offering high possibility toward achieving a sustainable energy supply.In summary, we have reported a new and promising avenue (on‐surface self‐assembly) to make full use of the large‐area in‐plane domains of MoS4DFT Calculations: All DFT calculations were carried out through the projector augmented wave method2 substrate. The Brillouin zone was sampled using the Monkhorst–Pack scheme with k‐point mesh of 3 × 3 × 1 for 2D systems and 3 × 3 × 3 for 3D systems. The kinetic energy cutoff for the plane‐wave basis set was set as 500 eV. All structures were fully relaxed until reaching the convergence threshold of 0.02 eV Å−1 for force and 10−4 eV for energy.MD Simulations: All simulations were performed by using the software Gromacs version 42 or Co(abt)2 were constructed by combining all‐atom Amber99sb FF2 crystal (see Table S1). Moreover, the simulated dynamic behavior of Ni(abt)2 in water and diethyl ether, i.e., insoluble in water and soluble in diethyl ether, agrees with the experimental results, further guaranteeing the reliability of the constructed FF parameters. The FF parameters for diethyl ether were built on all‐atom Amber99sb FF and water was described by extended simple point charge model. Molecular partial charges were obtained based on the Chelpg methodology.2 were obtained from ref. 2 interaction from DFT calculations. The size of MoS2 used in simulations is 10.18592 nm × 9.92124 nm consisting of 1152 Mo and 2304 S atoms. The evaporation process of diethyl ether in Figure ΔG Calculations: ΔG is the difference of the free energy (G) between products and reactants and G is calculated asE, EZPE, T, and S represent the energy, the zero‐point energy, the reaction temperature, and the entropy, respectively. The difference of the zero‐point energies is obtained via vibrational frequency calculation for the catalyst with and without adsorbed species. The difference between the entropies of the catalyst with and without adsorbed species is very small and neglected. All calculations were done under the standard hydrogen electrode in which the free energy of protons and electrons (H+ + e−) is taken as a half of the free energy of gas H2. The solvation corrections to account for the effect of water as well as the zero‐point energies and the entropies of gas H2 and liquid H2O were obtained from ref. 2 (GO2(g)) is calculated byGH2(g) represents the free energy of gas H2, GH2O(l) stands for the free energy of liquid H2O, and 4.92 eV is the reaction free energy for splitting liquid H2O into gas H2 and O2.The authors declare no conflict of interest.SupplementaryClick here for additional data file.SupplementaryClick here for additional data file.SupplementaryClick here for additional data file.