Highly selective supported gold catalyst for CO-driven reduction of furfural in aqueous media

The reductive transformation of furfural （FAL） into furfuryl alcohol （FOL） is an attractive route for the use of renewable bio‐sources but it suffers from the heavy use of H2. We describe here a highly efficient reduction protocol for converting aqueous FAL to FOL. A single phase rutile TiO2 support with a gold catalyst （Au/TiO2‐R） that used CO/H2O as the hydrogen source catalyze this reduction efficiently under mild conditions. By eliminating the consumption of fossil fuel‐derived H2, our pro‐cess has the benefit afforded by using CO as a convenient and cost competitive reducing reagent.

Imbalanced Data Classification Using SVM Based on Improved Simulated Annealing Featuring Synthetic Data Generation and Reduction

Imbalanced data classification is one of the major problems in machine learning.This imbalanced dataset typically has significant differences in the number of data samples between its classes.In most cases,the performance of the machine learning algorithm such as Support Vector Machine(SVM)is affected when dealing with an imbalanced dataset.The classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class samples.In this paper,a hybrid approach combining data pre-processing technique andSVMalgorithm based on improved Simulated Annealing(SA)was proposed.Firstly,the data preprocessing technique which primarily aims at solving the resampling strategy of handling imbalanced datasets was proposed.In this technique,the data were first synthetically generated to equalize the number of samples between classes and followed by a reduction step to remove redundancy and duplicated data.Next is the training of a balanced dataset using SVM.Since this algorithm requires an iterative process to search for the best penalty parameter during training,an improved SA algorithm was proposed for this task.In this proposed improvement,a new acceptance criterion for the solution to be accepted in the SA algorithm was introduced to enhance the accuracy of the optimization process.Experimental works based on ten publicly available imbalanced datasets have demonstrated higher accuracy in the classification tasks using the proposed approach in comparison with the conventional implementation of SVM.Registering at an average of 89.65%of accuracy for the binary class classification has demonstrated the good performance of the proposed works.

Disaster Reduction Decision Support System Against Debris Flows and Landslides Along Highway in Mountainous Area

Highways in mountainous areas are easy to be damaged by such natural disasters as debris flows and landslides and disaster reduction decision support system (DRDSS) is one of the important means to mitigate these disasters. Guided by the theories and technologies of debris flow and landslide reduction and supported by geographical information system (GIS), remote sensing and database techniques, a DRDSS against debris flow and landslide along highways in mountainous areas has been established on the basis of such principles as pertinence, systematicness, effectiveness, easy to use, open and expandability. The system consists of database, disaster analysis models and decisions on reduction of debris flows and landslides, mainly functioning to zone disaster dangerous degree, analyze debris flow activity, simulate debris flow deposition and diffusion, analyze landslide stability, select optimal highway renovation scheme and plan disaster prevention and control engineering. This system has been applied successfully to the debris flow and landslide treatment works along Palongzangbu Section of Sichuan-Tibet Highway.

One-pot synthesis of poly vinyl alcohol(PVA) supported silver nanoparticles and its efficiency in catalytic reduction of methylene blue

Stable silver nanoparticles were synthesized using polyvinyl alcohol （PVA） as reducing and capping agent. The method of steric stabilization was adopted for the incorporation of silver nanoparticles in the polymer matrix. The successful incorporation of silver nanoparticles in a PVA matrix was confirmed by UV–Visible spectroscopy, transmission electron microscopy （TEM） and Fourier transform infrared （FT-IR） spectroscopy. The synthesized silver nanoparticles were characterized by a peak at 426 nm in the UV–Vis spectrum. TEM studies showed the formation of spherical shaped silver nanoparticles of 10-13 nm, following the reduction by UV irradiation. Catalytic properties were studied by means of UV-Visible spectroscopic analysis. The synthesized silver nanoparticles exhibited good catalytic properties in the reduction of methylene blue.

Tuning precise numbers of supported nickel clusters on graphdiyne for efficient CO_(2)electroreduction toward various multi-carbon products

Compared to single-atom catalysts,supported metal clusters can exhibit enhanced activity and designated selectivity in heterogeneous catalysis due to their unique geometric and electronic features.Herein,by means of comprehensive density functional theory (DFT) computations,we systematically investigated the potential of several Ni clusters supported on graphdiyne (Ni_(x)/GDY,x=1–6) for CO_(2) reduction reaction (CO_(2)RR).Our results revealed that,due to the strong interaction between Ni atoms and sp-hybridized C atoms,these supported Ni clusters on GDY exhibit high stabilities and excellent electronic properties.In particular,according to the computed free energy profiles for CO_(2)RR on these Ni_(x)/GDY systems,the anchored Ni_(4) cluster was revealed to exhibit high CO_(2)RR catalytic activity with a small limiting potential and moderate kinetic barrier for C–C coupling,and CH_(4),C_(2)H_(5)OH,and C_(3)H_(7)OH were identified as the main products,which can be attributed to its strong capacity for CO_(2) activation due to its unique configuration and excellent electronic properties.Thus,by carefully controlling the precise numbers of atoms in sub-nano clusters,the spatially confined Ni clusters can perform as promising CO_(2)RR catalysts with high-efficiency and high-selectivity,which may provide a useful guidance to further develop novel and low-cost metal clusters-based catalysts for sustain CO_(2)conversion to valuable chemicals and fuels.

Magneli phase titanium sub-oxide conductive ceramic Ti_nO_(2n-1) as support for electrocatalyst toward oxygen reduction reaction with high activity and stability

Magneli phase titanium sub-oxide conductive ceramic Tin O2n-1 was used as the support for Pt due to its excellent resistance to electrochemical oxidation, and Pt/Tin O2n-1 composites were prepared by the impregnation-reduction method. The electrochemical stability of Tin O2n-1 was investigated and the results show almost no change in the redox region after oxidation for 20 h at 1.2 V(vs NHE) in 0.5 mol/L H2SO4 aqueous solution. The catalytic activity and stability of the Pt/Tin O2n-1 toward the oxygen reduction reaction(ORR) in 0.5 mol/L H2SO4 solution were investigated through the accelerated aging tests(AAT), and the morphology of the catalysts before and after the AAT was observed by transmission electron microscopy. At the potential of 0.55 V(vs SCE), the specific kinetic current density of the ORR on the Pt/Tin O2n-1 is about 1.5 times that of the Pt/C. The LSV curves for the Pt/C shift negatively obviously with the half-wave potential shifting about 0.02 V after 8000 cycles AAT, while no obvious change takes place for the LSV curves for the Pt/Tin O2n-1. The Pt particles supported on the carbon aggregate obviously, while the morphology of the Pt supported on Tin O2n-1 remains almost unchanged, which contributes to the electrochemical surface area loss of Pt/C being about 2times that of the Pt/Tin O2n-1. The superior catalytic stability of Pt/Tin O2n-1 toward the ORR could be attributed to the excellent stability of the Tin O2n-1 and the electronic interaction between the metals and the support.

Critical role of carbon support in metal nanoaggregate facilitating Fe-N-C catalyst for PEM fuel cell application

Metal nanoaggregates can simultaneously enhance the activity and stability of Fe-N-C catalysts in proton-exchange-membrane fuel cells(PEMFC).Previous studies on the relevant mechanism have focused on the direct interaction between FeN_(4)active sites and metal nanoaggregates.However,the role of carbon support that hosts metal nanoaggregates and active sites has been overlooked.Here,a Fe-N-C catalyst encapsulating inactive gold nanoparticles is prepared as a model catalyst to investigate the electronic tuning of Au nanoparticles(NPs)towards the carbon support.Au NPs donate electrons to carbon support,making it rich inπelectrons,which reduces the work function and regulates the electronic configuration of the FeN_(4)sites for an enhanced ORR activity.Meanwhile,the electron-rich carbon support can mitigate the electron depletion of FeN_(4)sites caused by carbon support oxidation,thereby preserving its high activity.The yield and accumulation of H_(2)O_(2)are thus alleviated,which delays the oxidation of the catalyst and benefits the stability.Due to the electron-rich carbon support,the composite catalyst achieves a top-level peak power density of 0.74 W/cm^(2) in a 1.5 bar H_(2)-air PEMFC,as well as the improved stability.This work elucidates the key role of carbon support in the performance enhancement of the FeN-C/metal nanoaggregate composite catalysts for fuel cell application.

Effect of Reduction Temperature on Hydrofining Performance of Supported Molybdenum Phosphide Catalyst

A series of supported molybdenum phosphide catalysts were prepared by impregnation method. XRD, TG-DTG, XPS and BET were used to study the phase, compositions and surface areas of the prepared catalysts. A model reactant containing thiophene, pyridine and cyclohexene was used for the measurements of catalytic activities. The effect of reduction temperature on catalytic activities was investigated. The analysis results by XRD and BET are very different when the reduction temperature is changed from 400 to 900 ℃. MoP/γ-Al2O3 catalysts and CoMoP/γ-Al2O3 catalysts prepared at the reduction temperature of 500 ℃ are the most active ones.

Reduction Aid in Proximal Femoral Fractures: The Thigh Support

Reducing the Unstable Intertrochanteric Fractures in a closed manner is challenging especially in the old and aged people with co morbidities. We provide a simple reducing aid to achieve the fracture reduction. This will protect your surgical resident’s or operating theatre practitioner’s back in maintaining the reduction throughout the procedure and continuing with the procedure of cephalomedullary nailing. It will also facilitate in the better rehabilitation of the patient with minimal morbidity and offer a good radiographic view.

NOISE REDUCTION FOR FAST FADING CHANNEL BY RECURRENT LEAST SQUARES SUPPORT VECTOR MACHINES IN EMBEDDING PHASE SPACES

A new strategy for noise reduction of fast fading channel is presented. Firstly, more information is acquired utilizing the reconstructed embedding phase space. Then, based on the Recurrent Least Squares Sup-port Vector Machines (RLS-SVM), noise reduction of the fast fading channel is realized. This filtering tech-nique does not make use of the spectral contents of the signal. Based on the stability and the fractal of the cha-otic attractor, the RLS-SVM algorithm is a better candidate for the nonlinear time series noise-reduction. The simulation results shows that better noise-reduction performance is acquired when the signal to noise ratio is 12dB.

New Horizon in stabilization of single atoms on metal-oxide supports for CO_(2) reduction

Elecrochemical CO_(2) reduction is a promising route to convert CO_(2) into the value added chemicals,and to control the increasing accumulation of 002 in the atmosphere.The CO_(2) molecule is stable and chemically inert,so a highly efficient electrocatalyst is required for CO_(2) reduction.The single atoms with metal oxdde support is a new fronder in CO_(2) reduction.The supported single atoms contain disperse isolated atoms with an appropriate support maxcimize the atom efficiency of metals for boosting the catalytc perfomance.In this review,we had discussed state-of-the-art research on the synthestis of single atoms supported on the metal oxdde for boosting catalytic activity for CO_(2) reduction and special emphasis is placed on the influence of single atoms supported on metal oxdde on the CO_(2) reduction.Futhermore,we had discussed the challenges,and opportunities for paving the development of single atoms with metal support and their application in ecrocatalytic CO_(2) reduction.

DIMENSIONALITY REDUCTION BASED ON SVM AND LDA,AND ITS APPLICATION TO CLASSIFICATION TECHNIQUE

Some dimensionality reduction （DR） approaches based on support vector machine （SVM） are proposed. But the acquirement of the projection matrix in these approaches only considers the between-class margin based on SVM while ignoring the within-class information in data. This paper presents a new DR approach, call- ed the dimensionality reduction based on SVM and LDA （DRSL）. DRSL considers the between-class margins from SVM and LDA, and the within-class compactness from LDA to obtain the projection matrix. As a result, DRSL can realize the combination of the between-class and within-class information and fit the between-class and within-class structures in data. Hence, the obtained projection matrix increases the generalization ability of subsequent classification techniques. Experiments applied to classification techniques show the effectiveness of the proposed method.

Progress and Outlook of Carbon-supported Single-atom Electrocatalyst for Oxygen Reduction Reaction

Single-atom catalysts (SACs) have garnered extensive attention in the field of catalysis due to their exceptional inherent reaction activity, optimal utilization of metal atoms, etc. Controlled synthesis plays a crucial role in elucidating the structure-activity relationship of SACs. This paper reviews various synthetic strategies for SACs, encompassing defect engineering, metal-organic frameworks (MOFs) pyrolysis, and ion exchange. With specific examples, the significance of constructing catalysts at the atomic level is discussed, aiming to comprehensively understand the synthetic strategies of SACs. Finally, it addresses the challenges and prospects associated with controlled synthesis techniques for SACs as well as their future applications.

Strong support effect induced by MXene for the synthesis of metal sulfides nanosheet arrays with sulfur vacancies towards selective CO_(2)-to-CO photoreduction

Selective CO_(2)-to-CO photoreduction is under intensive research and requires photocatalysts with tuned microstructures to accelerate the reaction kinetics.Here,we report CuInS_(2)nanosheet arrays with sulfur vacancies(VS)grown on the two-dimensional(2D)support of Ti_(3)C_(2)T_(x)MXene for CO_(2)-to-CO photoreduction.Our results reveal that the use of Ti_(3)C_(2)T_(x)induces strong support effect,which causes the hierarchical nanosheet arrays growth of CuInS_(2)and simultaneously leads to charge transfer from CuInS_(2)to Ti_(3)C_(2)T_(x)support,resulting in VSformed in CuInS_(2).The strong support effect based on Ti_(3)C_(2)T_(x)is proven to be applicable to prepare a series of different metal indium sulfide arrays with VS.CuInS_(2)nanosheet arrays with VSsupported on Ti_(3)C_(2)T_(x)benefit the photocatalytic selective reduction of CO_(2)to CO,manifesting a remarkable over 14.8-fold activity enhancement compared with pure CuInS_(2).The experimental and computational investigations pinpoint that VSof CuInS_(2)resulting from the support effect of Ti_(3)C_(2)T_(x)lowers the barrier of the rate-limiting step of^(*)COOH→^(*)OH+^(*)CO,which is the key to the photoactivity enhancement.This work demonstrates MXene support effects and offers an effective approach to regulate the atomic microstructure of metal sulfides toward enhancing photocatalytic performance.

Improving the electrocatalytic performances of Pt-based catalysts for oxygen reduction reaction via strong interactions with single-CoN_(4)-rich carbon support

Developing platinum-group-metal(PGM)catalysts possessing strong metal-support interaction and controllable PGM size is urgent for the sluggish oxygen reduction reaction(ORR)in proton-exchange membrane fuel cells.Herein,we propose an in-situ self-assembled reduction strategy to successfully induce highly-dispersed sub-3nm platinum nanoparticles(Pt NPs)to attach on resin-derived atomic Co coordinated by N-doped carbon substrate(Pt/Co_(SA)-N-C)for ORR.To be specific,the interfacial electron interaction effect,along with a highly robust Co_(SA)-N-C support endow the as-fabricated Pt/Co_(SA)-N-C catalyst with significantly enhanced catalytic properties,i.e.,a mass activity(MA)of 0.719 A/mgPt at 0.9 ViR-free and a reduction of 24.2%in MA after a 20,000-cycles test.Density functional theory(DFT)calculations demonstrate that the enhanced electron interaction between Pt and Co_(SA)-N-C support decreases the dband center of Pt,which is in favor of lowering the desorption energy of ^(*)OH on Pt/Co_(SA)-N-C surface and accelerating the formation of H_(2)O,thus enhance the instinct activity of ORR.Furthermore,the higher binding energy between Pt and Co_(SA)-N-C compared to Pt and C indicates that the migration of Pt has been suppressed,which theoretically explains the improved durability of Pt/Co_(SA)-N-C.Our work offers an enlightenment on constructing composite Pt-based catalysts with multiple active sites.

A case study of multi-seam coal mine entry stability analysis with strength reduction method

In this paper,the advantage of using numerical models with the strength reduction method(SRM) to evaluate entry stability in complex multiple-seam conditions is demonstrated.A coal mine under variable topography from the Central Appalachian region is used as a case study.At this mine,unexpected roof conditions were encountered during development below previously mined panels.Stress mapping and observation of ground conditions were used to quantify the success of entry support systems in three room-and-pillar panels.Numerical model analyses were initially conducted to estimate the stresses induced by the multiple-seam mining at the locations of the affected entries.The SRM was used to quantify the stability factor of the supported roof of the entries at selected locations.The SRM-calculated stability factors were compared with observations made during the site visits,and the results demonstrate that the SRM adequately identifies the unexpected roof conditions in this complex case.It is concluded that the SRM can be used to effectively evaluate the likely success of roof supports and the stability condition of entries in coal mines.

Instance reduction for supervised learning using input-output clustering method

A method that applies clustering technique to reduce the number of samples of large data sets using input-output clustering is proposed.The proposed method clusters the output data into groups and clusters the input data in accordance with the groups of output data.Then,a set of prototypes are selected from the clustered input data.The inessential data can be ultimately discarded from the data set.The proposed method can reduce the effect from outliers because only the prototypes are used.This method is applied to reduce the data set in regression problems.Two standard synthetic data sets and three standard real-world data sets are used for evaluation.The root-mean-square errors are compared from support vector regression models trained with the original data sets and the corresponding instance-reduced data sets.From the experiments,the proposed method provides good results on the reduction and the reconstruction of the standard synthetic and real-world data sets.The numbers of instances of the synthetic data sets are decreased by 25%-69%.The reduction rates for the real-world data sets of the automobile miles per gallon and the 1990 census in CA are 46% and 57%,respectively.The reduction rate of 96% is very good for the electrocardiogram(ECG) data set because of the redundant and periodic nature of ECG signals.For all of the data sets,the regression results are similar to those from the corresponding original data sets.Therefore,the regression performance of the proposed method is good while only a fraction of the data is needed in the training process.

Carbon Reduction Policies: A Regional Comparison of Their Contributions to CO_2 Abatement in Six Carbon Trading Pilot Schemes in China

The contributions of carbon reduction policies were evaluated and compared for six carbon trading pilot schemes in China, in four municipalities(Beijing, Shanghai, Tianjin, and Chongqing) and two provinces(Guangdong and Hubei). The carbon emissions accounting method of the Intergovernmental Panel on Climate Change was used to calculate the actual CO2 and the support vector machine model was used to predict CO2. Chinese carbon reduction policies abated CO2 in the six carbon trading pilot schemes after the comprehensive policies came into force. However, the contribution of policies to CO2 abatement varied among regions, and the effect of carbon reduction policy on municipality pilot schemes was greater than on provincial pilot schemes. The largest contribution of carbon reduction policy to CO2 abatement was 28.3%, for the pilot carbon trading scheme in Beijing, and the smallest contribution was 3.7%, for that in Hubei. It is crucial to consider "carbon leakage" and a carbon trading linking program in order to evaluate the effects of carbon reduction policies.

Suppressing byproduct formation for high selective CO_(2) reduction over optimized Ni/TiO_(2) based catalysts

One of the challenges for catalytic CO_(2)reduction is to control product selectivity,and new findings that can modify selectivity would be transformative.Herein,two kinds of TiO_(2)(homemade and commercial)with the same crystal phase but different surface properties are chosen as supports to prepare Ni-based catalysts for CO_(2)reduction,which show distinctly different product selectivity for CO_(2)reduction to CH_(4) or CO,as well as the CO_(2)conversion.The catalysts based on the homemade TiO_(2)support are highly selective for CH_(4) formation,while the latter ones are about 100%selective for CO formation under the same reaction conditions.In addition,the former ones are much active(more than 3 times)than the latter ones.We found that the collaborative contribution of Ti^(3+)and Ni^(2+)species and the electronic metal-support interactions effect maybe the main driving force behind for determining the product selectivity.Methane is almost exclusively produced over the catalysts with abundant Ti^(3+)and Ni^(2+)species and greater electronic metal-support interaction,otherwise,it will give priority to CO generation.The addition of CeO_(2)can reduce the Ni particle size and improve the dispersion of Ni nanoparticles,as well as create more Ti^(3+)species,contributing to the enhancement of CO_(2)conversion,but shows a negligible effect on product selectivity.Furthermore,the in situ DRIFT experiments and kinetic experiments indicate that the CO route is probably involved in the CO_(2)reduction process over the homemade Ni-CeO_(2)/TiO_(2)-CO catalyst with abundant Ti^(3+)and Ni^(2+)species and a strong electronic transform effect.

Critical Evaluation of Linear Dimensionality Reduction Techniques for Cardiac Arrhythmia Classification

Embedding the original high dimensional data in a low dimensional space helps to overcome the curse of dimensionality and removes noise. The aim of this work is to evaluate the performance of three different linear dimensionality reduction techniques (DR) techniques namely principal component analysis (PCA), multi dimensional scaling (MDS) and linear discriminant analysis (LDA) on classification of cardiac arrhythmias using probabilistic neural network classifier (PNN). The design phase of classification model comprises of the following stages: preprocessing of the cardiac signal by eliminating detail coefficients that contain noise, feature extraction through daubechies wavelet transform, dimensionality reduction through linear DR techniques specified, and arrhythmia classification using PNN. Linear dimensionality reduction techniques have simple geometric representations and simple computational properties. Entire MIT-BIH arrhythmia database is used for experimentation. The experimental results demonstrates that combination of PNN classifier (spread parameter, σ = 0.08) and PCA DR technique exhibits highest sensitivity and F score of 78.84% and 78.82% respectively with a minimum of 8 dimensions.