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61.
Dr. Lan Hu Prof. Dr. Yan Zhao 《Chemistry (Weinheim an der Bergstrasse, Germany)》2019,25(32):7702-7710
Outcomes of chemical reactions are generally dominated by the intrinsic reactivities of reaction partners, but enzymes frequently override such constraints to transform less reactive molecules in the presence of more reactive ones. Despite the attractiveness of such catalysis, it is difficult to build synthetic catalysts with these features. Micellar imprinting is a powerful method to create template-complementary binding sites inside protein-sized water-soluble nanoparticles. When a photocleavable functional monomer was used to bind two phosphonate/phosphate templates as transition-state analogues, active sites with predetermined size and shape were formed inside doubly cross-linked micelles through molecular imprinting. Postmodification replaced the binding group with a catalytic pyridyl group, forming highly selective artificial esterases. The catalysts displayed enzyme-like kinetics and turnover numbers that were in the hundreds. The selectivity of the catalysts, derived from the substrate-complementary imprinted active sites, enabled transformation of less reactive esters in the presence of more reactive ones. 相似文献
62.
Fatigue damage behavior of a surface-mount electronic package under different cyclic applied loads
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This paper studies and compares the effects of pull-pull and 3-point bending cyclic loadings on the mechanical fa- tigue damage behaviors of a solder joint in a surface-mount electronic package. The comparisons are based on experimental investigations using scanning electron microscopy (SEM) in-situ technology and nonlinear finite element modeling, respec- tively. The compared results indicate that there are different threshold levels of plastic strain for the initial damage of solder joints under two cyclic applied loads; meanwhile, fatigue crack initiation occurs at different locations, and the accumulation of equivalent plastic strain determines the trend and direction of fatigue crack propagation. In addition, simulation results of the fatigue damage process of solder joints considering a constitutive model of damage initiation criteria for ductile materials and damage evolution based on accumulating inelastic hysteresis energy are identical to the experimental results. The actual fatigue life of the solder joint is almost the same and demonstrates that the FE modeling used in this study can provide an accurate prediction of solder joint fatigue failure. 相似文献
63.
Bentolhoda Hadavi Moghadam Akbar Khodaparast Haghi Shohreh Kasaei 《Journal of Macromolecular Science: Physics》2015,54(11):1404-1425
Comparative studies between response surface methodology (RSM) and artificial neural network (ANN) methods to find the effects of electrospinning parameters on the porosity of nanofiber mats is described. The four important electrospinning parameters studied included solution concentration (wt.%), applied voltage (kV), spinning distance (cm) and volume flow rate (mL/h). It was found that the applied voltage and solution concentration are the two critical parameters affecting the porosity of the nanofiber mats. The two approaches were compared for their modeling and optimization capabilities with the modeling capability of RSM showing superiority over ANN, having comparatively lower values of errors. The mean relative error for the RSM and ANN models were 1.97% and 2.62% and the root mean square errors (RMSE) were 1.50 and 1.95, respectively. The superiority of the RSM-based approach is due to its high prediction accuracy and the ability to compute the combined effects of the electrospinning factors on the porosity of the nanofiber mats. 相似文献
64.
This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region. Recommendations for resource managers
- Snow properties, such as snow wetness and snow density are mainly investigated through field‐based survey but rugged terrains, difficult weather conditions, and logistics management issues establish remote sensing as an efficient alternative to monitor snow properties, especially in the mountain environment.
- Although Hyperspectral remote sensing is a powerful tool to conduct the quantitative analysis of the physical properties of snow, only a few studies have used hyperspectral data for the estimation of snow density and wetness in the Himalayan region. This could be because of the lack of synchronized snow properties data with field‐based spectral acquisitions.
- In combination with Hyperspectral image processing, Artificial Neural Network (ANN) can be a useful tool for effective snow modeling because of its ability to capture and represent complex input‐output relationships.
- Further research into understanding the applicability of neural networks to determine snow properties is required to obtain results from large snow cover areas of the Himalayan region.
65.
66.
Mengmeng Ma Ying Wang Nan Gao Xinping Liu Yuhuan Sun Prof. Jinsong Ren Prof. Dr. Xiaogang Qu 《Chemistry (Weinheim an der Bergstrasse, Germany)》2019,25(51):11852-11858
Proteolysis of amyloid-β (Aβ) is a promising approach against Alzheimer's disease. However, it is not feasible to employ natural hydrolases directly because of their cumbersome preparation and purification, poor stability, and hazardous immunogenicity. Therefore, artificial enzymes have been developed as potential alternatives to natural hydrolases. Since specific cleavage sites of Aβ are usually embedded inside the β-sheet structures that restrict access by artificial enzymes, this strongly hinders their efficiency for practical applications. Herein, we construct a NIR (near-IR) controllable artificial metalloprotease (MoS2-Co) using a molybdenum disulfide nanosheet (MoS2) and a cobalt complex of 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (Codota). Evidenced by detailed experimental and theoretical studies, the NIR-enhanced MoS2-Co can circumvent the restriction by simultaneously inhibition of β-sheet formation and destroying β-sheet structures of the preformed Aβ aggregates in living cell. Furthermore, our designed MoS2-Co is an easy to graft Aβ-target agent that prevents misdirected or undesirable hydrolysis reactions, and has been demonstrated to cross the blood brain barrier. This method can be adapted for hydrolysis of other kinds of amyloids. 相似文献
67.
68.
A back propagation artificial neural network (BPANN) prediction model for warpage of injection-molded polypropylene was developed based on an orthogonal design method. The BPANN model was trained by the input and output data obtained from the moldflow software platform simulations. It is proved that the BPANN model can predict the warpage with reasonable accuracy. Utilizing the BPANN model, the effects of the process parameters, packing pressure (Pp), melt temperature (Tme), mold temperature (Tmo), packing time (tp), cooling time (tc), and fill pressure (pf), on the warpage were investigated. The most important process parameter affecting the warpage was Pp, and the second most important was Tme. The rest of the process parameters, Tmo, tp, tc, and pf, were found to be relatively less influential. Warpage increased with elevating Tmo. In contrast, an increase in Pp and Tme caused the warpage to decrease. 相似文献
69.
This paper presents a review of procedural steps and implementation techniques used in the development of artificial intelligence models, generally referred to as artificial neural networks (ANNs), within the water resources domain. It focusses on identifying different areas wherein ANNs have found application thereby elucidating its advantages and disadvantages as well as various challenges encountered in its use. Results from this review provide useful insights into how the performance of ANNs can be improved and potential areas of application that are yet to be explored in hydrological modeling. Recommendations for Resource Managers
- Development of integrated and hybrid artificial intelligent tools is critical to achieving improved forecasts in hydrological modeling studies.
- Further research into comprehending the internal mechanisms of neural networks is required to obtain a practical meaning of each network component deployed to solve real‐world problems.
- More robust optimization techniques and tools like differential evolution, particle swarm optimization and deep neural nets, are yet to be fully explored in the water resources analysis, and should be given more attention to enhance neural networks aptitude for modeling complex and nonlinear hydrological processes.
70.
Caleb J. Hiller Dr. Chi Chung Lee Dr. Martin T. Stiebritz Lee A. Rettberg Prof. Dr. Yilin Hu 《Chemistry (Weinheim an der Bergstrasse, Germany)》2019,25(10):2389-2395
Nitrogenase utilizes an ATP-dependent reductase to deliver electrons to its catalytic component to enable two important reactions: the reduction of N2 to NH4+, and the reduction of CO to hydrocarbons. The two nitrogenase-based reactions parallel the industrial Haber–Bosch and Fischer–Tropsch processes, yet they occur under ambient conditions. As such, understanding the enzymatic mechanism of nitrogenase is crucial for the future development of biomimetic strategies for energy-efficient production of valuable chemical commodities. Mechanistic investigations of nitrogenase has long been hampered by the difficulty to trap substrates and intermediates relevant to the nitrogenase reactions. Recently, we have successfully captured CO on the Azotobacter vinelandii V-nitrogenase via two approaches that alter the electron fluxes in a controlled manner: one approach utilizes an artificial electron donor to trap CO on the catalytic component of V-nitrogenase in the resting state; whereas the other employs a mismatched reductase component to reduce the electron flux through the system and consequently accumulate CO on the catalytic component of V-nitrogenase. Here we summarize the major outcome of these recent studies, which not only clarified the catalytic relevance of the one-CO (lo-CO) and multi-CO (hi-CO) bound states of nitrogenase, but also pointed to a potential competition between N2 and CO for binding to the same pair of reactive Fe sites across the sulfur belt of the cofactor. Together, these results highlight the utility of these strategies in poising the cofactor at a well-defined state for substrate- or intermediate-trapping via controlled alteration of electron fluxes, which could prove beneficial for further elucidation of the mechanistic details of nitrogenase-catalyzed reactions. 相似文献