The paper exploits a unique Chinese municipal dataset to assess the impact of Special Economic Zones on the local economy. Comparing the changes between the municipalities that created a SEZ in ...earlier rounds and those in later waves, I find that the SEZ program increases foreign direct investment not merely through firm relocation, and does not crowd out domestic investment. With dense investment in the targeted municipality the SEZ achieves agglomeration economies and generates wage increases for workers more than the increase in the local cost of living. The effects are heterogeneous: for zones created later the benefits are smaller while the distortions in firm location behavior are larger than those for the early zones. Municipalities with multiple SEZs experience larger effects than those with only one SEZ.
The introduction of oxygen vacancies (Ov) has been regarded as an effective method to enhance the catalytic performance of photoanodes in oxygen evolution reaction (OER). However, their stability ...under highly oxidizing environment is questionable but was rarely studied. Herein, NiFe‐metal–organic framework (NiFe‐MOFs) was conformally coated on oxygen‐vacancy‐rich BiVO4 (Ov‐BiVO4) as the protective layer and cocatalyst, forming a core–shell structure with caffeic acid as bridging agent. The as‐synthesized Ov‐BiVO4@NiFe‐MOFs exhibits enhanced stability and a remarkable photocurrent density of 5.3±0.15 mA cm−2 at 1.23 V (vs. RHE). The reduced coordination number of Ni(Fe)‐O and elevated valence state of Ni(Fe) in NiFe‐MOFs layer greatly bolster OER, and the shifting of oxygen evolution sites from Ov‐BiVO4 to NiFe‐MOFs promotes Ov stabilization. Ovs can be effectively preserved by the coating of a thin NiFe‐MOFs layer, leading to a photoanode of enhanced photocurrent and stability.
A core–shell Ov‐BiVO4@NiFe‐MOFs photoanode was constructed via a coordination‐assisted self‐assembly method. A NiFe‐MOFs thin layer acts as protective layer and cocatalyst to shift active sites from oxygen vacancies to NiFe‐MOFs, leading to improved stability and activity for OER. This molecular‐based approach tailors the coordination and electronic structure of active sites and provides mechanistic insights for rational design of photocatalysts.
Chemical structure of small molecule acceptors determines their performance in organic solar cells. Multiscale simulations are necessary to avoid trial‐and‐error based design, ultimately to save time ...and resources. In current study, the effect of sp2‐hybridized nitrogen substitution at the inner or the outmost position of central core, side chain, and terminal group of small molecule acceptors is investigated using multiscale computational modelling. Quantum chemical analysis is used to study the electronic behavior. Nitrogen substitution at end‐capping has significantly decreased the electron‐reorganization energy. No big change is observed in transfer integral and excited state behavior. However, nitrogen substitution at terminal group position is good way to improve electron‐mobility. Power conversion efficiency (PCE) of newly designed acceptors is predicted using machine learning. Molecular dynamics simulations are also performed to explore the dynamics of acceptor and their blends with PBDB‐T polymer donor. Florgy‐Huggins parameter is calculated to study the mixing of designed small molecule acceptors with PBDB‐T. Radial distribution function has indicated that PBDB‐T has a closer packing with N3 and N4. From all analysis, it is found that nitrogen substitution at end‐capping group is a better strategy to design efficient small molecule acceptors.
Multidimensional modelling is performed to envision the structural changes at atomic and molecular level. sp2‐hybridized nitrogen is substituted at the inner or the outmost position of central core, side chain, and terminal group of ITIC for improving the device performance. It is found that nitrogen substitution at end‐capping group is a better strategy to design efficient small molecule acceptors.
A near infrared (NIR) optical biosensor based on peptide functionalized single-walled carbon nanotubes (SWCNTs) hybrids for 2,4,6-trinitrotoluene (TNT) explosive detection was developed. The TNT ...binding peptide was directly anchored on the sidewall of the SWCNTs using the π-π interaction between the aromatic amino acids and SWCNTs, forming the peptide-SWCNTs hybrids for near infrared absorption spectra measurement. The evidence of the morphology of peptide-SWCNTs hybrids was obtained using atomic force microscopy (AFM). The results demonstrated that peptide-SWCNTs hybrids based NIR optical biosensor exhibited sensitive and highly selective for TNT explosive determination, addressing a promising optical biosensor for security application.
In recent years, a rapid evolution of organic solar cells (OSCs) has been achieved by virtue of structural design of active layer materials and optimization of film morphology. Along with other ...characterization techniques, grazing incidence small‐ and wide‐angle X‐ray scattering (GISAXS and GIWAXS) have played significant role in deeper understanding of film morphology. Herein, the importance of these techniques is explained with examples from various aspects of OSCs. Different pre‐ and post‐processing conditions such as solvent effect, solvent additive, solvent, and thermal annealing are studied in the framework of these techniques. Moreover, the impact of donor:acceptor ratio and molecular weight of semiconductor on microstructure is also explored. Finally, the effect of chemical structure of organic semiconductors (both polymers and small molecules) on the film morphology is discussed. These techniques provide valuable information about crystallinity, phase separation, and domain size of nanostructured film morphology, which helps to optimize the film morphology and enhances the performance of OSCs. The role of these techniques will become more important as the mystery of film morphology still has to be solved.
Grazing incidence small‐ and wide‐angle X‐ray scattering (GISAXS and GIWAXS) are extensively used for the characterization of film morphology of organic solar cells (OSCs). Herein, the use of these techniques to find the effect of chemistry of active layer materials and different pre‐ and postprocessing conditions on the film morphology of OSCs is discussed.
As low‐dimensional lead‐free hybrids with higher stability and lower toxicity than those of three‐dimensional lead perovskites, organic antimony(III) halides show great application potential in ...opt‐electronic field owing to diverse topologies along with exceptional optical properties. We report herein an antimony(III) hybrid (MePPh3)2SbCl5 with a zero‐dimensional (0D) structure, which exhibits brilliant orange emission peaked at 593 nm with near‐unity photoluminescent quantum yield (99.4 %). The characterization of photophysical properties demonstrates that the broadband emission with a microsecond lifetime (3.24 μs) arises from self‐trapped emission (STE). Electrically driven organic light‐emitting diodes (OLEDs) based on neat and doped films of (MePPh3)2SbCl5 were fabricated. The doped devices show significant improvement in comparison to non‐doped OLEDs. Owing to the much improved surface morphology and balanced carrier transport in light‐emitting layers of doped devices, the peak luminance, current efficiency (CE) and external quantum efficiency (EQE) are boosted from 82 cd m−2 to 3500 cd m−2, 1.1 cd A−1 to 6.8 cd A−1, and 0.7 % to 3.1 % relative to non‐doped devices, respectively.
A highly luminescent organic antimony(III) hybrid (MePPh3)2SbCl5 featured with STE emission is prepared with good reproducibility and high stability. High‐efficiency OLEDs are demonstrated with this hybrid as an emitter with the luminance of 3500 cd m−2, current efficiency of 6.8 cd A−1 and EQE of 3.1 %, respectively.
Tumor microenvironment (TME)‐responsive nanozyme‐catalyzed cancer therapy shows great potential due to its specificity and efficiency. However, breaking the self‐adaption of tumors and improving the ...sustainable remodeling TME ability remains a major challenge for developing novel nanozymes. Here, a rapid method is developed first to synthesize unprecedented trimetalic nanozyme (AuMnCu, AMC) with a targeting peptide (AMCc), which exhibits excellent peroxidase‐like, catalase‐like, and glucose oxidase‐like activities. The released Cu and Mn ions in TME consume endogenous H2O2 and produce O2, while the AMCccatalyzes glucose oxidation reaction to generate H2O2 and gluconic acid, which achieves the starvation therapy by depleting the energy and enhances the chemodynamic therapy effect by lowering the pH of the TME and producing extra H2O2. Meanwhile, the reactive oxygen species damage is amplified, as AMCc can constantly oxidize intracellular reductive glutathione through the cyclic valence alternation of Cu and Mn ions, and the generated Cu+ elevate the production of ·OH from H2O2. Further studies depict that the well‐designed AMCc exhibits the excellent photothermal performance and achieves TME‐responsive sustainable starvation/photothermal‐enhanced chemodynamic synergistic effects in vitro and in vivo. Overall, a promising approach is demonstrated here to design “all‐in‐one” nanozyme for theranostics by remodeling the TME.
A rapid and facile method to synthesize acidity‐responsive trimetallic nanozyme (AMCc) under the combined action of tannic acid and ascorbic acid is attempted first. The unprecedented nanozyme AMCc with excellent peroxidase‐like, catalase‐like, and glucose oxidase‐like activity can be used for glutathione depletion and substrates self‐supply and achieve self‐replenishing and self‐reinforcing cascaded catalytic starvation/photothermal‐enhanced chemodynamic synergistic therapy via tumor microenvironment remodulation.
Since the full implementation of the innovation-driven development strategy in the country, all regions and departments have placed great emphasis on the work of mass entrepreneurship and innovation, ...resulting in a significant increase in innovation and entrepreneurship. The national focus on mass entrepreneurship and innovation has shifted from quantitative to qualitative improvements, with greater attention being paid to improving substance rather than expanding scope. There is a growing trend among colleges to recognize innovation and entrepreneurial management as a significant achievement in college education and teaching. By prioritizing innovation and entrepreneurship management and professional education, the aim is to cultivate college students with an entrepreneurial spirit, and to develop inventive and entrepreneurial talent. Cultivating innovative talent is one way to apply the innovation-driven development plan and build a creative country. However, the implementation of the innovation and entrepreneurship development plan in the country has posed both new opportunities and challenges to the management of innovation and entrepreneurship education in colleges across the country. As a result, innovation and entrepreneurship have become increasingly important tasks that can help improve the management of innovation and entrepreneurship in these institutions. In order to address this issue, this paper proposes a neural network for evaluating innovation and entrepreneurship management methods in colleges, which combines convolutional neural networks with these tasks. Specifically, a multi-scale convolutional neural network is designed to more efficiently extract innovation and entrepreneurship management features in colleges, ultimately leading to improved model performance.
Solar energy‐driven conversion of CO2 into fuels with H2O as a sacrificial agent is a challenging research field in photosynthesis. Herein, a series of crystalline porphyrin‐tetrathiafulvalene ...covalent organic frameworks (COFs) are synthesized and used as photocatalysts for reducing CO2 with H2O, in the absence of additional photosensitizer, sacrificial agents, and noble metal co‐catalysts. The effective photogenerated electrons transfer from tetrathiafulvalene to porphyrin by covalent bonding, resulting in the separated electrons and holes, respectively, for CO2 reduction and H2O oxidation. By adjusting the band structures of TTCOFs, TTCOF‐Zn achieved the highest photocatalytic CO production of 12.33 μmol with circa 100 % selectivity, along with H2O oxidation to O2. Furthermore, DFT calculations combined with a crystal structure model confirmed the structure–function relationship. Our work provides a new sight for designing more efficient artificial crystalline photocatalysts.
COF catalysts: A series of crystalline covalent organic frameworks (COFs) was designed and applied for CO2 photoreduction coupled with H2O photooxidation, in the absence of photosensitizers and sacrificial agents. This approach gives a more straightforward and clear understanding of the structure–function relationship of artificial photosynthesis.
Artificial intelligence, which can provide cognitive services in mobile edge cloud computing to improve the user’s quality of experience, has attracted significant attention. Edge computing can ...replace local computing to acquire low-latency quality of service in 5th-generation networks. Offloading is one of the key techniques in the 5th-generation network mobile edge cloud computing environment. Big data and computationally intensive applications may suffer from inevitable delays caused by resource homogenization competition and resource switching among different user environments. Affinity, which can help to mitigate resource homogenization competition and reduce switching, is a contextual trait that exists widely between data and between tasks. Due to the mobility of terminals and erratic service requests, deep reinforcement learning has been introduced to learn the offloading strategy for offloading offline affinity tasks in a dynamic environment. The conflict between resource demand fluctuations and fixed resource provisioning yields discontent over the edge resource utilization constraint. In our approach, we develop a long-short term memory and dynamic Bayesian network-based algorithm to predict the periodic throughput and computational capacity data. The distributed unit device in the 5th-generation network is opened up/called back accordingly to relax the edge resource constraint and save energy. We combine deep learning and deep reinforcement learning to provide cognitive services for affinity task offloading and dynamic resource adjustment. A thorough evaluation of the compared algorithms is conducted, and the results show that our approach can help reduce the makespan and costs for big data and computationally intensive applications by 36.3 − 48.2
%
.
