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.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Machine learning (ML) is a field of computer science that uses algorithms and techniques for automating solutions to complex problems that are hard to program using conventional programming methods. ...Owing to the chemical versatility of organic building blocks, a large number of organic semi-conductors have been used for organic solar cells. Selecting a suitable organic semi-conductor is like searching for a needle in a haystack. Data-driven science, the fourth paradigm of science, has the potential to guide experimentalists to discover and develop new high-performance materials. The last decade has seen impressive progress in materials informatics and data science; however, data-driven molecular design of organic solar cell materials is still challenging. The data-analysis capability of machine learning methods is well known. This review is written about the use of machine learning methods for organic solar cell research. In this review, we have outlined the basics of machine learning and common procedures for applying machine learning. A brief introduction on different classes of machine learning algorithms as well as related software and tools is provided. Then, the current research status of machine learning in organic solar cells is reviewed. We have discussed the challenges in anticipating the data driven material design, such as the complexity metric of organic solar cells, diversity of chemical structures and necessary programming ability. We have also proposed some suggestions that can enhance the usefulness of machine learning for organic solar cell research enterprises.
In this review, current research status about the machine learning use in organic solar cell research is reviewed. We have discussed the challenges in anticipating the data driven material design.
The power conversion efficiency (PCE) of organic solar cells (OSCs) is increasing continuously, however, commercialization is far from being achieved due to the very high synthetic cost of materials ...and toxic solvents. Poly(3-hexylthiophene) (P3HT) is the cheapest donor, however, its PCE has remained relatively low for a long time. Recently, a PCE of over 9% has been reported. This study was performed with the aim of predicting the performance of P3HT based organic solar cells through statistical data fit, bypassing the complexity of OSC devices. For this purpose, we have used machine learning to explore data from previously reported devices. Molecular descriptors were used to train machine learning models. We have identified the descriptors that have a positive impact on the PCE. Various machine learning models are used to classify non-fullerene acceptors (NFAs) on the basis of their PCEs. We have also developed various regression models to predict the PCE. The support vector machine showed the best predictive capability. Machine learning models were also trained to predict the energy levels. Over 3000 NFAs were designed using high performing and easily synthesizable building blocks. For virtual screening of new molecules, energy levels were predicted through the already trained model. Acceptors with suitable energy levels matching with those of P3HT were selected. The PCEs of these selected acceptors were also predicted. 87 acceptors with >7.5% PCE were selected. Green solvents were selected on the basis of Hansen solubility parameters predicted using machine learning. Green solvent-containing organic solar cells have better scope of commercialization due to their environment-friendly nature. This study will pave the way for the cheap and fast design of materials for efficient P3HT-based organic solar cells. This study will help to select potential candidates and speed up the breakthroughs.
A time and money efficient machine learning assisted design of non-fullerene small molecule acceptors for P3HT based organic solar cells is reported. Green solvents are also selected using machine learning predicted Hansen solubility parameters.
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.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Solubility plays a critical role in many aspects of research (drugs to materials). Solubility parameters are very useful for selecting appropriate solvents/non-solvents for various applications. In ...the present study, Hansen solubility parameters are predicted using machine learning. More than 40 machine models are tried in the search for the best model. Molecular descriptors and fingerprints are used as inputs to get a comparative view. Machine learning models trained using molecular descriptors have shown higher prediction ability than the model trained using molecular fingerprints. Machine learning models trained using molecular descriptors have shown their potential to be easy and fast models compared to the density functional theory (DFT)/thermodynamic approach. Machine learning creates a "black box" connection to the properties. Therefore, minimal computational cost is required. With the help of the best-trained machine learning model, green solvents are selected for small molecule donors that are used in organic solar cells. Our introduced framework can help to select solvents for organic solar cells in an easy and fast way.
A fast machine learning based framework is introduced for the prediction of solubility parameters and selection of green solvents for small molecular donor-based organic solar cells.
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.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Organic solar cells are the most promising candidates for future commercialization. This goal can be quickly achieved by designing new materials and predicting their performance without ...experimentation to reduce the number of potential targets. We introduce a multidimensional design and discovery pipeline to systematize materials discovery and reduce the dependence on a serendipitous approach. Machine learning models are trained on data collected from the literature for the prediction of various properties, such energy levels (HOMO and LUMO), UV/visible absorption maxima (both in solution and film) and power conversion efficiency (PCE). More than 5000 new small molecule acceptors (SMAs) are designed using easily synthesizable building blocks. 1700 small molecule acceptors without a suitable energy level match with PBT7-Th are filtered off. SMAs with blue-shifted absorption maxima are not further considered. The number of SMAs was reduced to 2350 on the basis of the predicted UV/visible absorption maxima. Then, the SMAs were further screened on the basis of the predicted power conversion efficiency (PCE). More than 100 SMAs with PCE >13% were selected and further studied using molecular dynamics (MD) simulations. The mixing behavior of the PBT7-Th:SMA blends was studied using the Flory–Huggins parameter. 15 SMAs that exhibited balanced mixing with PBT7-Th were selected. Finally, the best predicted PCE is over 15% with the common IDTT core, which is far better than that of reported results. This is an effective pipeline to design and screen potential materials without revisiting the previous experimental work, and thus will pave the way to cost- and time-efficient discovery of novel materials.
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.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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.
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