Superb Efficiency Research Articles

Integrating Mo2C/C as cocatalyst into S-scheme Mo2C/C/Co0.5Cd0.5S heterojunction with spatial photocarrier separation for photocatalytic synergistic H2 evolution

Reasonable design and production of potent photocatalysts for sustainable H2 production from solar energy is one of the necessary strategies in the photocatalysis process. The S-scheme heterojunction exposes remarkable superiority in boosting light harvesting and progressing photocarrier separation efficiency in the photocatalyst. Here, new S-scheme heterojunction Mo2C/C/Co0.5Cd0.5S photocatalysts, including different weights of noble metal-free Mo2C/C nanostructures as cocatalysts, were successfully synthesized. As expected, the nanocomposite structures exhibited increased photocatalytic activity relative to single Co0.5Cd0.5S. The nanocomposite containing 5 wt% Mo2C/C exhibited an increased hydrogen production efficiency of 83.86 mmol g−1 h−1, which was 8.4 times higher than single Co0.5Cd0.5S (10.06 mmol g−1 h−1). This implies that for Co0.5Cd0.5S, Mo2C/C increases visible light absorption, facilitates photocarrier separation, and boosts the H2 evolution rate. Moreover, conductive carbon nanoparticles, electron mediators with strong interfacial interaction, serve as rapid photocarrier transfer bridges between Co0.5Cd0.5S and Mo2C. In summary, the superb efficiency of Mo2C/C/Co0.5Cd0.5S is accredited to the synergistic effect between Co0.5Cd0.5S and Mo2C/C, the creation of S-scheme heterojunction, which assists the separation of photocarriers and declines the recombination possibilities. This research ensures a reference and new profound insight for understanding and designing noble metal-free cocatalyst-assisted S-scheme heterojunctions for immensely efficient photocatalytic H2 production.

Simultaneously Realizing High Efficiency and High Color Rendering Index for Hybrid White Organic Light-Emitting Diodes by Ultra-Thin Design of Delayed Fluorescence Sensitized Phosphorescent Layers.

In consideration of energy economization and light quality, concurrently attaining high external quantum efficiency (ηext ) and high color rendering index (CRI) is of high significance for the commercialization of hybrid white organic light-emitting diodes (WOLEDs) but is challenging. Herein, a blue luminescent molecule (2PCz-XT) consisting of a xanthone acceptor and two 3,6-diphenylcarbazole donors is prepared, which exhibits strong delayed fluorescence, short delayed fluorescence lifetime, and excellent electroluminescence property, and can sensitize green, orange, and red phosphorescent emitters efficiently. By employing 2PCz-XT as sensitizer and phosphorescent emitters as dopants, efficient two-color and three-color WOLED architectures with ultra-thin phosphorescent emitting layers (EMLs) are proposed and constructed. By incorporating a thin interlayer to modulate exciton recombination zone and reduce exciton loss, high-performance three-color hybrid WOLEDs are finally achieved, providing a high ηext of 26.8% and a high CRI value 83 simultaneously. Further configuration optimization realizes a long device operational lifetime. These WOLEDs with ultra-thin phosphorescent EMLs are among the state-of-the-art hybrid WOLEDs in the literature, demonstrating the success and applicability of the proposed device design for developing robust hybrid WOLEDs with superb efficiency and color quality.

Solid Lipid Nanoparticles (SLN) and Nanostructured Lipid Carriers (NLC) Prepared by Microwave and Ultrasound-Assisted Synthesis: Promising Green Strategies for the Nanoworld.

Many pharmaceutically active molecules are highly lipophilic, which renders their administration and adsorption in patients extremely challenging. Among the countless strategies to overcome this problem, synthetic nanocarriers have demonstrated superb efficiency as drug delivery systems, since encapsulation can effectively prevent a molecules' degradation, thus ensuring increased biodistribution. However, metallic and polymeric nanoparticles have been frequently associated with possible cytotoxic side effects. Solid lipid nanoparticles (SLN) and nanostructured lipid carriers (NLC), which are prepared with physiologically inert lipids, therefore emerged as an ideal strategy to bypass toxicities issues and avoid the use of organic solvents in their formulations. Different approaches to preparation, using only moderate amounts of external energy to facilitate a homogeneous formation, have been proposed. Greener synthesis strategies have the potential to provide faster reactions, more efficient nucleation, better particle size distribution, lower polydispersities, and furnish products with higher solubility. Particularly microwave-assisted synthesis (MAS) and ultrasound-assisted synthesis (UAS) have been utilized in the manufacturing of nanocarrier systems. This narrative review addresses the chemical aspects of those synthesis strategies and their positive influence on the characteristics of SLNs and NLCs. Furthermore, we discuss the limitations and future challenges for the manufacturing processes of both types of nanoparticles.

Monolithic multistage solar-to-steam device for tandemly generating freshwater and electricity with superb efficiency

As an increasingly attractive global issue, water-power nexus is strongly interplayed and promoted with each other. To solve the mutual expenditure in a low energy-consumption way, hydropower cogeneration nanotechnology rose in an innovative style which strove to explore an integrated design on tandem production of fresh water and power synergistically. Herein, we firstly designed a bionic honeycomb multi-stage solar-thermal-electricity co-generation device (MSTE) with the highlights of its composability, detachability and scalability. A high freshwater production rate is achieved by constructing multi-stage solar evaporator device to recycle the latent heat of prior water vapor in each steamer stage, such as 7-stage MSTE could realize 2.79 kg·m−2·h−1. Simultaneously integrating moist-electric generators (MEG) on the 8–10 stages, a moist-electric generation electricity output of 55.3 mW·m−2 was achieved. This auspicious design of multi-stage hydropower cogeneration device can sufficiently utilize energy by sharing the identical framework system so exhibits a great potential to realize both clean water and electricity production with zero CO2 emission, optimal energy conversion pathway.

Modulation of electronic density states of carbon atom via multifaceted Cu doped Co2P particle for robust and efficient electrocatalytic hydrogen evolution reaction in aqueous acidic medium

Development of Pt free catalyst for hydrogen evolution reaction (HER) having both superb efficiency and sound stability is a key challenge in the field of alternate fuel production. Herein, we have reported Cu doped multifaceted Co2P particles incorporated on expanded graphite synthesized via solvothermal method, and studied its catalytic activity. The composite catalyzes the evolution of H2 efficiently with a potential value 81 mV to achieve the benchmark current density of 10 mA cm-2 and a Tafel slope value of 113 mV dec‑1 coupled with a remarkable long-term stability for 21.6 h. Our experimental analysis provides strong evidence that the multifaceted Cu doped Co2P could work in concert with carbon atoms of the expanded graphite (EG) that leads in the changing of the energy states of nearby carbon atoms of the metal phosphide particle which is also in well agreement of our theoretical calculation. The lower contact electrical resistance of EG have significant impact in endowing fast electron transfer to the catalytic active sites and the aid of catalytic performance. The specific morphology of the catalyst hints at increased surface area and overall exposed active sites to bestow the catalytic performance. The study suggests that the energetically modulated carbon atoms can act as a fine active site for HER.

A new quasi-dynamic method for the prediction of tendon tension of TLP platform

The assessment of tendon mechanics for the tension leg platform (TLP) is crucial to the safety of the whole TLP system. The tendon design includes nonlinear mechanical characteristics such as set-down and various dynamic effects such as slow drift and springing. In this study, a new quasi-dynamic method dedicated to the dynamic tension of tendon and global motion of TLP is established by employing two primary assumptions, i.e. the separation of the hull’s horizontal and vertical response, and the separation of low-frequency, wave-frequency, and high-frequency excitations due to waves. An analytical tendon model is proposed, and the simultaneous time history of tendon tension can be obtained through the post-treatment after carefully handling the hull’s various dynamic responses. A remarkable efficiency is achieved since the time-consuming convolution integral, finite element model of the tendon, etc., are avoided. A comprehensive validation regarding the TLP system, global motion, and tendon tension is conducted by the comparisons with model test measurement and fully coupled analysis using OrcaFlex. An excellent agreement is achieved no matter the cycles and phases in the time series or the extremes in the statistics. The study has proven the quasi-dynamic method a reliable approach with satisfactory accuracy and superb efficiency, which should be a useful method in the design of the TLP platform.

GO/TiO2-Related Nanocomposites as Photocatalysts for Pollutant Removal in Wastewater Treatment.

Water pollution has been a prevalent issue globally for some time. Some pollutants are released into the water system without treatment, making the water not suitable for consumption. This problem may lead to more grave problems in the future including the destruction of the ecosystem along with the organisms inhabiting it, and illness and diseases endangering human health. Conventional methods have been implemented to remove hazardous pollutants such as dyes, heavy metals, and oil but are incapable of doing so due to economic restraints and the inability to degrade the pollutants, leading to secondary pollution. Photocatalysis is a more recently applied concept and is proven to be able to completely remove and degrade pollutants into simpler organic compounds. Titanium dioxide (TiO2) is a fine example of a photocatalyst owing to its cost-effectiveness and superb efficiency. However, issues such as the high recombination rate of photogenerated electrons along with positive holes while being only limited to UV irradiation need to be addressed. Carbonaceous materials such as graphene oxide (GO) can overcome such issues by reducing the recombination rate and providing a platform for adsorption accompanied by photocatalytic degradation of TiO2. The history and development of the synthesis of GO will be discussed, followed by the methods used for GO/TiO2 synthesis. The hybrid of GO/TiO2 as a photocatalyst has received some attention in the application of wastewater treatment due to its efficiency and it being environmentally benign. This review paper thereby aims to identify the origins of different pollutants followed by the sickness they may potentially inflict. Recent findings, including that GO/TiO2-related nanocomposites can remove pollutants from the water system, and on the photodegradation mechanism for pollutants including aromatic dyes, heavy metal and crude oil, will be briefly discussed in this review. Moreover, several crucial factors that affect the performance of photocatalysis in pollutant removal will be discussed as well. Therefore, this paper presents a critical review of recent achievements in the use of GO/TiO2-related nanocomposites and photocatalysis for removing various pollutants in wastewater treatment.

Microstructure-guided deep material network for rapid nonlinear material modeling and uncertainty quantification

Modeling nonlinear materials with arbitrary microstructures and loading paths is crucial in structural analyses with heterogeneous materials with uncertainty. However, it is computationally prohibitive because (1) fine meshes are required to resolve the microstructures for nonlinear simulations, (2) enormous model evaluations are expected to simulate stochastic microstructures and the loading variabilities, and (3) nonlinear material models are generally expensive to solve. In this paper, we present the microstructure-guided deep material network (MgDMN), a deep-learning-based approach to model multi-phase materials and efficiently predict their mechanical response under virtually any loading paths and microstructure definitions considering plasticity. This work is built upon the earlier development of the deep material network (DMN) method for fast predictions of nonlinear homogenized responses of multi-phase materials using linear elastic training data for a predefined microstructure. We extend DMN to MgDMN for any microstructure definition by parameterizing the microstructure space, mapping a small set of DMNs to their respective microstructure parameters, and interpolating the DMNs to generate new models in the microstructure space. The proposed method achieves superb efficiency and accuracy since the interpolated DMNs do not need additional training to predict nonlinear behavior of any new microstructures. We demonstrate our approach via modeling and uncertainty quantification of short fiber reinforced polymer (SFRP) composites. We show that for this dual-phase material, only four base DMNs are needed to predict the nonlinear stress responses for any loading path given any fiber volume fraction, orientation states, and constituent properties as long as the combination is within the microstructure space enclosed by the base models. The accelerated modeling process enables complex structural simulations of heterogeneous materials and nonlinear stochastic analyses with uncertain microstructures, which are essential in computational materials engineering and multiscale modeling. Our research also draws insights into the impact of microstructure uncertainties on linear and nonlinear material behaviors.

Sustainable Synthesis of a Highly Stable and Coke-Free Ni@CeO2 Catalyst for the Efficient Carbon Dioxide Reforming of Methane

A facile and green synthetic strategy is developed in this paper for the construction of an efficient catalyst for the industrially important carbon dioxide reforming of methane, which is also named the dry reforming of methane (DRM). Through controlling the synthetic strategy and Ni content, a high-performance Ni@CeO2 catalyst was successfully fabricated. The catalyst showed superb efficiency for producing the syngas with high and stable conversions at prolonged operating conditions. Incorporating Ni during the ceria (CeO2) crystallization resulted in a more stable structure and smaller nanoparticle (NP) size with a more robust interaction with the support than loading Ni on CeO2 supports by the conventional impregnation method. The H2/CO ratio was almost 1.0, indicating the promising applicability of utilizing the obtained syngas for the Fischer–Tropsch process to produce worthy chemicals. No carbon deposits were observed over the as-synthesized catalyst after operating the DRM reaction for 50.0 h, even at a more coke-favoring temperature (700 °C). Owing to the superb resistance to coke and sintering, control of the size of the Ni-NPs, uniform dispersion of the active phase, and potent metal interaction with the support, the synthesized catalyst achieved a magnificent catalytic activity and durability during serving for the DRM reaction for extended operating periods.

Proxy-Based Deep Learning Framework for Spectral–Spatial Hyperspectral Image Classification: Efficient and Robust

Deep convolutional networks have been extensively deployed in hyperspectral image (HSI) classification. Reaching for high accuracy, the existing deep-learning-based methods commonly deepen or widen their networks for better performance, which brings higher computational complexity and the risk of overfitting. Although the introduction of the residual module and batch-normalization reduces the generalization degradation in complex networks, the mainstream methods still suffer from low robustness to the noise. To tackle these issues, a compact proxy-based deep learning framework is proposed to perform highly accurate HSI classification with superb efficiency and robustness. In this article: 1) novel deep proxies are integrated to replace the dense classifier layers in conventional networks, which represents specific classes in deep embedding space and enables fast and reliable convergence; 2) the proxy-based feature embedding is studied in distance metric and similarity metric, and compatible dual-metric loss functions are designed for further optimized embedding distribution, which leads to more robust generalization; and 3) state-of-the-art performance and robustness are demonstrated by the proposed framework on mainstream HSI data sets with the minimal network scale and time complexity.

Design and synthesis of BiVO4@CuOx as a photo assisted Fenton-like catalyst for efficient degradation of tetracycline

The rational design and fabrication of stable and efficient heterogeneous Fenton-like catalyst is of great significance for wastewater treatment, but is also fraught with obstacles. In this paper, we synthesized BiVO4@CuOx composite catalyst through a simple hydrothermal method and in situ chemical deposition. The degradation of tetracycline (TC) is carried out as a main model pollutant through Fenton reaction assisted by light irradiation. The prepared catalyst exhibits superb efficiency and satisfactory stability for the degradation of TC owing to the synergistic effect of light irradiation and decomposition of H2O2. The factors influencing the degradation such as catalyst dosage, H2O2 concentration and initial pH value are also investigated. Moreover, a possible mechanism of degradation of TC in BiVO4@CuOx/H2O2 system is discussed based on the premise that •OH, •O2− and h+ are demonstrated to be the main active species via in-situ ESR measurements. This work puts forward a new suggestion for designing and synthesizing photo assisted Fenton-like catalyst to remove organic contaminants in sewage.

A method to predict the flow-induced noise of an open cavity of a complex geometry

Shear flow past a cavity involves a complex fluid dynamic process. Of vital importance is the occurrence of self-sustained oscillations that give rise to tones and the amplitudes of which may be further amplified if the hydrodynamic mode is coupled with the cavity mode. Extensive efforts have been made to investigate the mechanisms of such a simple yet compelling system as well as to predict the noise generated, while most of them are focused on geometry of rectangular shape. For an irregular shaped cavity, numerical methods are usually used which are computationally expensive. A method is developed to predict the tones generated by the shear flow past an open cavity of a complex geometry. In view of the feedback process involved within the system, a describing-function method decomposing the system into a non-linear part and a linear part is used. The linear description function is established by the patch mobility method where the transfer function between patches is extracted from finite element results, while the nonlinear description function is established based on the vortex sound theory. The proposed method showed a superb computation efficiency over CFD method and its accuracy was justified by comparing with the results of public literature.

Computational design of highly stable and soluble alcohol dehydrogenase for NADPH regeneration

Nicotinamide adenine dinucleotide phosphate (NADPH), as a well-known cofactor, is widely used in the most of enzymatic redox reactions, playing an important role in industrial catalysis. However, the absence of a comparable method for efficient NADP+ to NADPH cofactor regeneration radically impairs efficient green chemical synthesis. Alcohol dehydrogenase (ADH) enzymes, allowing the in situ regeneration of the redox cofactor NADPH with high specific activity and easy by-product separation process, are provided with great industrial application potential and research attention. Accordingly, herein a NADP+-specific ADH from Clostridium beijerinckii was selected to be engineered for cofactor recycle, using an automated algorithm named Protein Repair One-stop Shop (PROSS). The mutant CbADH-6M (S24P/G182A/G196A/H222D/S250E/S254R) exhibited a favorable soluble and highly active expression with an activity of 46.3 U/mL, which was 16 times higher than the wild type (2.9 U/mL), and a more stable protein conformation with an enhanced thermal stability: Δ {T}_{1/2}^{60mathrm{min}}= + 3.6 °C (temperature of 50% inactivation after incubation for 60 min). Furthermore, the activity of CbADH-6M was up-graded to 2401.8 U/mL by high cell density fermentation strategy using recombinant Escherichia coli, demonstrating its industrial potential. Finally, the superb efficiency for NADPH regeneration of the mutant enzyme was testified in the synthesis of some fine chiral aromatic alcohols coupling with another ADH from Lactobacillus kefir (LkADH).

Molecular free energy optimization on a computational graph.

Free energy is arguably the most important property of molecular systems. Despite great progress in both its efficient estimation by scoring functions/potentials and more rigorous computation based on extensive sampling, we remain far from accurately predicting and manipulating biomolecular structures and their interactions. There are fundamental limitations, including accuracy of interaction description and difficulty of sampling in high dimensional space, to be tackled. Computational graph underlies major artificial intelligence platforms and is proven to facilitate training, optimization and learning. Combining autodifferentiation, coordinates transformation and generalized solvation free energy theory, we construct a computational graph infrastructure to realize seamless integration of fully trainable local free energy landscape with end to end differentiable iterative free energy optimization. This new framework drastically improves efficiency by replacing local sampling with differentiation. Its specific implementation in protein structure refinement achieves superb efficiency and competitive accuracy when compared with state of the art all-atom mainstream methods.

Extremely superb efficiency and lifetime of deep blue phosphorescent OLEDs by introducing a hypsochromic emissive intermolecular complex (HEIC) with a negligibly small ΔEST and fast reverse intersystem crossing rate

Concept of a hypsochromic emissive intermolecular complex (HEIC) compared with the conventional exciplex and electroplex. A mixed host system with HEIC exhibits blue-shifted electroluminescence as well as photoluminescence.

Recyclable Magnetic Cu/CuFe2O4 Nanocomposites for the Rapid Degradation of 4-NP

Magnetic Cu/CuFe2O4 nanocomposites were prepared by the one-pot thermal decomposition of acetylacetone compounds. Adjusting the molar ratios of Fe to Cu was used to control the content of Cu in the synthetic process. XRD, TEM, XPS and UV-Vis were employed to reveal detailed structural and catalytic activities of Cu/CuFe2O4 nanocomposites. Magnetic measurements demonstrated that Cu/CuFe2O4 nanocomposites possessed a considerable magnetic saturation. Cu/CuFe2O4 nanocomposites showed superb efficiency in the degradation of 4-nitrophenol (4-NP) to 4-aminophenol (4-AP). 4-NP could be reduced by Cu/CuFe2O4 nanocomposites within 40 s in the attendance of NaBH4. Cu nanocrystals played an indispensable rose in the enhancement of catalytic performance. The synergistic effect of Cu and CuFe2O4 nanocrystals achieved the high-efficiency catalytic reduction for 4-NP. After six recycling experiments, the efficiency of Cu/CuFe2O4 nanocomposites was almost stable. Our work advances a straightforward strategy to synthesize efficient and recoverable Cu/CuFe2O4 nanocomposites, which has promising utilizations in the purifying of nitrophenolic contamination.

Functionalized nanofibrous nylon 6 membranes for efficient reusable and selective separation of laccase enzyme

The development of biomolecules adsorbent with superb efficiency and large processing throughput is of great importance for separation and reusing of enzymes in its free form in various vital industries. Herein, laccase enzyme adsorbent was prepared by carboxylation of nanofibrous nylon 6 membranes (Ny NFM) to form complex with copper metal ions [Ny-CCA-Cu(II)] with high selectivity for laccase adsorption with adsorption capacity 220 mg.g−1 within 4 h. The prepared nylon NFM provides a promising candidate for separation and use of laccase enzyme in its free form for several times without significant change in its activity. Moreover, [Ny-CCA-Cu(II)] showed dynamic binding efficiency of 160 mg g−1 which was achieved solely by a gravity filtration, as well as good reusability of [Ny-CCA-Cu(II)] at least for 5 cycles. The results were due to stable physical and chemical structures, highly tortuous open-porous structure of the nanofibrous membranes, and incorporated Cu+2 complex in [Ny-CCA-Cu(II)]. This simple approach may introduce a way for the design and development of a cost-effective, environmentally friendly and selective adsorptive membranes to recover the valuable enzymes in industrial applications.

Early Detection of Alzheimer's Disease Using Magnetic Resonance Imaging: A Novel Approach Combining Convolutional Neural Networks and Ensemble Learning.

Early detection is critical for effective management of Alzheimer’s disease (AD) and screening for mild cognitive impairment (MCI) is common practice. Among several deep-learning techniques that have been applied to assessing structural brain changes on magnetic resonance imaging (MRI), convolutional neural network (CNN) has gained popularity due to its superb efficiency in automated feature learning with the use of a variety of multilayer perceptrons. Meanwhile, ensemble learning (EL) has shown to be beneficial in the robustness of learning-system performance via integrating multiple models. Here, we proposed a classifier ensemble developed by combining CNN and EL, i.e., the CNN-EL approach, to identify subjects with MCI or AD using MRI: i.e., classification between (1) AD and healthy cognition (HC), (2) MCIc (MCI patients who will convert to AD) and HC, and (3) MCIc and MCInc (MCI patients who will not convert to AD). For each binary classification task, a large number of CNN models were trained applying a set of sagittal, coronal, or transverse MRI slices; these CNN models were then integrated into a single ensemble. Performance of the ensemble was evaluated using stratified fivefold cross-validation method for 10 times. The number of the intersection points determined by the most discriminable slices separating two classes in a binary classification task among the sagittal, coronal, and transverse slice sets, transformed into the standard Montreal Neurological Institute (MNI) space, acted as an indicator to assess the ability of a brain region in which the points were located to classify AD. Thus, the brain regions with most intersection points were considered as those mostly contributing to the early diagnosis of AD. The result revealed an accuracy rate of 0.84 ± 0.05, 0.79 ± 0.04, and 0.62 ± 0.06, respectively, for classifying AD vs. HC, MCIc vs. HC, and MCIc vs. MCInc, comparable to previous reports and a 3D deep learning approach (3D-SENet) based on a more state-of-the-art and popular Squeeze-and-Excitation Networks model using channel attention mechanism. Notably, the intersection points accurately located the medial temporal lobe and several other structures of the limbic system, i.e., brain regions known to be struck early in AD. More interestingly, the classifiers disclosed multiple patterned MRI changes in the brain in AD and MCIc, involving these key regions. These results suggest that as a data-driven method, the combined CNN and EL approach can locate the most discriminable brain regions indicated by the trained ensemble model while the generalization ability of the ensemble model was maximized to successfully capture AD-related brain variations early in the disease process; it can also provide new insights into understanding the complex heterogeneity of whole-brain MRI changes in AD. Further research is needed to examine the clinical implication of the finding, capability of the advocated CNN-EL approach to help understand and evaluate an individual subject’s disease status, symptom burden and progress, and the generalizability of the advocated CNN-EL approach to locate the most discriminable brain regions in the detection of other brain disorders such as schizophrenia, autism, and severe depression, in a data-driven way.

Research Direction toward Scalable, Stable, and High Efficiency Perovskite Solar Cells

AbstractDiscovery of the 9.7% efficiency, 500 h stable solid‐state perovskite solar cell (PSC) in 2012 triggered off a wave of perovskite photovoltaics. As a result, a certified power conversion efficiency (PCE) of 25.2% was recorded in 2019. Publications on PSCs have increased exponentially since 2012 and the total number of publications reached over 13 200 as of August 2019. PCE has improved by developing device structures from mesoscopic sensitization to planar p‐i‐n (or n‐i‐p) junction and by changing composition from MAPbI3 to FAPbI3‐based mixed cations and/or mixed anion perovskites. Long‐term stability has been significantly improved by interfacial engineering with hydrophobic materials or the 2D/3D concept. Although small area cells exhibit superb efficiency, scale‐up technology is required toward commercialization. In this review, research direction toward large‐area, stable, high efficiency PSCs is emphasized. For large‐area perovskite coating, a precursor solution is equally important as coating methods. Precursor engineering and formulation of the precursor solution are described. For hysteresis‐less, stable, and higher efficiency PSCs, interfacial engineering is one of the best ways as defects can be effectively passivated and thereby nonradiative recombination is efficiently reduced. Methodologies are introduced to minimize interfacial and grain boundary recombination.

A robust meshfree method for analysis of cohesive crack propagation problems

In this paper, a new numerical approach is presented for the analysis of cohesive crack propagation in quasi-brittle materials. This approach effectively takes advantage of meshfree radial point interpolation method (RPIM) in conjunction with an accurate integration technique for simulation of cohesive crack growth. Using meshfree methods makes it possible to simply and efficiently model the propagation of the cohesive crack by adding a few number of nodes in the vicinity of the crack tip at each step of crack propagation. Furthermore, a discrete crack can easily be modeled in the meshfree formulation by methods such as visibility criterion. The meshfree method used here is based on the global weak formulation whose domain integrations are calculated with the background decomposition method (BDM). This integration technique can suitably evaluate the domain integrals with high accuracy and minimum computational cost. For the sake of modeling cohesive crack propagation path, consecutive straight segments are used, for each of which interfacial intervals are utilized to apply the constitutive law of cohesive zone. The discretized system of equations for the proposed meshfree method is derived and it is shown how the cohesive forces contribute in generation of the stiffness matrix. Through some numerical examples, the accuracy and efficiency of the proposed method for the analysis of cohesive crack propagation are verified. Also, the accuracy and efficiency of the proposed method are compared to other widely-used numerical schemes. By these examples, the superb efficiency of the presented technique is assessed. Particularly, it is shown that the number of nodes required for achieving accurate results is considerably smaller than other numerical methods such as the extended finite element method XFEM.