Institute of Applied Technology

The aqueous zinc ion battery has emerged as a promising alternative technology for large-scale energy storage due to its low cost, natural abundance, and high safety features. However, the sluggish kinetics stemming from the strong electrostatic interaction of divalent zinc ions in the host crystal structure is one of challenges for highly efficient energy storage. Oxygen vacancies (VO••), in the present work, lead to a larger tunnel structure along the b axis, which improves the reactive kinetics and enhances Zn-ion storage capability in VO2 (B) cathode. DFT calculations further support that VO•• in VO2 (B) result in a narrower bandgap and lower Zn ion diffusion energy barrier compared to those of pristine VO2 (B). VO••-rich VO2 (B) achieves a specific capacity of 375 mAh g–1 at a current density of 100 mA g–1 and long-term cyclic stability with retained specific capacity of 175 mAh g–1 at 5 A g–1 over 2000 cycles (85% capacity retention), higher than that of VO2 (B) nanobelts (280 mAh g–1 at 100 mA g–1 and 120 mAh g–1 at 5 A g–1, 65% capacity retention).

Principal component analysis (PCA) has been widely applied in the area of computer science. It is well-known that PCA is a popular transform method and the transform result is not directly related to a sole feature component of the original sample. However, in this paper, we try to apply principal components analysis (PCA) to feature selection. The proposed method well addresses the feature selection issue, from a viewpoint of numerical analysis. The analysis clearly shows that PCA has the potential to perform feature selection and is able to select a number of important individuals from all the feature components. Our method assumes that different feature components of original samples have different effects on feature extraction result and exploits the eigenvectors of the covariance matrix of PCA to evaluate the significance of each feature component of the original sample. When evaluating the significance of the feature components, the proposed method takes a number of eigenvectors into account. Then it uses a reasonable scheme to perform feature selection. The devised algorithm is not only subject to the nature of PCA but also computationally efficient. The experimental results on face recognition show that when the proposed method is able to greatly reduce the dimensionality of the original samples, it also does not bring the decrease in the recognition accuracy.

Forest fire disaster is currently the subject of intense research worldwide. The development of accurate strategies to prevent potential impacts and minimize the occurrence of disastrous events as much as possible requires modeling and forecasting severe conditions. In this study, we developed five new hybrid machine learning algorithms namely, Frequency Ratio-Multilayer Perceptron (FR-MLP), Frequency Ratio-Logistic Regression (FR-LR), Frequency Ratio-Classification and Regression Tree (FR-CART), Frequency Ratio-Support Vector Machine (FR-SVM), and Frequency Ratio-Random Forest (FR-RF), for mapping forest fire susceptibility in the north of Morocco. To this end, a total of 510 points of historic forest fires as the forest fire inventory map and 10 independent causal factors including elevation, slope, aspect, distance to roads, distance to residential areas, land use, normalized difference vegetation index (NDVI), rainfall, temperature, and wind speed were used. The area under the receiver operating characteristics (ROC) curves (AUC) was computed to assess the effectiveness of the models. The results of conducting proposed models indicated that RF-FR achieved the highest performance (AUC = 0.989), followed by SVM-FR (AUC = 0.959), MLP-FR (AUC = 0.858), CART-FR (AUC = 0.847), LR-FR (AUC = 0.809) in the forecasting of the forest fire. The outcome of this research as a prediction map of forest fire risk areas can provide crucial support for the management of Mediterranean forest ecosystems. Moreover, the results demonstrate that these novel developed hybrid models can increase the accuracy and performance of forest fire susceptibility studies and the approach can be applied to other areas.

Historical exploration of flash flood events and producing flash-flood susceptibility maps are crucial steps for decision makers in disaster management. In this article, classification and regression tree (CART) methodology and its ensemble models of random forest (RF), boosted regression trees (BRT) and extreme gradient boosting (XGBoost) were implemented to create a flash-flood susceptibility map of the Bâsca Chiojdului River Basin, one of the areas in Romania that is constantly exposed to flash floods. The torrential areas including 962 flash flood events were delineated from orthophotomaps and field observations. Furthermore, a set of conditioning forces to explain the flash floods was constructed which included aspect, land use and land cover (LULC), hydrological soil groups lithology, slope, topographic wetness index (TWI), topographic position index (TPI), profile curvature, convergence index and stream power index (SPI). All models indicated the slope as the most important factor triggering the flash flood occurrence. The highest area under the curve (AUC) was achieved by the RF model (AUC = 0.956), followed by the BRT model (AUC = 0.899), XGBoost model (AUC = 0.892) and CART model (AUC = 0.868), respectively. The results showed that the central part of the Bâsca Chiojdului river basin, which covers approximately 30% of the study area, is more susceptible to flash flooding.

Abstract With the potential of achieving high efficiency and low production costs, perovskite solar cells (PSCs) have attracted great attention. However, their unstableness under moist condition has retarded the commercial development. Recently, 2D perovskites have received a lot of attention due to their high moisture resistance. In this work, four quasi 2D quasi perovskites are prepared, then their stability under moist condition is investigated. The surface morphology, crystal structure, optical properties, and photovoltaic performance are measured. Among the four quasi‐2D perovskites, (C 6 H 5 CH 2 NH 3 ) 2 (FA) 8 Pb 9 I 28 has the best performance: uniform and dense film, extremely well‐oriented crystal structure, strong absorption, and a high power conversion efficiency (PCE) of 17.40%. The aging tests show that quasi‐2D perovskites are more stable under moist conditions than FAPbI 3 is. The (C 6 H 5 CH 2 NH 3 ) 2 (FA) 8 Pb 9 I 28 quasi‐2D perovskite devices exhibit high humidity stability, maintaining 80% of the starting PCE after 500 h under 80% relative humidity. Compared with other quasi‐2D perovskites, (C 6 H 5 CH 2 NH 3 ) 2 (FA) 8 Pb 9 I 28 has the highest humidity stability, due to their strongest hydrophobicity from C 6 H 5 CH 2 NH 3 + . This work demonstrates that the properties of perovskite materials can be modified by adding different ammonium salts into FAPbI 3 . Thus, by introducing ammonium salts with high hydrophobic properties the fabrication of highly efficient and stable 2D PSCs may be possible.

Due to the malicious attacks in wireless networks, physical layer security has attracted increasing concerns from both academia and industry. The research on physical layer security mainly focuses either on the secrecy capacity/achievable secrecy rate/capacity-equivocation region from the perspective of information theory, or on the security designs from the viewpoints of optimization and signal processing. Because of its importance in security designs, the latter research direction is surveyed in a comprehensive way in this paper. The survey begins with typical wiretap channel models to cover common scenarios and systems. The topics on physical-layer security designs are then summarized from resource allocation, beamforming/precoding, and antenna/node selection and cooperation. Based on the aforementioned schemes, the performance metrics and fundamental optimization problems are discussed, which are generally adopted in security designs. Thereafter, the state of the art of optimization approaches on each research topic of physical layer security is reviewed from four categories of optimization problems, such as secrecy rate maximization, secrecy outrage probability minimization, power consumption minimization, and secure energy efficiency maximization. Furthermore, the impacts of channel state information on optimization and design are discussed. Finally, the survey concludes with the observations on potential future directions and open challenges.

system was studied by various analytical methods and first-principles calculation.

Portable electronic devices and wireless communication systems enable a broad range of applications such as environmental and food safety monitoring, personalized medicine and healthcare management. Particularly, hybrid smartphone and microfluidic devices provide an integrated solution for the new generation of mobile sensing applications. Such mobile sensing based on microfluidic devices (broadly defined) and smartphones (MS(2)) offers a mobile laboratory for performing a wide range of bio-chemical detection and analysis functions such as water and food quality analysis, routine health tests and disease diagnosis. MS(2) offers significant advantages over traditional platforms in terms of test speed and control, low cost, mobility, ease-of-operation and data management. These improvements put MS(2) in a promising position in the fields of interdisciplinary basic and applied research. In particular, MS(2) enables applications to remote in-field testing, homecare, and healthcare in low-resource areas. The marriage of smartphones and microfluidic devices offers a powerful on-chip operating platform to enable various bio-chemical tests, remote sensing, data analysis and management in a mobile fashion. The implications of such integration are beyond telecommunication and microfluidic-related research and technology development. In this review, we will first provide the general background of microfluidic-based sensing, smartphone-based sensing, and their integration. Then, we will focus on several key application areas of MS(2) by systematically reviewing the important literature in each area. We will conclude by discussing our perspectives on the opportunities, issues and future directions of this emerging novel field.

Abstract Groundwater plays a vital role in the sustainable development of agriculture, society and economy, and it's demand is increasing due to low rainfall, especially in arid and semiarid regions. In this context, delineation of groundwater potential zones is essential for meeting the demand of different sectors. In this research, the integrated approach consisting of analytical hierarchy process (AHP), multiple influence factors (MIF) and receiver operating characteristics (ROC) was applied. The demarcation of groundwater potential zones is based on thematic maps, namely Land Use/Land Cover (LULC), Digital Elevation Model (DEM), hillshade, soil texture, slope, groundwater depth, geomorphology, Normalized Difference Vegetation Index (NDVI), and flow direction and accumulation. The pairwise comparison matrix has been created, and weights are assigned to each thematic layer. The comparative score to every factor was calculated from the overall weight of two major and minor influences. Groundwater potential zones were classified into five classes, namely very poor, poor, moderate, good and very good, which cover an area as follows: 3.33 km 2 , 785.84 km 2 , 1147.47 km 2 , 595.82 km 2 and 302.65 km 2 , respectively, based on AHP method. However, the MIF groundwater potential zones map was classified into five classes: very poor, poor, moderate, good and very good areas covered 3.049 km 2 , 567.42 km 2 , 1124.50 km 2 868.86 km 2 and 266.67 km 2 , respectively. The results of MIF and AHP techniques were validated using receiver operating characteristics (ROC). The result of this research would be helpful to prepare the sustainable groundwater planning map and policy. The proposed framework has admitted to test and could be implemented in different in various regions around the world to maintain the sustainable practices.

A mesoporous SnO₂electrode is firstly introduced in the CH₃NH₃PbI₃perovskite solar cell as the electron-transporting material and scaffold layer with over 10% power conversion efficiency.

Abstract The organic–inorganic halide CH 3 NH 3 PbI 3 (MAPbI 3 ) has been the most commonly used light absorber layer of perovskite solar cells (PSCs); however, solution‐processed MAPbI 3 films usually suffer from random crystal orientation and high trap density, resulting in inferior power conversion efficiency (PCE) with open circuit voltage ( V oc ) being typically below 1.2 V for PSC devices. Herein, for the first time an imidazole sulfonate zwitterion, 4‐(1H‐imidazol‐3‐ium‐3‐yl)butane‐1‐sulfonate (IMS), is applied as a bifunctional additive in regular‐structure planar heterojunction PSC devices to regulate the crystal orientation, yielding highly ordered MAPbI 3 film and passivating the trap states of the film. Such a dual effect of IMS is fulfilled via coordination interactions between the sulfonate moiety of IMS with the Pb2 + ion and the electrostatic interaction between the imidazole of IMS with the I – ion of MAPbI 3 . As a result, under a optimized IMS doping ratio of 0.5 wt%, the PSC device exhibits a significant increase in PCE from 18.77% to 20.84%, with suppressed current–voltage hysteresis and promoted ambient stability. Moreover, a high V oc of 1.208 V is achieved under a higher IMS doping ratio of 1.2 wt%, which is the highest V oc for regular‐structure MAPbI 3 planar PSC devices based on TiO 2 electron transport layer.

Various types of chronic diseases (CD) are the leading causes of disability and death worldwide. While those diseases are chronic in nature, accurate and timely clinical decision making is critically required. Current diagnosis procedures are often lengthy and costly, which present a major bottleneck for effective CD healthcare. Rapid, reliable and low-cost diagnostic tools at point-of-care (PoC) are therefore on high demand. Owing to miniaturization, lab-on-chip (LoC) technology has high potential to enable improved biomedical applications in terms of low-cost, high-throughput, ease-of-operation and analysis. In this direction, research toward developing new LoC-based PoC systems for CD diagnosis is fast growing into an emerging area. Some studies in this area began to incorporate digital and mobile technologies. Here we review the recent developments of this area with the focus on chronic respiratory diseases (CRD), diabetes, and chronic kidney diseases (CKD). We conclude by discussing the challenges, opportunities and future perspectives of this field.

The nucleation stage has an important influence on the lead halide perovskite film morphology, and therefore the solar cell performance.

Zeolites are microporous silicates with a large variety of applications as catalysts, adsorbents, and cation exchangers. Stable silica-based zeolites with increased porosity are in demand to allow adsorption and processing of large molecules but challenge our synthetic ability. We report a new, highly stable pure silica zeolite called ZEO-3, which has a multidimensional, interconnected system of extra-large pores open through windows made by 16 and 14 silicate tetrahedra, the least dense polymorph of silica known so far. This zeolite was formed by an unprecedented one-dimensional to three-dimensional (1D-to-3D) topotactic condensation of a chain silicate. With a specific surface area of more than 1000 square meters per gram, ZEO-3 showed a high performance for volatile organic compound abatement and recovery compared with other zeolites and metal-organic frameworks.

Landslides are dangerous events which threaten both human life and property. The study aims to analyze the landslide susceptibility (LS) in the Kysuca river basin, Slovakia. For this reason, previous landslide events were analyzed with 16 landslide conditioning factors. Landslide inventory was divided into training (70% of landslide locations) and validating dataset (30% of landslide locations). The heuristic approach of Fuzzy Decision Making Trial and Evaluation Laboratory (FDEMATEL)-Analytic Network Process (ANP) was applied first, followed by bivariate Frequency Ratio (FR), multivariate Logistic Regression (LR), Random Forest Classifier (RFC), Naive Bayes Classifier (NBC) and Extreme Gradient Boosting (XGBoost), respectively. The results showed that 52.2%, 36.5%, 40.7%, 50.6%, 43.6% and 40.3% of the total basin area had very high to high LS corresponding to FDEMATEL-ANP, FR, LR, RFC, NBC and XGBoost model, respectively. The analysis revealed that RFC was the most accurate model (overall accuracy of 98.3% and AUC of 97.0%). Besides, the heuristic approach of FDEMATEL-ANP model (overall accuracy of 93.8% and AUC of 92.4%) had better prediction capability than bivariate FR (overall accuracy of 86.9% and AUC of 86.1%), multivariate LR (overall accuracy of 90.5% and AUC of 91.2%), machine learning NBC (overall accuracy of 76.3% and AUC of 90.9%) and even deep learning XGBoost (overall accuracy of 92.3% and AUC of 87.1%) models. The study revealed that the FDEMATEL-ANP outweighed the NBC and XGBoost machine learning models, which suggests that heuristic methods should be tested out before directly applying machine learning models.

One of the limitations of TiO2 based perovskite solar cells is the poor electron mobility of TiO2. Here, perovskite oxide BaSnO3 is used as a replacement. It has a higher electron mobility and the same perovskite structure as the light harvesting materials. After optimization, devices based on BaSnO3 showed the best performance of 12.3% vs. 11.1% for TiO2.

The orientation of ultrahigh aspect ratio thermally conductive fillers can construct a heat transfer path to enhance the thermal conductivity of composite materials effectively with low filler loading. Nevertheless, single orientation (vertical or horizontal) limited the application of these materials when there was the need for isotropic heat transferring. Here we report a novel strategy to prepare thermally conductive flexible cycloaliphatic epoxy resin nanocomposites with an oriented three-dimensional staggered interconnected network of vertically aligned h-BN (hexagonal boron nitride) platelets and randomly dispersed CNT-NH2 (aminated carbon nanotubes). In this structure, h-BN platelets coated with magnetic particles could respond to the external magnetic field; however, the CNT-NH2 couldn’t. The obtained composites exhibited both through-plane (0.98 ± 0.037 W/m·K) and in-plane (0.99 ± 0.001 W/m·K) thermal conductivity enhancement at low h-BN loading of 30 wt %, and also presented excellent electrical insulating properties (<1.2 × 10–12 S/cm). In addition, the equal value of thermal conductivity of two directions (in-plane and through-plane) was shown when the content of h-BN was about 26.43 wt % and of CNT-NH2 was 2 wt %, displaying no difference between the thermal conductivity of two directions (in-plane and through-plane). The infrared imaging tests showed the outstanding heat dissipation capability of the composites by capturing the surface temperature variations of a heater with the composites as the heat dissipating material.

We demonstrate a novel simplified close space sublimation (CSS) deposition for growing a high quality CH₃NH₃PbI₃thin film in a low-vacuum and even non-vacuum oven.

"This book focuses on two kinds of advanced biometric recognition technologies, biometric data discrimination and multi-biometrics"--Provided by publisher

Yolk-shell TiO2 microspheres were synthesized via a one-pot template-free solvothermal method building on the aldol condensation reaction of acetylacetone. This unique structure shows superior light scattering ability resulting in power conversion efficiency as high as 11%. This work provided a new synthesis system for TiO2 microspheres from solid to hollow and a novel material platform for high performance solar cells.