Seed‐Assisted Growth for Low‐Temperature‐Processed All‐Inorganic CsPbIBr2 Solar Cells with Efficiency over 10%

All‐inorganic CsPbIBr₂ perovskite has recently received growing attention due to its balanced band gap and excellent environmental stability. However, the requirement of high‐temperature processing limits its application in flexible devices. Herein, a low‐temperature seed‐assisted growth (SAG) method for high‐quality CsPbIBr₂ perovskite films through reducing the crystallization temperature by introducing methylammonium halides (MAX, X = I, Br, Cl) is demonstrated. The mechanism is attributed to MA cation based perovskite seeds, which act as nuclei lowering the formation energy of CsPbIBr₂ during the annealing treatment. It is found that methylammonium bromide treated perovskite (Pvsk‐Br) film fabricated at low temperature (150 °C) shows micrometer‐sized grains and superior charge dynamic properties, delivering a device with an efficiency of 10.47%. Furthermore, an efficiency of 11.1% is achieved for a device based on high‐temperature (250 °C) processed Pvsk‐Br film via the SAG method, which presents the highest reported efficiency for inorganic CsPbIBr₂ solar cells thus far.     

Plasmonic Retrofitting of Membrane Materials: Shifting from Self‐Regulation to On‐Command Control of Fluid Flow

This work calls for a paradigm shift in order to change the operational patterns of self‐regulated membranes in response to chemical signals. To this end, the fabrication of a retrofitting material is introduced aimed at developing an innovative generation of porous substrates endowed with symbiotic but fully independent sensing and actuating capabilities. This is accomplished by transferring carefully engineered plasmonic architectures onto commercial microfiltration membranes lacking of such features. The integration of these materials leads to the formation of a coating surface proficient for ultrasensitive detection and “on‐command” gating. Both functionalities can be synergistically modulated by the spatial and temporal distribution of an impinging light beam offering an unprecedented control over the membrane performance in terms of permeability. The implementation of these hybrid nanocomposites in conventional polymeric porous materials holds great potential in applications ranging from intelligent fluid management to advanced filtration technologies and controlled release.     

Selection of control structure for distributed model predictive control in the presence of model errors

This paper presents a new methodology for selecting control structure in the context of distributed model predictive control. An index was developed to quantify the performance of distributed MPC strategies in the presence of model errors. This index was used for two purposes: to solve the decomposition problem whereby the process is decomposed into parts and to compare distributed MPC strategies with different degrees of coordination. Then, a multi-objective Mixed Integer Nonlinear Programming MINLP formulation is proposed to achieve an optimal trade-off between performance and structure simplicity.Four examples are considered to illustrate the methodology. The simulation results are consistent with the conclusions obtained from the analysis. The proposed methodology can be applied at the design stage to select the best control configuration in the presence of model errors.     

Window Query Processing in Linear Quadtrees

The linear quadtree is a spatial access method that is built by decomposing the spatial objects in a database into quadtree blocks and storing these quadtree blocks in a B-tree. The linear quadtree is very useful for geographic information systems because it provides good query performance while using existing B-tree implementations. An algorithm and a cost model are presented for processing window queries in linear quadtrees. The algorithm can handle query windows of any shape in the general case of spatial databases with overlapping objects. The algorithm recursively decomposes the space into quadtree blocks, and uses the quadtree blocks overlapping the query window to search the B-tree. The cost model estimates the I/O cost of processing window queries using the algorithm. The cost model is also based on a recursive decomposition of the space, and it uses very simple parameters that can easily be maintained in the database catalog. Experiments with real and synthetic data sets verify the accuracy of the cost model.     

Breaking the curse of dimensionality: hierarchical Bayesian network model for multi-view clustering

Annals of Mathematics and Artificial Intelligence - Clustering high-dimensional data under the curse of dimensionality is an arduous task in many applications domains. The wide dimension yields the...     

Compositional specification of real time embedded systems by priority time Petri Nets

An important key challenge in Embedded Real Time Systems (ERTS) analysis is to provide a seamless scheduling strategy. Formal methods for checking the temporal characteristics and timing constraints at a high abstraction level have proven to be useful for making the development process reliable. In this paper, we present a Petri Net modeling formalism and an analysis technique which supports not only systems scheduling analysis but also the compositional specification of real time systems. The proposed Priority Time Petri Net gives determinism aspect to the model and accelerates its execution. Indeed, a compositional specification of a PTPN for complex application and multiprocessor architecture that solves the problem of hierarchy is presented.     

A survey of recursive analysis and Moore’s notion of real computation

The theory of analog computation aims at modeling computational systems that evolve in a continuous space. Unlike the situation with the discrete setting there is no unified theory of analog computation. There are several proposed theories, some of them seem quite orthogonal. Some theories can be considered as generalizations of the Turing machine theory and classical recursion theory. Among such are recursive analysis and Moore’s class of recursive real functions. Recursive analysis was introduced by Turing (Proc Lond Math Soc 2(42):230–265, 1936), Grzegorczyk (Fundam Math 42:168–202, 1955), and Lacombe (Compt Rend l’Acad Sci Paris 241:151–153, 1955). Real computation in this context is viewed as effective (in the sense of Turing machine theory) convergence of sequences of rational numbers. In 1996 Moore introduced a function algebra that captures his notion of real computation; it consists of some basic functions and their closure under composition, integration and zero-finding. Though this class is inherently unphysical, much work have been directed at stratifying, restricting, and comparing it with other theories of real computation such as recursive analysis and the GPAC. In this article we give a detailed exposition of recursive analysis and Moore’s class and the relationships between them.     

Beneficial Effects of Silicon (Si) on Sea Barley (Hordeum marinum Huds.) under Salt Stress

Silicon - Silicon (Si) plays an important role in providing beneficial effects on plant growth and yield, especially under stressful environments such as salinity. The objective of this work is to...     

The Influence of Supercritical Carbon Dioxide (SC‐CO2) on Electrolytes and Hydrogenation of Soybean Oil

The electrochemical hydrogenation of soybean oil with supercritical carbon dioxide (SC‐CO₂) has been studied to seek ways for substantial reduction of the trans fatty acids (TFA). The solubility of CO₂ in electrolytes and the conductivity of electrolytes were investigated using a self‐made electrochemical hydrogenation reactor. The optimum hydrogenation parameters were assessed. Both the solubility of CO₂ in electrolytes and the conductivity of electrolytes increased with increasing CO₂ pressure. When the pressure reached a critical point of CO₂, the solubility of CO₂ expressed as a mole fraction was 0.42 in cathode electrolyte and 0.1 in anode electrolyte. At 8 MPa, the conductivity of electrolytes was 1.5 times higher than that at 2 MPa. When the pressure was higher than the critical point of CO₂, the solubility of CO₂ in electrolytes and the conductivity of electrolytes reached a stable value. The optimum condition for electrochemical hydrogenation of soybean oil in SC‐CO₂ were reaction pressure (8 MPa), reaction temperature (48 °C), current (125 mA), agitation speed (300 rpm), and reaction time (8 h). Fatty acid profile, iodine value, and TFA content were evaluated at the optimum parameters. This investigation showed that the electrochemical hydrogenation of soybean oil in SC‐CO₂ was improved. The reaction time was shortened by 4 h, and TFA content was reduced by 35.8% compared to traditional hydrogenation process.