The formation of semiconductor composites comprising multicomponent or multiphase heterojunctions is a very effective strategy to design highly active photocatalyst systems. This review summarizes the recent strategies to develop such composites, and highlights the most recent developments in the fi eld. After a general introduction into the different strategies to improve photocatalytic activity through formation of heterojunctions, the three different types of heterojunctions are introduced in detail, followed by a historical introduction to semiconductor heterojunction systems and a thorough literature overview. Special chapters describe the highly-investigated carbon nitride heterojunctions as well as very recent developments in terms of multiphase heterojunction formation, including the latest insights into the anatase-rutile system. When carefully designed, semiconductor composites comprising two or three different materials or phases very effectively facilitate charge separation and charge carrier transfer, substantially improving photocatalytic and photoelectrochemical effi ciency.

https://faculty.missouri.edu/~glaserr/3700s15/Marschall_Strategies_adfm201303214.pdf

Semiconductor Composites: Strategies for Enhancing Charge Carrier Separation to Improve Photocatalytic Activity

Charge kinetics is highly critical in determining the quantum efficiency of solar-to-chemical conversion in photocatalysis, and this includes, but is not limited to, the separation of photoexcited electron–hole pairs, utilization of plasmonic hot carriers and delivery of photo-induced charges to reaction sites, as well as activation of reactants by energized charges. In this review, we highlight the recent progress on probing and steering charge kinetics toward designing highly efficient photocatalysts and elucidate the fundamentals behind the combinative use of controlled synthesis, characterization techniques (with a focus on spectroscopic characterizations) and theoretical simulations in photocatalysis studies. We first introduce the principles of various processes associated with charge kinetics that account for or may affect photocatalysis, from which a set of parameters that are critical to photocatalyst design can be summarized. We then outline the design rules for photocatalyst structures and their corresponding synthetic approaches. The implementation of characterization techniques and theoretical simulations in different steps of photocatalysis, together with the associated fundamentals and working mechanisms, are also presented. Finally, we discuss the challenges and opportunities for photocatalysis research at this unique intersection as well as the potential impact on other research fields.

Steering charge kinetics in photocatalysis: intersection of materials syntheses, characterization techniques and theoretical simulations

Nanomaterials have been extensively studied for heavy metal ions and dye removals from wastewater. This article reviews the role of nanomaterials as effective adsorbents for wastewater purification. In recent years, numerous novel nanomaterial adsorbents have been developed for enhancing the efficiency and adsorption capacities of removing contaminants from wastewater. The innovation, forthcoming development, and challenges of cost-effective and environmentally acceptable nanomaterials for water purification are discussed and reviewed in this article. This review concludes that nanomaterials have many unique morphological and structural properties that qualify them to be used as effective adsorbents to solve several environmental problems.

/pdf/the-role-of-nanomaterials-as-effective-adsorbents-and-their-1xwpxvvlx7.pdf

The role of nanomaterials as effective adsorbents and their applications in wastewater treatment

Kinetics, isotherms and thermodynamic modeling of liquid phase adsorption of Rhodamine B dye onto Raphia hookerie fruit epicarp

TiO2 has received tremendous attention owing to its potential applications in the field of photocatalysis for solar fuel production and environmental remediation. This review mainly describes vario...

https://www.tandfonline.com/doi/pdf/10.1080/17518253.2018.1440324

Modification strategies of TiO2 for potential applications in photocatalysis: a critical review

https://cyberleninka.org/article/n/13265.pdf

Kinetics and equilibrium study for the adsorption of textile dyes on coconut shell activated carbon

Titanium dioxide nanoparticles consisting of pure anatase, anatase-rich, brookite-rich, and pure brookite modifications were synthesized and characterized by X-ray diffraction, field emission-scanning electron microscopy and nitrogen adsorption. The phase transformations among the three modifications of TiO2 (anatase, brookite, and rutile) and the photocatalytic activities of these nanoparticles were investigated by heat treatment over the temperature range from 400 to 800 °C and by the photooxidation of methanol, respectively. Direct transformation of anatase and brookite to rutile was observed, while in the case of the anatase–brookite mixture, anatase transforms firstly to brookite and then to rutile. The photocatalytic activity measurements indicate that brookite nanoparticles exhibit higher photocatalytic activities than anatase, and a comparable activity to that of the anatase-rich nanoparticles. The phase transformations and photocatalytic results are discussed regarding their dependence on crystallite size, surface area, and phase composition.

/pdf/brookite-versus-anatase-tio2-photocatalysts-phase-5exp7f2fiu.pdf

Brookite versus anatase TiO2 photocatalysts: phase transformations and photocatalytic activities

One of the important trends in an intelligent data analysis will be the growing importance of data processing. But this point faces problems similar to those of data mining (i.e., high-dimensional data, missing value imputation and data integration); one of the challenges in estimation missing value methods is how to select the optimal number of nearest neighbors of those values. This paper, attempting to search the capability of building a novel tool to estimate missing values of various datasets called developed random forest and local least squares (DRFLLS). By developing random forest algorithm, seven categories of similarity measures were defined. These categories are person similarity coefficient, simple similarity, and fuzzy similarity (M1, M2, M3, M4 and M5). They are sufficient to estimate the optimal number of neighborhoods of missing values in this application. Hereafter, local least squares (LLS) has been used to estimate the missing values. Imputation accuracy can be measured in different ways: Pearson correlation (PC) and NRMSE. Then, the optimal number of neighborhoods is associated with the highest value of PC and a smaller value of NRMSE. The experimental results were carried out on six datasets obtained from different disciplines, and DRFLLS proves the dataset which has a small rate of missing values gave the best estimation to the number of nearest neighbors by DRFPC and in the second degree by DRFFSM1 when r = 4, while if the dataset has high rate of missing values, then it gave the best estimation to number of nearest neighbors by DRFFSM5 and in the second degree by DRFFSM3. After that, the missing value was estimated by LLS, and the results accuracy was measured by NRMSE and Pearson correlation. The smallest value of NRMSE for a given dataset is corresponding to DRF correlation function which is a better function for a given dataset. The highest value of PC for a given dataset is corresponding to DRF correlation function which is a better function for a given dataset.

A nifty collaborative analysis to predicting a novel tool (DRFLLS) for missing values estimation

Renewable energy becomes one of the main resources that help the world to safety the environment from pollution and provide the people of new type of energy; therefore, this paper presents model called multi-objectives renewable energy-generation (MORE-G) for generating electrical energy from the wind. In general, this model consists of five basic phases: in a first phase collecting and preparing the data, so to make it in format suitable for the decision-making stage, this phase split into multi-steps (i.e., handle missing values and normalization dataset), and the second phase focuses on building constraints for each dataset and develops one of the optimization algorithms called cuckoo based on horizontal combination and multi-objective optimization used in third phase to generate the energy. Another model is developed as multi-layer neural network called (DCapsNet) based on linear combination and multi-objective functions used in the fourth phase to generate the energy. Final phase is related to evaluation of both models (DCOM and DCapsNet) to determine the best. The MORE-G is characterized by addressing one of the real problems, saving on material costs (i.e., reducing the need for manpower and reducing dependence on other countries in importing electric power) and upgrading the scope of the ministry of electricity.

An Innovative synthesis of deep learning techniques (DCapsNet & DCOM) for generation electrical renewable energy from wind energy

/pdf/effect-of-ph-on-adsorption-and-photocatalytic-degradation-1ksl2fjrvb.pdf

Ayad F. Alkaim

Papers

Kinetics and equilibrium study for the adsorption of textile dyes on coconut shell activated carbon

Brookite versus anatase TiO2 photocatalysts: phase transformations and photocatalytic activities

A nifty collaborative analysis to predicting a novel tool (DRFLLS) for missing values estimation

An Innovative synthesis of deep learning techniques (DCapsNet & DCOM) for generation electrical renewable energy from wind energy

Effect of pH on adsorption and photocatalytic degradation efficiency of different catalysts on removal of methylene blue.