Superparamagnetic Multifunctionalized Chitosan Nanohybrids for Efficient Copper Adsorption: Comparative Performance, Stability, and Mechanism Insights.

To limit the dangers posed by Cu(II) pollution, chitosan-nanohybrid derivatives were developed for selective and rapid copper adsorption. A magnetic chitosan nanohybrid (r-MCS) was obtained via the co-precipitation nucleation of ferroferric oxide (Fe3O4) co-stabilized within chitosan, followed by further multifunctionalization with amine (diethylenetriamine) and amino acid moieties (alanine, cysteine, and serine types) to give the TA-type, A-type, C-type, and S-type, respectively. The physiochemical characteristics of the as-prepared adsorbents were thoroughly elucidated. The superparamagnetic Fe3O4 nanoparticles were mono-dispersed spherical shapes with typical sizes (~8.5-14.7 nm). The adsorption properties toward Cu(II) were compared, and the interaction behaviors were explained with XPS and FTIR analysis. The saturation adsorption capacities (in mmol.Cu.g-1) have the following order: TA-type (3.29) > C-type (1.92) > S-type (1.75) > A-type(1.70) > r-MCS (0.99) at optimal pH0 5.0. The adsorption was endothermic with fast kinetics (except TA-type was exothermic). Langmuir and pseudo-second-order equations fit well with the experimental data. The nanohybrids exhibit selective adsorption for Cu(II) from multicomponent solutions. These adsorbents show high durability over multiple cycles with desorption efficiency > 93% over six cycles using acidified thiourea. Ultimately, QSAR tools (quantitative structure-activity relationships) were employed to examine the relationship between essential metal properties and adsorbent sensitivities. Moreover, the adsorption process was described quantitatively, using a novel three-dimensional (3D) nonlinear mathematical model.

Current-voltage curves of planar heterojunction perovskite solar cells - Novel expressions based on Lambert W function and Special Trans Function Theory.

INTRODUCTION: At the present time, much attention has been focused on new types of solar cells, called perovskite solar cells. They are highly efficient devices with more than 25% power conversion efficiency. However, perovskite solar cell performance has not yet been fully explored. OBJECTIVES: We aimed to mathematically investigate the analytical modeling of current-voltage curves of planar heterojunction perovskite solar cells using Perovich Special Trans Function Theory (STFT). Furthermore, we proposed novel analytical closed-form solutions for short-circuit current and open-circuit voltage of these cells in terms of STFT. We evaluated the safety for laying the theoretical foundation by comparing the accuracy of the proposed expressions by the known methods. METHODS: A novel hybrid metaheuristic algorithm, called particle swarm optimization (PSO) - evaporation rate water cycle algorithm (ERWCA), is proposed to determine equivalent circuit parameters of the perovskite solar cell. A novel objective function is introduced for estimating the parameters for that purpose too. RESULTS: It was shown that STFT is very applicable and efficient for representing current-voltage expressions of perovskite solar cells. STFT provides a more accurate solution and requires fewer order members than the solutions provided by the conventional Taylor series. Based on these expressions and numerical calculations, it is verified that the characteristic values ​​of variables (short-circuit current, no-load voltage, efficiency, and fill factor) were not accurately calculated in the literature. Also, parameters of equivalent circuits of these cells were not accurately estimated. The equivalent circuit parameters were determined using the algorithm proposed in this work, which fit the verified values ​​of characteristic quantities much better than the literature. CONCLUSION: This work lays the foundation for developing the planar-structured perovskite solar cell models, in which the proposed estimation method and expressions are highly effective and provide excellent results.

LAGAM: A Length-Adaptive Genetic Algorithm With Markov Blanket for High-Dimensional Feature Selection in Classification.

Feature selection (FS) is an essential technique widely applied in data mining. Recent studies have shown that evolutionary computing (EC) is very promising for FS due to its powerful search capability. However, most existing EC-based FS methods use a length-fixed encoding to represent feature subsets. This inflexible encoding turns ineffective when high-dimension data are handled, because it results in a huge search space, as well as a large amount of training time and memory overhead. In this article, we propose a length-adaptive genetic algorithm with Markov blanket (LAGAM), which adopts a length-variable individual encoding and enables individuals to evolve in their own search space. In LAGAM, features are rearranged decreasingly based on their relevance, and an adaptive length changing operator is introduced, which extends or shortens an individual to guide it to explore in a better search space. Local search based on Markov blanket (MB) is embedded to further improve individuals. Experiments are conducted on 12 high-dimensional datasets and results reveal that LAGAM performs better than existing methods. Specifically, it achieves a higher classification accuracy by using fewer features.

Mesoporous Magnetic Cysteine Functionalized Chitosan Nanocomposite for Selective Uranyl Ions Sorption: Experimental, Structural Characterization, and Mechanistic Studies.

Nuclear power facilities are being expanded to satisfy expanding worldwide energy demand. Thus, uranium recovery from secondary resources has become a hot topic in terms of environmental protection and nuclear fuel conservation. Herein, a mesoporous biosorbent of a hybrid magnetic-chitosan nanocomposite functionalized with cysteine (Cys) was synthesized via subsequent heterogeneous nucleation for selectively enhanced uranyl ion (UO22+) sorption. Various analytical tools were used to confirm the mesoporous nanocomposite structural characteristics and confirm the synthetic route. The characteristics of the synthesized nanocomposite were as follows: superparamagnetic with saturation magnetization (MS: 25.81 emu/g), a specific surface area (SBET: 42.56 m2/g) with a unipore mesoporous structure, an amine content of ~2.43 mmol N/g, and a density of ~17.19/nm2. The experimental results showed that the sorption was highly efficient: for the isotherm fitted by the Langmuir equation, the maximum capacity was about 0.575 mmol U/g at pH range 3.5-5.0, and Temperature (25 ± 1 °C); further, there was excellent selectivity for UO22+, likely due to the chemical valent difference. The sorption process was fast (~50 min), simulated with the pseudo-second-order equation, and the sorption half-time (t1/2) was 3.86 min. The sophisticated spectroscopic studies (FTIR and XPS) revealed that the sorption mechanism was linked to complexation and ion exchange by interaction with S/N/O multiple functional groups. The sorption was exothermic, spontaneous, and governed by entropy change. Desorption and regeneration were carried out using an acidified urea solution (0.25 M) that was recycled for a minimum of six cycles, resulting in a sorption and desorption efficiency of over 91%. The as-synthesized nanocomposite's high stability, durability, and chemical resistivity were confirmed over multiple cycles using FTIR and leachability. Finally, the sorbent was efficiently tested for selective uranium sorption from multicomponent acidic simulated nuclear solution. Owing to such excellent performance, the Cys nanocomposite is greatly promising in the uranium recovery field.

Single Diode Solar Cells-Improved Model and Exact Current-Voltage Analytical Solution Based on Lambert's W Function.

There are three standard equivalent circuit models of solar cells in the literature-single-diode, double-diode, and triple-diode models. In this paper, first, a modified version of the single diode model, called the Improved Single Diode Model (ISDM), is presented. This modification is realized by adding resistance in series with the diode to enable better power loss dissipation representation. Second, the mathematical expression for the current-voltage relation of this circuit is derived in terms of Lambert's W function and solved by using the special trans function theory. Third, a novel hybrid algorithm for solar cell parameters estimation is proposed. The proposed algorithm, called SA-MRFO, is used for the parameter estimation of the standard single diode and improved single diode models. The proposed model's accuracy and the proposed algorithm's efficiency are tested on a standard RTC France solar cell and SOLAREX module MSX 60. Furthermore, the experimental verification of the proposed circuit and the proposed solar cell parameter estimation algorithm on a solar laboratory module is also realized. Based on all the results obtained, it is shown that the proposed circuit significantly improves current-voltage solar cell representation in comparison with the standard single diode model and many results in the literature on the double diode and triple diode models. Additionally, it is shown that the proposed algorithm is effective and outperforms many literature algorithms in terms of accuracy and convergence speed.

Catalytic Fast Pyrolysis of Soybean Straw Biomass for Glycolaldehyde-Rich Bio-oil Production and Subsequent Extraction.

In this study, soybean straw (SS) as a promising source of glycolaldehyde-rich bio-oil production and extraction was investigated. Proximate and ultimate analysis of SS was performed to examine the feasibility and suitability of SS for thermochemical conversion design. The effect of the co-catalyst (CaCl2 + ash) on glycolaldehyde concentration (%) was examined. Thermogravimetric-Fourier-transform infrared (TG-FTIR) analysis was applied to optimize the pyrolysis temperature and biomass-to-catalyst ratio for glycolaldehyde-rich bio-oil production. By TG-FTIR analysis, the highest glycolaldehyde concentration of 8.57% was obtained at 500 °C without the catalyst, while 12.76 and 13.56% were obtained with the catalyst at 500 °C for a 1:6 ratio of SS-to-CaCl2 and a 1:4 ratio of SS-to-ash, respectively. Meanwhile, the highest glycolaldehyde concentrations (%) determined by gas chromatography-mass spectrometry (GC-MS) analysis for bio-oils produced at 500 °C (without the catalyst), a 1:6 ratio of SS-to-CaCl2, and a 1:4 ratio of SS-to-ash were found to be 11.3, 17.1, and 16.8%, respectively. These outcomes were fully consistent with the TG-FTIR results. Moreover, the effect of temperature on product distribution was investigated, and the highest bio-oil yield was achieved at 500 °C as 56.1%. This research work aims to develop an environment-friendly extraction technique involving aqueous-based imitation for glycolaldehyde extraction with 23.6% yield. Meanwhile, proton nuclear magnetic resonance (1H NMR) analysis was used to confirm the purity of the extracted glycolaldehyde, which was found as 91%.