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This study investigated the structural and electrochemical characteristics of binary and quaternary systems comprising nickel, cobalt, and iron selenides. The powders were obtained via a solvothermal route. X-ray diffraction (XRD) and Raman spectroscopy revealed significant phase diversity. It was observed that increasing the proportion of d-block metals in quaternary systems enhances structural entropy, potentially leading to more homogeneous and stable structures dominated by energetically preferred components such as nickel. The electrochemical analysis indicated that the binary system exhibited a reversible redox reaction, with nickel selenide-based samples demonstrating the highest electrochemically active surface area. Quaternary systems display varying degrees of electrochemical stability. An equal contribution of nickel, cobalt, and iron appears beneficial in achieving stable electrodes. This research contributes to understanding the relationship between transition metal selenides' structural, morphological, and electrochemical properties, providing insights into their potential applications in hydrogen generation.

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Analyzing multi-fluorophoric real systems without pre-processing is challenging, often addressed with unconventional fluorescence techniques and chemometrics. In this context, we introduce a novel addition - 'Angular Sweep Total Fluorescence Spectroscopy (ASTFS),' to the arsenal of multidimensional steady-state fluorescence spectroscopic techniques. ASTFS utilizes a series of variable angle synchronous fluorescence spectra, strategically covering the fluorescence region between the first and second-order Rayleigh scattering ridges. The plot features a minimal data matrix size, avoids Rayleigh scattering signals, and incurs no blind regions. The study delves into the instrumental configurations for spectral acquisition, highlights the enhanced spectral resolution due to the band-narrowing effect, and discusses other notable features of the ASTFS plot. Further, this technique is reported to be effective in analyzing analytes in complex systems with strong background fluorescence, such as milk. The antibiotic- Norfloxacin is quantified via minimal pre-processing in milk samples and yields excellent analytical figures of merit.

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Cleansing foam is a common multicomponent polymeric functional material. It contains ingredients in innumerable combinations, which makes formulation optimization challenging. In this study, we used artificial intelligence (AI) with machine learning to develop a cleansing capability prediction system that considers the effects of self-assembled structures and chemical properties of ingredients. Over 500 cleansing foam samples were prepared and tested. Molecular descriptors and Hansen solubility index were used to estimate the cleansing capabilities of each formulation set. We used five machine-learning models to predict the cleansing capability. In addition, we employed an in silico formulation by generating virtual formulations and predicting their cleansing capabilities using an established AI model. The achieved accuracy was R2 = 0.770. Our observations revealed that mixtures of cosmetic ingredients exhibit complex interactions, resulting in nonlinear behavior, which adds to the complexity of predicting cleansing performance. Nevertheless, accurate chemical property descriptors, along with the aid of in silico formulations, enabled the identification of potential ingredients. We anticipate that our system will efficiently predict the chemical properties of polymer-containing blends.

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Huangqi Guizhi Wuwu decoction (HGWD), as a classical formula, has been used for thousands of years in China. In this work, a comprehensive strategy was proposed for characterizing the chemical profile of HGWD based on online two-dimensional hydrophilic interaction and reversed-phase liquid chromatography coupled with hybrid linear ion trap-Orbitrap mass spectrometry (online HILIC × RP-ESI/HRMS/MSn). The compounds in HGWD were first separated by the combined use of an XBridge amide column (150 × 4.6 mm, 3.5 µm) and Accucore C18 column (50 mm × 4.6 mm, 2.6 µm). Modulation with assistant technology, including trap columns and online dilution, was optimized and developed to decrease potential analyte loss and improve the resolution of the system. Subsequently, the accurate mass was determined by high-resolution Orbitrap and MSn fragment data by a hybrid linear ion trap (LTQ). In total, 170 chemical constituents were unambiguously identified or tentatively characterized in both positive and negative ion modes. Our study demonstrated that the proposed online HILIC × RP system coupled to the LTQ-Orbitrap MS platform is an efficient analytical technique for characterizing the chemical profile of multicomponent systems.

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New particle formation (NPF) represents a significant source of aerosol particles in the atmosphere; however, the NPF mechanisms remain uncertain, hindering the understanding and assessment of its environmental effects. Hence, we investigated the nucleation mechanisms in multicomponent systems including two inorganic sulfonic acids (ISAs), two organic sulfonic acids (OSAs), and dimethylamine (DMA) by combining quantum chemical (QC) calculations and molecular dynamics (MD) simulations, and evaluated the comprehensive effect of ISAs and OSAs on DMA-driven NPF. The QC results showed that the (Acid)2(DMA)0-1 clusters were strongly stable, and the (ISA)2(DMA)1 clusters exhibited higher stability than the (OSA)2(DMA)1 clusters because ISAs (sulfuric and sulfamic acids) provided more H-bonds and stronger proton transfer than OSAs (methanesulfonic and ethanesulfonic acids). ISAs readily engaged in dimer formation, whereas the stability of trimer clusters was mainly regulated by the synergistic effects of ISAs and OSAs. OSAs participated in cluster growth earlier than ISAs. Our results revealed that ISAs promote cluster formation, whereas OSAs facilitate the growth of clusters. The synergistic effect of ISAs and OSAs should be further investigated in areas with high [OSAs]: [ISAs].

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When random-strength components work as an interconnected parallel system, then its carrying capacity is random as well. In a case where such a multicomponent system is a subject of the stepwise-growing workload, some of its components fail and their loads are taken over by the ones that are intact. When the loading process is continued, the additional loads trigger consecutive failures that degrade the system, eventually leading to a complete failure. If the goal of the system is to carry as much load as possible, then the loading process should be continued, but no longer than until the loading capacity of the whole system is reached. On the other hand, with every additional load step, a failure of the system becomes more probable, as the carrying capacity is random and known solely through its probability distribution. In such cases, the decision on when to cease the loading process is not obvious. We introduce and analyse a minimal model of failure spreading in an array of progressively loaded pillars controlled by a decision-maker who stops the process when a required load is attained. We show how to construct an optimal stopping rule. Under some additional assumptions regarding the adopted loss function, it is argued that the optimal stopping rule is of the threshold type and it significantly depends on the shape of the load-step probability distribution.

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Co-assembly of inorganic nanoparticles (NPs) and nanostructured polymer matrix represents an intricate interplay of enthalpic or entropic forces. Particle size largely affects the phase behavior of the nanocomposite. Theoretical studies indicate that new morphologies would emerge when the particles become comparable to the soft matrix's size, but this has rarely been supported experimentally. By designing a multicomponent blend composed of NPs, block copolymer-based supramolecules, and small molecules, a 3-D ordered lattice beyond the native BCP's morphology was recently reported when the particle is larger than the microdomain of BCP. The blend can accommodate various formulation variables. In this paper, when the particle size equals the microdomain size, a symmetry-broken phase appears in a narrow range of particle sizes and compositions, which we named the "train track" structure. In this phase, the NPs aligned into a 3-D hexagonal lattice and packed asymmetrically along the c axis, making the projection of the ac and the bc plane resemble train tracks. Computational studies show that the broken symmetry reduces the polymer chain deformation and stabilizes the metastable hexagonally perforated lamellar morphology. Given the mobility of the multicomponent blend, the system shows a self-sorting behavior: segregating into two macroscopic phases with different nanostructures based on only a few nanometers NP size differences. Smaller NPs form "train track" morphology, while larger NPs form a "simple hexagon" structure, where the NPs take a symmetric hexagonal arrangement. Detailed structural evolution and simulation studies confirm the systematic-wide cooperativity across different components, indicating the strong self-regulation of the multicomponent system.

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Amino acids are natural compounds that can be safely used in pharmaceutical applications. Considering the great interest in the amino acids used in the pharmaceutical industry, this article presents an overview of investigations reported in recent years. In this regard, the first sections begin with an introductory description of the properties, classification and safety of amino acids, while in the other sections the most common methods for the preparation of amino acids formulations and their application on solubilization, permeation and stabilization of several active pharmaceutical ingredients are described. Furthermore, available data about the multicomponent systems approach is included. Lastly, the impact of amino acids formulations on therapeutic efficacy is explored. The advantages illustrated suggest that amino acids are capable of improving the biopharmaceutical properties of drugs.

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The paper presents the photodegradation process of one-, two- and three-component dye mixtures by ZnO-SnO2 nanoparticles. After 60 min of running the processes, the dye removal efficiencies of 76.44, 72.69, 62.43, 77.00 and 92.46% for MB, RB, TB, MO and YQ degradation, respectively, were obtained. For binary and ternary systems, dye removal efficiencies for all cases exceeded 70%. When the binary and ternary dye mixtures were tested, the photodegradation efficiencies of ZnO-SnO2 were similar to those of the single mixtures, indicating that this material could be used in industrial applications in the future. The focus of the study was to investigate the effect of sorption on photodegradation efficiency and the presence of both cationic and anionic dyes on their degradation efficiency under UV light. The significance of the effect of sorption on the degradation efficiency allowing the interaction of the catalyst with the dyes removed was confirmed. The main factor influencing sorption and consequently photocatalysis was the nature of the dye. It was confirmed that the positively charged ZnO-SnO2 surface effectively sorbs the dyes and causes their degradation.

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Although significant progress has been made in the self-assembly of nanostructures, present successes heavily rely on precision in building block design, composition, and pair interactions. These requirements fundamentally limit our ability to synthesize macroscopic materials where the likelihood of impurity inclusion escalates and, more importantly, to access molecular-to-nanoscopic-to-microscopic-to-macroscopic hierarchies, since the types and compositions of building blocks vary at each stage. Inspired by biological blends and high-entropy alloys, we hypothesize that diversifying the blend's composition can overcome these limitations. Increasing the number of components increases mixing entropy, leading to the dispersion of different components and, as a result, enhances interphase miscibility, weakens the dependence on specific pair interactions, and enables long-range cooperativity. This hypothesis is validated in complex blends containing small molecules, block copolymer-based supramolecules, and nanoparticles/colloidal particles. Hierarchically structured composites can be obtained with formulation flexibility in the filler selection and blend composition. It is worth noting that, by adding small molecules, we can solve the size constraint that plagues traditional block copolymer/nanoparticle blends. Detailed characterization and simulation further confirm that each component is distributed to locally mediate unfavorable interactions, cooperatively mitigate composition fluctuations, and retain structural fidelity. Furthermore, the blends have sufficient mobility to access tunable microstructures without compromising the order of the nanostructure. Besides establishing a kinetically viable pathway to release current constraints in the composite design and to navigate uncertainties during structure formation over multiple length scales, the present study demonstrates that entropy-driven behaviors can be realized in systems beyond high-entropy alloys despite inherent differences between metal alloys and organic/inorganic hybrids.

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Layered Double Hydroxides (LDH) have received great attention in the development of drug carrier systems. LDHs have become intelligent excipients of high technological potential for the pharmaceutical industry due to their ability to intercalate biomaterials in the interlayer region, adsorb substances on its vast surface area, have flexible structure, swelling properties, high chemical and thermal stability, modulate drug release, have high biocompatibility and be easily synthesized. This article, using typical examples, mainly addresses the systems formed between LDHs and antimicrobial, antineoplastic and anti-inflammatory agents, which constitute the main pharmacological classes of wide interest due to the problems encountered with low solubility, control in administration, stability in body fluids and toxicity, among others. Additionally, the article also reports on the recent development of ternary or quaternary (multicomponent systems) compounds based on LDH, bringing the advantages of targeted therapy, improving the aqueous stability of nanohybrids and the performance of these inorganic carriers.

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We developed a robust ternary PdO-CeO2-OMS-2 catalyst with excellent catalytic performance in the selective reduction of NO with CO using a strategy based on combining components that synergistically interact leading to an effective abatement of these toxic gases. The catalyst affords 100% selectivity to N2 at the nearly full conversion of NO and CO at 250 °C, high stability in the presence of H2O, and a remarkable SO2 tolerance. To unravel the origin of the excellent catalytic performance, the structural and chemical properties of the PdO-CeO2-OMS-2 nanocomposite were analyzed in the as-prepared and used state of the catalyst, employing a series of pertinent characterization methods and specific catalytic tests. The experimental as well as theoretical results, based on density-functional theory calculations suggest that CO and NO follow different reaction pathways, CO is preferentially adsorbed and oxidized at Pd sites (PdII and Pd0), while NO decomposes on the ceria surface. Lattice oxygen vacancies at the interfacial perimeter of PdO-CeO2 and PdO-OMS-2, and the diffusion of oxygen and oxygen vacancies are proposed to play a critical role in this multicenter reaction system.

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The biomass, bottlebrush flower, is exploited for the preparation of functionalized porous carbons by one-pot thermal activation using NaHCO3 and dicyandiamide. An intensified cross-linking effect among the precursors boosts pore (especially mesopore) formation in the pyrolysis process, producing N-doped porous carbons (NPCs) with a large specific surface area (SSA, up to 2025 m2 g-1). The biomass-derived carbon samples turn out to be highly effective in adsorption, and catalytic activation of peroxymonosulfate for degradation of aqueous phenol and p-hydroxybenzoic acid (HBA) in single and binary systems. The effects of N content, porous structure, and trace Ni species on the adsorptive and catalytic behavior of carbon are investigated. It is found that the porous structure plays a more critical role in adsorption than surface N functionality, while the contributions of various reactive species for phenol and HBA degradation are different.

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Nano-cryptomelane was prepared and characterized using SEM with mapping, HRTEM, FT-IR spectra, thermal analysis and surface area. The diameter distribution of nano-cryptomelane was found to be 4-6 nm. Sorption performance of the prepared material was studied for the removal of Co2+, Cs+ and Pb2+ from a multi-system solution of equal molar ratio, 1:1:1. The sorption capacity of nano-cryptomelane was found to be 179.6, 442.6 and 716.9 mg/g for Co2+, Cs+ and Pb2+, respectively. The kinetic studies revealed that the sorption process obeys non-linear pseudo-second-order model and is controlled by an intra-particle diffusion mechanism. The equilibrium isotherm investigations outlined that the extended Langmuir isotherm model fits the data reasonably well and it is more applicable than Freundlich multicomponent sorption isotherm. The value of diffusion coefficient for the three metal ions is in the order 10-17 m2/s which indicates the chemisorption nature of the process. The desorption percentage attains the maximum value (98.13%, 97.29 and 97.04 for lead, cesium and cobalt ions, respectively) using 0.7 mol/L of HNO3. This revealed that nano-cryptomelane can be regenerated and reused for farther sorption of Pb2+, Cs+ and Co2+ from wastewater.

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This study evaluated the combined effect of Cd(II), Cu(II), Ni(II), Fe(III), Pb(II) and Zn(II) on each other removal by anaerobic biomass under sulfate reducing condition. Statistically valid Plackett-Burman design of experiments was employed to carry out this mixture study. The results obtained showed a maximum removal of Cu(II) (98.9%), followed by Ni(II) (97%), Cd(II) (94.8%), Zn(II) (94.6%), Pb(II) (94.4%) and Fe(III) (93.9%). Analysis of variance (ANOVA) of the sulfate and chemical oxygen demand (COD) reduction revealed that the effect due to copper was highly significant (P value<0.05) on sulfate and COD removal. To establish the role of sulfate reducing bacteria (SRB) in the metal removal process, surface morphology and composition of the metal loaded biomass were analyzed by transmission electron microscopy (TEM) equipped with energy dispersive spectroscopy (EDS) and by field emission scanning electron microscopy (FESEM) integrated with energy dispersive X-ray spectroscopy (EDX). The results obtained revealed that the metal precipitates are associated with the outer and inner cell surface of the SRB as a result of the sulfide generated by SRB.

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A heterogeneous supramolecular catalytic system for multicomponent aldol-'click' reactions is reported. The copper(I) metallohydrogel functionalized with a phenyltriazole fragment was able to catalyze the multicomponent reaction between phenylacetylene, p-nitrobenzaldehyde, and an azide containing a ketone moiety, obtaining the corresponding aldol products in good yields. A possible mechanistic pathway responsible for this unexpected catalytic behavior has been proposed.

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Fusion protein construction is a widely employed biochemical technique, especially when it comes to multi-component enzymes such as cytochrome P450s. Here we describe a novel method for generating fusion proteins with variable linker lengths, protein fusion with variable linker insertion (P-LinK), which was validated by fusing P450cin monooxygenase (CinA) to the flavodoxin shuttle protein (CinC). CinC was fused to the C terminus of CinA through a series of 16 amino acid linkers of different lengths in a single experiment employing 3 PCR amplifications. Screening for 2-ß-hydroxy-1,8-cineole production by CinA-CinC fusion proteins revealed that enzymatically active variants possessed linker lengths of more than 5 amino acids, reaching optimum enzyme activity at a linker length of 10 amino acids. Our P-LinK method not only minimizes experimental effort and significantly reduces time demands but also requires only a single cloning and transformation step in order to generate multiple linker variants (1 to 16 amino acids long), making the approach technically simple and robust.

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A method is described to determine vapor pressures of compounds in multicomponent systems simultaneously. The method is based on temperature-gradient analysis by comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GCxGC-TOFMS). Vapor pressures are determined with the aid of known vapor pressure values of reference compounds eluting before and after the analytes. Reference compounds with the same functionalities as the analytes are preferred, but when these are not available, the alkane series can be utilized. The number of compounds whose vapor pressures can be determined is limited only by the peak capacity of the chromatographic system. Although the lowest subcooled vapor pressure determined was 0.006 Pa, for tetrahydroaraucarolone in an atmospheric aerosol sample, vapor pressures as low as 10(-6) Pa can be measured with the described set-up. Even lower values can be measured with higher GC temperatures and longer analysis times. Since only a few picograms of compound is required, in a mixture of any complexity, the GCxGC-TOFMS method offers unique sensitivity, rapidity, and comprehensiveness.