Hasil untuk "Chemical technology"

Qi You, Yan Sun, Feng Wang et al.

Understanding the solvation structure of electrolytes is critical for optimizing the electrochemical performance of rechargeable batteries, as it directly influences properties such as ionic conductivity, viscosity, and electrochemical stability. The highly complex structures and strong interactions in high-concentration electrolytes make accurate modeling and interpretation of their ``structure-property" relationships even more challenging with spectroscopic methods. In this study, we present a machine learning-based approach to predict dynamic $^7$Li NMR chemical shifts in LiFSI/DME electrolyte solutions. Additionally, we provide a comprehensive structural analysis to interpret the observed chemical shift behavior in our experiments, particularly the abrupt changes in $^7$Li chemical shifts at high concentrations. Using advanced modeling techniques, we quantitatively establish the relationship between molecular structure and NMR spectra, offering critical insights into solvation structure assignments. Our findings reveal the coexistence of two competing local solvation structures that shift in dominance as electrolyte concentration approaches the concentrated limit, leading to anomalous reverse of $^7$Li NMR chemical shift in our experiment. This work provides a detailed molecular-level understanding of the intricate solvation structures probed by NMR spectroscopy, leading the way for enhanced electrolyte design.

Eugenia Hyung, Juliana Cherston, Stein B. Jacobsen et al.

Cosmic spherules have largely been classified into S-, I-, and G-types according to their compositions, and are identified to have chondritic or achondritic materials as precursors. A recent recovery expedition attempted to sample fragments of the CNEOS 2014-01-08 bolide retrieved roughly 850 magnetic particles, some of which have unknown origins. Among those identified were a new group of highly differentiated materials consisting of close to 160 specimens categorized as "D-type" particles. We studied the D-type particles with the goal of comparing their various morphological features to their chemical compositional groupings. Four morphological classifications are considered: "scoriaceous," "stubby," "blocky," and "vesicular." The specimens from the "scoriaceous" and "stubby" groups exhibit a spinel/magnetite rim in at least one instance, characteristic of atmospheric entry, and textures indicative of quenching such as dendritic microcrystalline structures, suggesting that a subset of specimens from these groups are candidates for materials of extraterrestrial origin. The particles exhibiting "blocky" and "vesicular" textures are likely to be terrestrial in origin, with no obvious quench features or signs of ablation. The D-type particles identified and characterized in this study have a spectrum of terrestrial and probable extraterrestrial origins.

吴娟娟1，田佳宁1，陈奕蒙1，王成成1，王玉明1,2 , 姜晓明1, 薛长湖1, 张恬恬1 WU Juanjuan1, TIAN Jianing1, CHEN Yimeng1, WANG Chengcheng1, WANG Yuming1,2, JIANG Xiaoming1, XUE Changhu1, ZHANG Tiantian1

为了揭示雌、雄膨腹海马磷脂组成及其差异，用溶剂萃取法分别提取雌、雄膨腹海马脂质，冷丙酮提取其中的磷脂，进一步采用薄层色谱法和硅胶柱层析分离制备各磷脂组分，利用气相色谱技术分析比较雌、雄膨腹海马磷脂及其各组分的脂肪酸组成。结果表明：雌、雄膨腹海马的磷脂含量丰富，分别占总脂的18.11%和18.72%；膨腹海马磷脂包括磷脂酰乙醇胺、磷脂酰胆碱、溶血磷脂酰胆碱和鞘磷脂，其中磷脂酰胆碱含量最高；膨腹海马磷脂中共鉴定出20种脂肪酸，且不饱和脂肪酸含量高于饱和脂肪酸含量；雌、雄膨腹海马磷脂中海洋特征性n-3多不饱和脂肪酸DHA和EPA总含量分别为18.30%、19.61%；膨腹海马的磷脂酰乙醇胺、磷脂酰胆碱和溶血磷脂酰胆碱饱和脂肪酸都以C16∶ 0和C18∶ 0为主，而在鞘磷脂中以C14∶ 0和C16∶ 0为主；膨腹海马4种磷脂组分的单不饱和脂肪酸均以C18∶ 1为主，多不饱和脂肪酸均以EPA和DHA为主；除了雄性膨腹海马溶血磷脂酰胆碱外，其余磷脂组分DHA和EPA总含量均超过16%。综上，膨腹海马磷脂组成多样，海洋特征性n-3多不饱和脂肪酸含量丰富，具有较高的营养价值。 In order to reveal the phospholipids compositions and differences between male and female Hippocampus abdominalis, the lipids of female and male Hippocampus abdominalis were extracted by solvent extraction method, then phospholipids in the lipids were extracted by cold acetone. Moreover, the different phospholipids fractions were separated by thin-layer chromatography and silica gel column chromatography, then the fatty acid composition of phospholipids and the different phospholipids fractions from female and male Hippocampus abdominalis were analyzed by gas chromatography. The results showed that the female and male Hippocampus abdominalis were rich in phospholipids, accounting for 18.11% and 18.72% of the total lipids, respectively. The phospholipids of Hippocampus abdominalis included phosphatidylethanolamine, phosphatidylcholine, lysophosphatidylcholine and sphingomyelin, among which phosphatidylcholine was the most abundant. The phospholipids of Hippocampus abdominalis identified 20 kinds of fatty acids, and the content of unsaturated fatty acids was higher than that of saturated fatty acids. Furthermore, the total content of marine characteristic n-3 polyunsaturated fatty acids DHA and EPA in female and male Hippocampus abdominalis phospholipids were 18.30% and 19.61%, respectively. The saturated fatty acids of phosphatidylethanolamine, phosphatidylcholine and lysophosphatidylcholine in both male and female Hippocampus abdominalis were all dominated by C16∶ 0 and C18∶ 0, whereas those in sphingomyelin were dominated by C14∶ 0 and C16∶ 0. The monounsaturated fatty acids of the four phospholipid fractions of the male and female Hippocampus abdominalis were all dominated by C18∶ 1, and the polyunsaturated fatty acids were all dominated by EPA and DHA. The total contents of DHA and EPA were more than 16%, except for lysophosphatidylcholine in male Hippocampus abdominalis. In conclusion, phospholipids composition of Hippocampus abdominalis is diverse, with abundant marine characteristic n-3 polyunsaturated fatty acids and high nutritional value.

Mutairu O. Ajiboye, Ayodele A. Daniyan, Paul C. Okonkwo et al.

Abstract This study presents the first investigation of halogen-substituted aniline-derived Schiff bases (SB1, SB2, SB3) as corrosion inhibitors for mild steel in Nigerian tar sand environments. Key novelty includes introducing inhibition power as a new gravimetric-based performance metric for alkaline conditions where electrochemical methods are limited. Tar sand from Ilubirin was processed with 0.58 M NaOH at 90 °C for 24 h with inhibitors at concentrations of 25–150 ppm. Gravimetric analysis, SEM–EDS, and Langmuir isotherm modelling revealed a significant corrosion rate with effectiveness order SB3 > SB2 > SB1. SB3 achieved 94.4% inhibition efficiency at 150 ppm due to a favourable molecular structure promoting enhanced adsorption. Langmuir analysis confirmed chemisorption (ΔG°ads > − 20 kJ mol−1), while microstructural evaluation demonstrated excellent surface protection. This research demonstrates the effectiveness of inhibition power in assessing corrosion inhibitors using gravimetric data due to the limitations of electrochemical measurement in tar sand environments. The study concludes that Schiff-based compounds offer promising solutions for corrosion control in a harsh alkaline tar sand processing environment.

Niklas Manz, Yurij Holovatch, John Tyson

In this article we present and discuss the work and scientific legacy of Julian Hirniak, the Ukrainian chemist and physicist who published two articles in 1908 and 1911 about periodic chemical reactions. Over the last 110+ years, his theoretical work has often been cited favorably in connection with Alfred Lotka's theoretical model of an oscillating reaction system. Other authors have pointed out thermodynamic problems in Hirniak's reaction scheme. Based on English translations of his 1908 Ukrainian and 1911 German articles, we show that Hirniak's claim (that a cycle of inter-conversions of three chemical isomers in a closed reaction vessel can show damped periodic behavior) violates the \textit{Principle of Detailed Balance} (i.e., the Second Law of Thermodynamics), and that Hirniak was aware of this Principle. We also discuss his results in relation to Lotka's first model of damped oscillations in an open system of chemical reactions involving an auto-catalytic reaction operating far from equilibrium. Taking hints from both Hirniak and Lotka, we show that the mundane case of a kinase enzyme catalyzing the phosphorylation of a sugar can satisfy Hirniak's conditions for damped oscillations to its steady state flux (i.e., the Michaelis--Menten rate law), but that the oscillations are so highly damped as to be unobservable. Finally, we examine historical and factual misunderstandings related to Julian Hirniak and his publications.

Yujing Wei, Sibali Debnath, John L. Weber et al.

This study evaluates the precision of widely recognized quantum chemical methodologies, CCSD(T), DLPNO-CCSD(T) and localized ph-AFQMC, for determining the thermochemistry of main group elements. DLPNO-CCSD(T) and localized ph-AFQMC, which offer greater scalability compared to canonical CCSD(T), have emerged over the last decade as pivotal in producing precise benchmark chemical data. Our investigation includes closed-shell, neutral molecules, focusing on their heat of formation and atomization energy sourced from four specific small molecule datasets. Firstly, we selected molecules from the G2 and G3 datasets, noted for their reliable experimental heat of formation data. Additionally, we incorporate molecules from the W4-11 and W4-17 sets, which provide high-level theoretical reference values for atomization energy at 0 K. Our findings reveal that both DLPNO-CCSD(T) and ph-AFQMC methods are capable of achieving a root-mean-square deviation (RMSD) of less than 1 kcal/mol across the combined dataset, aligning with the threshold for chemical accuracy. Moreover, we make efforts to confine the maximum deviations within 2 kcal/mol, a degree of precision that significantly broadens the applicability of these methods in fields such as biology and materials science.

Lorenzo A. Mariano, Vu Ha Anh Nguyen, Valerio Briganti et al.

Magnetic anisotropy slows down magnetic relaxation and plays a prominent role in the design of permanent magnets. Coordination compounds of Co(II) in particular exhibit large magnetic anisotropy in the presence of low-coordination environments and have been used as single-molecule magnet prototypes. However, only a limited sampling of Cobalt's vast chemical space has been performed, potentially obscuring alternative chemical routes toward large magnetic anisotropy. Here we perform a computational high-throughput exploration of Co(II)'s chemical space in search of new single-molecule magnets. We automatically assemble a diverse set of about 15000 novel complexes of Co(II) and fully characterize them with multi-reference ab initio methods. More than 100 compounds exhibit magnetic anisotropy comparable to or larger than leading known compounds. The analysis of these results shows that compounds with record-breaking magnetic anisotropy can also be achieved with coordination four or higher, going beyond the established paradigm of two-coordinated linear complexes.

Eduardo Soares, Victor Shirasuna, Emilio Vital Brazil et al.

Large-scale pre-training methodologies for chemical language models represent a breakthrough in cheminformatics. These methods excel in tasks such as property prediction and molecule generation by learning contextualized representations of input tokens through self-supervised learning on large unlabeled corpora. Typically, this involves pre-training on unlabeled data followed by fine-tuning on specific tasks, reducing dependence on annotated datasets and broadening chemical language representation understanding. This paper introduces a large encoder-decoder chemical foundation models pre-trained on a curated dataset of 91 million SMILES samples sourced from PubChem, which is equivalent to 4 billion of molecular tokens. The proposed foundation model supports different complex tasks, including quantum property prediction, and offer flexibility with two main variants (289M and $8\times289M$). Our experiments across multiple benchmark datasets validate the capacity of the proposed model in providing state-of-the-art results for different tasks. We also provide a preliminary assessment of the compositionality of the embedding space as a prerequisite for the reasoning tasks. We demonstrate that the produced latent space is separable compared to the state-of-the-art with few-shot learning capabilities.

Atreyee Majumdar, Raghunathan Ramakrishnan

We embark on a quest to identify small molecules in the chemical space that can potentially violate Hund's rule. Utilizing twelve TDDFT approximations and the ADC(2) many-body method, we report the energies of S$_1$ and T$_1$ excited states of 12,880 closed-shell organic molecules within the bigQM7$ω$ dataset with up to 7 CONF atoms. In this comprehensive dataset, none of the molecules, in their minimum energy geometry, exhibit a negative S$_1$-T$_1$ energy gap at the ADC($2$) level while several molecules display values $<0.1$ eV. The spin-component-scaled double-hybrid method, SCS-PBE-QIDH, demonstrates the best agreement with ADC(2). Yet, at this level, a few molecules with a strained $sp^3$-N center turn out as false-positives with the S$_1$ state lower in energy than T$_1$. We investigate a prototypical cage molecule with an energy gap $<-0.2$ eV, which a closer examination revealed as another false positive. We conclude that in the chemical space of small closed-shell organic molecules, it is possible to identify geometric and electronic structural features giving rise to S$_1$-T$_1$ degeneracy; still, there is no evidence of a negative gap. We share the dataset generated for this study as a module, to facilitate seamless molecular discovery through data mining.

Feriehiwote Weldeyohanis Gebremariam, Eneyew Tadesse Melaku, Venkatesa Prabhu Sundramurthy et al.

To develop high quality cookies, even seemingly smallest changes depended on factors that can affect taste, texture, and nutritional value. In this light, this study aimed to investigate the upshot of refined wheat flour and pumpkin seed flour on properties of cookies such as antioxidant activity, thermal and oxidative stability. In view of the foregoing, the roasted pumpkin seeds of particle size below 500 μm were blended with wheat flour at different ratios (BR) to bake at selected pre-determined temperatures (T) and time durations (TD). The synergetic effect of aforesaid parameters on cookie development, BR, T, and TD was studied by varying the parameters between the range 6–15 %, 180–200 °C and from 8 to 12 min, respectively, for the baking process of cookies. Further, the process was modelled and scrutinized using numerical optimization to achieve a highly acceptable product. On that account, it was deduced that the optimal condition for BR, T, and TD were 12.87 %, 186 °C and 9.5 min, respectively, that could pave to beget the excellent quality cookies with overall acceptance score of 8, protein content 14.28 %, fat 17.85 %, ash 2.23 %, moisture 2.46 %, fiber 2.38 % and total color difference 12.01. The optimized cookies (OCs) were found to have higher protein (11.49–14.28 %), fiber (0.93–2.41 %), ash (2.19–1.77 %), total antioxidant activity (38.7158–43.1860 %), oxidative stability (28.61–51.24 h), Zn (1.42–2.63 mg/100g), and Fe (2.12–3.20 mg/100g) content as compared to the control. Laconically, the study results provided the optimized processing condition for developing high quality cookies with respect to improved nutritional value and comparable overall acceptability.

In-In Hanidah, Siti Nurhasanah, Sumanti Debby Moody et al.

Inulin is a polysaccharide composed of 2-60 fructose monomers linked by β-(2,1) glycosidic bonds, with a glucose end group. The tuber of Musa balbisiana Colla (batu banana) is a part of the M. balbisiana Colla plant that contains dietary fiber 6,20%. The polysaccharide content allowed the batu banana tubers  to contain inulin. The aim of this study was to determine the characteristics of inulin in batu banana tubers. The research method used was the experimental method, which was analyzed descriptively with two replicates at the Universitas Padjadjaran Laboratory of Jatinangor – Sumedang from May until October 2023. The parameters were inulin content, degree of polymerization (DP), reducing sugar content, moisture content, pH, solubility, water activity (aw), color L*, a*, and  b*, viscosity, and sensory analysis using a descriptive method. The results showed that B. tuber inulin had an inulin content of 3,58%, DP of 2,8, reducing sugar content of 2,03%,  moisture content of 8,47%, pH of 6,31, solubility of 21,70% (90°C), aw of 0,432, L* of 66,76, a* of 8,28, b* of 15,90, and viscosity of 2068 mPas (90°C). In conclusion, sensory analysis showed that batu banana tuber inulin has a darker color, bitter taste, stronger flavor, and softer texture than the commercial inulin.

Liang Anqi, Zeng Shuang, Ren Jiahang et al.

To improve the comprehensive utilization of regional energy and promote low-carbon development, this study constructs an integrated energy system for typical areas, such as parks, including a new energy power generation system driven by photovoltaic and wind power, heating and cooling energy supply systems for ground-source/air-source heat pumps, water chillers, and energy storage equipment. TRNSYS? software is used to simulate and study the dynamic characteristics of the system under six climate conditions in Beijing, and the game theory is used for intelligent operation, which is then compared with the logic control method. The results show that the logic control method can meet the load demand but cannot realize the efficient operation of the heat pump unit and the charge and discharge balance of the energy storage device. The integrated energy system after optimization via game theory can not only realize flexible energy scheduling and distribution through electric-thermal coordination, but also save the entire energy consumption of the heat pump unit and achieve the goal of regional energy economic benefits. The research presented in this paper provides an important theoretical basis for the intelligent operation of heat pump systems in integrated electric-thermal cooperative grids.

Maknunah Hilyatul, Wonorahardjo Surjani

Sensors play a crucial role in various fields by enabling the detection and analysis of a wide range of substances, including hazardous substance detection, environmental and food safety monitoring, pharmaceutical industry, gas analysis, and others. Research continues to identify and develop sensor matrix materials that can increase the sensitivity, selectivity and responsiveness of sensors. Silica, an oxide mineral is a potential matrix material for sensor applications because of its unique characteristics. It has a large pore structure and modifiable pore size distribution. Silica’s stable chemical properties, high-temperature resistance and corrosion resistance make it an ideal matrix material for a wide range of sensor applications. In recent years, silica cellulose also become a potential material for sensor applications. Silica cellulose is produced by combining silica with cellulose components from natural materials, such as rice husk ash, bamboo leaf ash, rice straw ash, and other plant fibers. This article provides a comprehensive exploration of various methods of synthesis and characterization of silica and silica cellulose materials. The methods include sol-gel, acid leaching, alkaline extraction, and other techniques for extracting cellulose from natural sources. In addition, sensor applications that have been tested using this material are also discussed, including its use in detecting molecular compounds, food and environmental applications. The development of silica and silica cellulose materials based on natural materials is considered because of their sustainability. By continuing to explore the potential of these materials, it is hoped that it can make a significant contribution in the development of sensor technology that is more innovative, environmentally friendly and sustainable.

Shufeng Kong, F. Ricci, D. Guevarra et al.

Machine learning for materials discovery has largely focused on predicting an individual scalar rather than multiple related properties, where spectral properties are an important example. Fundamental spectral properties include the phonon density of states (phDOS) and the electronic density of states (eDOS), which individually or collectively are the origins of a breadth of materials observables and functions. Building upon the success of graph attention networks for encoding crystalline materials, we introduce a probabilistic embedding generator specifically tailored to the prediction of spectral properties. Coupled with supervised contrastive learning, our materials-to-spectrum (Mat2Spec) model outperforms state-of-the-art methods for predicting ab initio phDOS and eDOS for crystalline materials. We demonstrate Mat2Spec’s ability to identify eDOS gaps below the Fermi energy, validating predictions with ab initio calculations and thereby discovering candidate thermoelectrics and transparent conductors. Mat2Spec is an exemplar framework for predicting spectral properties of materials via strategically incorporated machine learning techniques. Electrons and phonons give rise to important properties of materials. The machine learning framework Mat2Spec vastly accelerates their computational characterization, enabling discovery of materials for thermoelectrics and solar energy technologies.

Yun-Wen Mao, Roman V. Krems

We demonstrate accurate data-starved models of molecular properties for interpolation in chemical compound spaces with low-dimensional descriptors. Our starting point is based on three-dimensional, universal, physical descriptors derived from the properties of the distributions of the eigenvalues of Coulomb matrices. To account for the shape and composition of molecules, we combine these descriptors with six-dimensional features informed by the Gershgorin circle theorem. We use the nine-dimensional descriptors thus obtained for Gaussian process regression based on kernels with variable functional form, leading to extremely efficient, low-dimensional interpolation models. The resulting models trained with 100 molecules are able to predict the product of entropy and temperature ($S \times T$) and zero point vibrational energy (ZPVE) with the absolute error under 1 kcal mol$^{-1}$ for $> 78$ \% and under 1.3 kcal mol$^{-1}$ for $> 92$ \% of molecules in the test data. The test data comprises 20,000 molecules with complexity varying from three atoms to 29 atoms and the ranges of $S \times T$ and ZPVE covering 36 kcal mol$^{-1}$ and 161 kcal mol$^{-1}$, respectively. We also illustrate that the descriptors based on the Gershgorin circle theorem yield more accurate models of molecular entropy than those based on graph neural networks that explicitly account for the atomic connectivity of molecules.

S. M. Butorin, D. K. Shuh

The electronic structure and the chemical state in Am binary oxides and Am-doped UO$_2$ were studied by means of x-ray absorption spectroscopy at shallow Am core ($4d$ and $5d$) edges. In particular, the Am $5f$ states were probed and the nature of their bonding to the oxygen states was analyzed. The interpretation of the experimental data was supported by the Anderson impurity model (AIM) calculations which took into account the full multiplet structure due to the interaction between $5f$ electrons as well as the interaction with the core hole. The sensitivity of the branching ratio of the Am $4d_{3/2}$ and $4d_{5/2}$ x-ray absorption lines to the chemical state of Am was shown using Am binary oxides as reference systems. The observed ratio for Am-doped UO$_2$ suggests that at least at low Am concentrations, americium is in the Am(III) state in the UO$_2$ lattice. To confirm the validity of the applied AIM approach, the analysis of the Am $4f$ x-ray photoelectron spectra of AmO$_2$ and Am$_2$O$_3$ was also performed which revealed a good agreement between experiment and calculations. As a whole, AmO$_2$ can be classified as the charge-transfer compound with the $5f$ occupancy ($n_f$) equal to 5.73 electrons, while Am$_2$O$_3$ is rather a Mott-Hubbard system with $n_f$=6.05.

LIU Xiaoying, ZHANG Runfeng, PAN Yuxue, LI Huixue, SUN Yapeng, CHEN Shan

Curdlan has unique gel properties and special triple helix conformation, and curdlan gel has great application potential in the fields of food and biomedicine because of its water-retention capacity, thickening capacity, film-forming capacity, freeze-thaw stability, pH stability. Curdlan hydrogels with physicochemical properties and application fields can be prepared by different methods. In addition, combination with other substances can improve gel properties and impart unique functional properties to hydrogels. In this paper, the preparation methods, gel properties, and gelation mechanism of curdlan hydrogels, as well as recent progress in research on curdlan-based composite hydrogels are reviewed. Meanwhile, an overview of the application of curdlan-based hydrogels in food and biomedical fields is provided. This review is expected to provide a reference for further research and application of curdlan-based hydrogels.

Binfang Hu, Chunhua Zhang, Baijun Chu et al.

Key aroma components of 33 fragrant peanut oils with different aroma types were screened by combined using flavoromics and machine learning. A total of 108 volatile compounds were identified and 100 kinds of them were accurately quantified, and 38 compounds out of them were with odorant activity value ≥1. The 33 peanut oils presented varied intensity of ‘fresh peanuts’, ‘roasted nut’, ‘burnt’, ‘over-burnt’, ‘sweet’, ‘peanut butter-like’, ‘puffed food’ and ‘exotic flavor’, and could be classified into four aroma types, namely raw, light, thick and salty. Partial least squares regression analysis, random forest and classification regression tree revealed that 2-acetyl pyrazine had a negative effect on ‘fresh peanuts’ and could distinguish raw flavor samples well; 2-methylbutanal and 4-vinylguaiacol were key compounds of ‘roasted nut’ and had significant differences (P < 0.0001) in thick and raw flavor samples; furfural contributed to the ‘puffed food’ as well as key compound of salty flavor.

Yuhui Xiong, Sijie Ma, Xiaodong Hong et al.

Various thicknesses of TiO₂ films were prepared by the sol–gel method and spin-coating process. These prepared TiO₂ films exhibit thickness-dependent photoelectrochemical performance. The 1.09-μm-thickTiO₂ film with 20 spin-coating layers (TiO₂-20) exhibits the highest short circuit current of 0.21 mAcm⁻² and open circuit voltage of 0.58 V among all samples and exhibits a low PEC reaction energy barrier and fast kinetic process. Photoelectrocatalytic (PEC) degradation of methyl orange (MO) by TiO₂ films was carried out under UV light. The roles of bias, film thickness, pH value, and ion properties were systematically studied because they are the four most important factors dominating the PEC performance of TiO₂ films. The optimized values of bias, film thickness, and pH are 1.0 V, 1.09 μm, and 12, respectively, which were obtained according to the data of the PEC degradation of MO. The effect of ion properties on the PEC efficiency of TiO₂-20 was also analyzed by using halide as targeted ions. The “activated” halide ions significantly promoted the PEC efficiency and the order was determined as Br > Cl > F. The PEC efficiency increased with increasing Cl content, up until the optimized value of 30 × 10⁻³ M. Finally, a complete degradation of MO by TiO₂-20 was achieved in 1.5 h, with total optimization of the four factors: 1.0 V bias, 1.09-μm-thick, pH 12, and 30 × 10⁻³ M Cl ion content. The roles of reactive oxygen species and electric charge of photoelectrodes were also explored based on photoelectrochemical characterizations and membrane-separated reactors. Hydrogen peroxide, superoxide radical, and hydroxyl radical were found responsible for the decolorization of MO.

Andrés I. Bertoni, Cristián G. Sánchez

In this work we propose a chemically-informed data-driven approach to benchmark the approximate density-functional tight-binding (DFTB) excited state (ES) methods that are currently available within the DFTB+ suite. By taking advantage of the large volume of low-detail ES-data in the machine learning (ML) dataset, QM8, we were able to extract valuable insights regarding the limitations of the benchmarked methods in terms of the approximations made to the parent formalism, density-functional theory (DFT), while providing recommendations on how to overcome them. For this benchmark, we compared the first singlet-singlet vertical excitation energies ($E_1$) predicted by the DFTB-approximate methods with predictions of less approximate methods from the reference ML-dataset. For the nearly 21,800 organic molecules in the GDB-8 chemical space, we were able to identify clear trends in the $E_1$ prediction error distributions, with respect to second-order approximate coupled cluster (CC2), showing a strong dependence on chemical identity.