Simple Decomposition Research Articles

Facile Synthesis and Multiple Application of Ultralong-Afterglow Room Temperature Phosphorescence Aggregate Carbon Dots from Simple Raw Materials.

Owing to the ultralong afterglow, room temperature decay phosphorescence nanomaterials have aroused enough attention. In the work, by simple one-pot solid-state thermal decomposition reaction, aggregate carbon dots (CDs) was prepared from trimesic and boric acid. Based on the intermolecular hydrogen bonds and intramolecular π-π stacking weak interaction from precursors, CDs was encapsulated in boron oxide matrix and formed aggregation. The aggregate state of CDs facilitated the triplet excited states (Tn), which could induce the room temperature decay phosphorescence properties. By careful investigation, under different excitation wavelengths at 254 and 365nm, the aggregate CDs showed > 15s and > 3s room temperature phosphorescence emission in the naked eye, which was associated with 1516.12 ms and 718.62 ms lifetime respectively. And the aggregate CDs exhibited widespread application in encoding encryption, optical anti-counterfeiting and fingerprint identification etc. The interesting aggregate CDs revealed unexpected ultralong-afterglow room temperature decay phosphorescence properties and the work opened a window for constructing ultralong-afterglow room temperature decay phosphorescence aggregate CDs nanomaterials.

Multi-metal interaction boosts reconstructed FeCoCrCuOx@CF toward efficient alkaline water electrolysis under large current density

Electrolysis of water is one of most promising technologies for green hydrogen production. However, the green hydrogen has a market of 4 % only, as the key-component for the technology, electrocatalyst, is expensive, inefficient or unstable. Herein, we design a series of multi-metal oxides (FeCoOx@CF, FeCoCrOx@CF, FeCoCuOx@CF, and FeCoCrCuOx@CF) on cobalt foams to achieve the high efficiency, low cost, and long-term stability by a simple thermal decomposition and electrochemical activation. Among them, FeCoCrCuOx@CF shows remarkable catalytic activity with an ultra-low overpotential (40 mV at 10 mA cm−2) and Tafel slope (27.3 mV dec−1) for hydrogen evolution reaction (HER), which is superior to most reported transition-metal catalysts and comparable to commercial Pt/C, because of the synergistic effect of multiple metals and strong Cr-OH interaction on the reconstructed surface as induced by the dynamic dissolution and redeposition process in the reaction. In addition, the catalyst shows excellent long-term stabilities for HER and overall water splitting at 500 mA cm−2. Importantly, FeCoCrCuOx@CF has the excellent activity and high stability (100 h with only 2 % increment in applied voltage) in the industrial working environment (6 M KOH, ∼ 60 °C). Most importantly, only 3.18 V is needed to obtain > 30 A on a large size electrode for AWE (16.5 cm2, ∼ 1.84 A cm−2) in the industrial conditions. Our findings should provide novel strategies for the design of efficient and stable catalysts toward industrial water electrolysis.

Recovery of high‐purity‐4‐chloroguaiacol from bleaching wastewater by a heterogeneous extraction method

AbstractBACKGROUNDDuring the pulping and bleaching processes, 4‐chloroguaiacol accounts for a large proportion of chlorine containing pollutants. In this study, a heterogeneous extraction method for extracting 4‐chloroguaiacol from the bleaching wastewater by using the intermediate as the solid phase extraction agent is proposed.RESULTSThe solid intermediate with the molar ratio of 4‐chloroguaiacol to calcium 2:1 is firstly prepared. It is used for reacting with the crude 4‐chloroguaiacol in the bleaching wastewater to generate the end solid complex with the molar ratio of 4‐chloroguaiacol to calcium 4:1. It is verified to be that the high‐purity 4‐chloroguaiacol can be released from the simple thermal decomposition of the end solid complex.CONCLUSIONAt an optimum extraction condition including 30 °C reaction temperature, the pH value of 7, using the deionized water as the solvent, the extraction yield and the purity of 4‐chloroguaiacol reached 89.2% and 97.8%. Furthermore, after 15 cycle extraction times, the extraction rate of 4‐chloroguaiacol can still reach 86.3%. The extraction yield and the purity of 4‐chloroguaiacol reached 68.15% and 99.56% when the guaiacol content was lower than 25 mg/L at the bleaching wastewater. © 2023 Society of Chemical Industry (SCI).

Preparation and properties of Gd2O3–Eu2O3–ZrO2 ceramics as promising control rod material

Gray control rods enable the safe operation of nuclear reactors by controlling and adjusting nuclear reactivity, in which neutron-absorbing materials with low reactive worth loss play an important role. In this work, new types of Gd2O3–Eu2O3–ZrO2 ceramics used as neutron absorbers are successfully prepared by simple thermal decomposition following the air sintering method. The cross-sectional SEM images and density curves of ceramics are analyzed, and the ceramic with a content of 85 wt% Eu2O3 sintered at 1500 °C presents a higher density (7.193 g/cm3) than other samples. When the content of Eu2O3 is 70 wt%, the Vickers hardness of the sintered ceramic is 4.89 GPa, which is higher than 4.01 GPa of Eu2O3 (sintered at 1500 °C). This material, which is easy to prepare and has good properties, is regarded as a potential reactor control rod material.

New insights into the chemical activation of lignins and tannins using K2CO3—a combined thermoanalytical and structural study

Engineering of activated carbons (ACs) through chemical activation of organic precursors has been extensively studied for a wide variety of biopolymers, biomasses, wastes and other fossil-based precursors. Despite huge efforts to engineer evermore performant and sustainable ACs, “searching-for-the-best-recipe” type of studies are more the rule than the exception in the published literature. Emerging AC applications related to energy and gas storage require strict control of the AC properties and a better understanding of the fundamentals underlying their engineering. In this study, we provide new insights into the K2CO3 chemical activation of plant-based polyphenols—lignins and tannins—through careful thermoanalytical and structural analyses. We showed for the the first time that the reactivity of polyphenols during K2CO3 chemical activation depends remarkably on their purity and structural properties, such as their content of inorganics, OH functionalities and average molecular weight. We also found that the burn-off level is proportional to the K2CO3/lignin impregnation ratio (IR), but only within a certain range—high impregnation ratios are not needed, unlike often reported in the literature. Furthermore, we showed for the first time that the K2CO3 chemical activation of different carbon surfaces from lignins and tannins can be modelled using simple global solid-state decomposition kinetics. The identified activation energies lay in the range of values reported for heterogenous gas-carbon surface gasification reactions (O2-C, H2O-C, or CO2-C) in which the decomposition of C(O) surface complexes is the common rate-limiting step.

The impact of revenue diversification on profitability, capital, and risk in US banks by size

This study examines the influence of US banks’ revenue diversification on profitability, capital, and credit risk by size. By a simple decomposition of Return On Capital (ROC) I show how popular revenue diversification measures reflect both the ROC and risk-adjusted ROC. I find substantial differences between size groups concerning the impact of revenue diversification measures on: profitability, capital, and credit risk both in comparative statics and dynamically along the business cycles. Profitability, capital, and credit risk in medium size banks reflect insensitivity to these measures compared to other size groups; large and small alike. A similar ‘smile’ pattern has also been found regarding the respective pairwise conditional correlations between profitability, capital, and credit risk.

Blind video watermarking scheme for medical video authentication

Medical video watermarking is one of the beneficial and efficient tools to prohibit important patients' data from illicit enrollment and redistribution. In this paper, a new blind watermarking scheme has been proposed to improve the confidentiality, integrity, authenticity, and perceptual quality of a medical video with minimum distortion. The proposed scheme is based on 2D-DWT and dual Hessenberg-QR decomposition, where the input medical video is initially processed into frames. Then, the processed frames are transformed into sub-bands using 2D-DWT, followed by applying Hessenberg-QR decomposition on the selected wavelet HL2 sub-band. The watermark is scrambled via Arnold cat map to raise confidentiality and then concealed in the modified selected features. The watermark is extracted in a fully blind mode without referencing the original video, which reduces the extraction time. The proposed scheme maintained a fundamental tradeoff between robustness and visual imperceptibility compared to existing methods against many commonly encountered attacks. The visual imperceptibility has been evaluated using well-known metrics PSNR, SSIM, Q-index, and histogram analysis. The proposed scheme achieves a high PSNR value of (70.6899 dB) with minimal distortion and a high robustness level with an average NC value of (0.9998) and BER value of (0.0023) while conserving a large payload capacity. The obtained results show superior performance over similar video watermarking methods. The limitation of this scheme is the elapsed time during the embedding process since we utilized dual Hessenberg-QR decomposition. One possible solution to reduce time consumption is simple decompositions like bound-constrained SVM or similar decompositions.

The migrant pay gap in Spain: Where do the differences come from?

AbstractThis paper focuses on the migrant pay gap in Spain. Going beyond descriptive evidence of the differences between immigrants and nationals in terms of wages, we analyse which part of the gross wage is most affected by features that cannot be captured using econometric models. Relying on microdata from the Wage Structure Survey, we divide the total gross wage into two main parts: base wage and wage supplements. Then we decompose the migrant wage gap into the explained and the unexplained terms, using a simple decomposition methodology, the Oaxaca-Blinder model. Our results show that a part of the differences in wage supplements does not seem to be explained by the set of control variables introduced in the model and that this effect is more pronounced when only men are considered. These findings offer a new perspective on the migrant pay gap in Spain and point to the importance of wage-setting practices related to wage supplements in explaining (and widening) the total migrant pay gap in our country.

Machine Learning Seams of Conical Intersection: A Characteristic Polynomial Approach.

The machine learning of potential energy surfaces (PESs) has undergone rapid progress in recent years. The vast majority of this work, however, has been focused on the learning of ground state PESs. To reliably extend machine learning protocols to excited state PESs, the occurrence of seams of conical intersections between adiabatic electronic states must be correctly accounted for. This introduces a serious problem, for at such points, the adiabatic potentials are not differentiable to any order, complicating the application of standard machine learning methods. We show that this issue may be overcome by instead learning the coordinate-dependent coefficients of the characteristic polynomial of a simple decomposition of the potential matrix. We demonstrate that, through this approach, quantitatively accurate machine learning models of seams of conical intersection may be constructed.

Simultaneous Imaging and Photodynamic-Enhanced Photothermal Inhibition of Cancer Cells Using a Multifunctional System Combining Indocyanine Green and Polydopamine-Preloaded Upconversion Luminescent Nanoparticles.

This work introduces a novel multifunctional system called UPIPF (upconversion-polydopamine-indocyanine-polyethylene-folic) for upconversion luminescent (UCL) imaging of cancer cells using near-infrared (NIR) illumination. The system demonstrates efficient inhibition of human hepatoma (HepG2) cancer cells through a combination of NIR-triggered photodynamic therapy (PDT) and enhanced photothermal therapy (PTT). Initially, upconversion nanoparticles (UCNP) are synthesized using a simple thermal decomposition method. To improve their biocompatibility and aqueous dispersibility, polydopamine (PDA) is introduced to the UCNP via a ligand exchange technique. Indocyanine green (ICG) molecules are electrostatically attached to the surface of the UCNP-polydopamine (UCNP@PDAs) complex to enhance the PDT and PTT effects. Moreover, polyethylene glycol (PEG)-modified folic acid (FA) is incorporated into the UCNP-polydopamine-indocyanine-green (UCNP@PDA-ICGs) nanoparticles to enhance their targeting capability against cancer cells. The excellent UCL properties of these UCNP enable the final UCNP@PDA-ICG-PEG-FA nanoparticles (referred to as UPIPF) to serve as a potential candidate for efficient anticancer drug delivery, real-time imaging, and early diagnosis of cancer cells. Furthermore, the UPIPF system exhibits PDT-assisted PTT effects, providing a convenient approach for efficient cancer cell inhibition (more than 99% of cells are killed). The prepared UPIPF system shows promise for early diagnosis and simultaneous treatment of malignant cancers.

Hierarchical Clause Annotation: Building a Clause-Level Corpus for Semantic Parsing with Complex Sentences

Most natural-language-processing (NLP) tasks suffer performance degradation when encountering long complex sentences, such as semantic parsing, syntactic parsing, machine translation, and text summarization. Previous works addressed the issue with the intuition of decomposing complex sentences and linking simple ones, such as rhetorical-structure-theory (RST)-style discourse parsing, split-and-rephrase (SPRP), text simplification (TS), simple sentence decomposition (SSD), etc. However, these works are not applicable for semantic parsing such as abstract meaning representation (AMR) parsing and semantic dependency parsing due to misalignments with semantic relations and unavailabilities to preserve the original semantics. Following the same intuition and avoiding the deficiencies of previous works, we propose a novel framework, hierarchical clause annotation (HCA), for capturing clausal structures of complex sentences, based on the linguistic research of clause hierarchy. With the HCA framework, we annotated a large HCA corpus to explore the potentialities of integrating HCA structural features into semantic parsing with complex sentences. Moreover, we decomposed HCA into two subtasks, i.e., clause segmentation and clause parsing, and provide neural baseline models for more-silver annotations. In evaluating the proposed models on our manually annotated HCA dataset, the performances of clause segmentation and parsing resulted in 91.3% F1-scores and 88.5% Parseval scores, respectively. Due to the same model architectures employed, the performance differences of the clause/discourse segmentation and parsing subtasks was reflected in our HCA corpus and compared discourse corpora, where our sentences contained more segment units and fewer interrelations than those in the compared corpora.

Simple, Efficient, and Universal Energy Decomposition Analysis Method Based on Dispersion-Corrected Density Functional Theory.

Energy decomposition analysis (EDA) is an important class of methods to explore the nature of interaction between fragments in a chemical system. It can decompose the interaction energy into different physical components to understand the factors that play key roles in the interaction. This work proposes an EDA strategy based on dispersion-corrected density functional theory (DFT), called sobEDA. This method is fairly easy to implement and very universal. It can be used to study weak interactions, chemical bond interactions, open-shell systems, and interactions between multiple fragments. The total time consumption of sobEDA is only about twice that of conventional DFT single-point calculation for the entire system. This work also proposes a variant of the sobEDA method named sobEDAw, which is designed specifically for decomposing weak interaction energies. Through a proper combination of DFT correlation energy and dispersion correction term, sobEDAw gives a ratio between dispersion energy and electrostatic energy that is highly consistent with the symmetry-adapted perturbation theory, which is quite popular and robust in studying weak interactions but expensive. We present a shell script sobEDA.sh to implement the methods proposed in this work based on the very popular Gaussian quantum chemistry program and Multiwfn wavefunction analysis code. Via the script, theoretical chemists can use the sobEDA and sobEDAw methods very conveniently in their study. Through a series of examples, the rationality of the new methods and their implementation are verified, and their great practical values in the study of various chemical systems are demonstrated.

Simple synthesis and efficient photocatalytic hydrogen production of WO3-WS2 and WO3–WS2–MoS2

Facile preparation of efficient photocatalysts is one of the challenges to realizing the practical application of photocatalysis technology. In this paper, binary composite WO3-WS2 and ternary composite WO3–WS2–MoS2 were easily prepared through simple one-step acidification-thermal decomposition (abbreviated as AT) of ammonium tetrathiotungstate (ATT) and ammonium tetrathiomolybdate (ATM) just using the inevitable formation of WO3 during the acidification of ATT. Especially, their photocatalytic hydrogen production performances were determined and the reasons for their activity differences were investigated systematically based on XRD, SEM, TEM, and XPS characterizations. The results showed that on the whole, these two composites possess better performance than both pure MoS2 and WS2, as well as our previously reported WO3-WS2 (1637.8 μmol h−1 g−1). Besides, the average hydrogen evolution rate of WO3-WS2 (5998.6 μmol h−1 g−1) is higher than that of WO3–WS2–MoS2 (4907.2 μmol h−1 g−1). Moreover, for the ternary composites, when the content of W and Mo is close, the change in W to Mo ratio has no obvious impact on the hydrogen production performance. Finally, a performance comparison of different preparation methods manifests that the one-step AT method is the simplest and most effective. This work can offer useful guidance for the preparation and photocatalytic application of both WS2 and MoS2-based composites.

Complex Mapping between Neural Response Frequency and Linguistic Units in Natural Speech.

When listening to connected speech, human brain can extract multiple levels of linguistic units, such as syllables, words, and sentences. It has been hypothesized that the time scale of cortical activity encoding each linguistic unit is commensurate with the time scale of that linguistic unit in speech. Evidence for the hypothesis originally comes from studies using the frequency-tagging paradigm that presents each linguistic unit at a constant rate, and more recently extends to studies on natural speech. For natural speech, it is sometimes assumed that neural encoding of different levels of linguistic units is captured by the neural response tracking speech envelope in different frequency bands (e.g., around 1 Hz for phrases, around 2 Hz for words, and around 4 Hz for syllables). Here, we analyze the coherence between speech envelope and idealized responses, each of which tracks a single level of linguistic unit. Four units, that is, phones, syllables, words, and sentences, are separately considered. It is shown that the idealized phone-, syllable-, and word-tracking responses all correlate with the speech envelope both around 3-6 Hz and below ∼1 Hz. Further analyses reveal that the 1-Hz correlation mainly originates from the pauses in connected speech. The results here suggest that a simple frequency-domain decomposition of envelope-tracking activity cannot separate the neural responses to different linguistic units in natural speech.

Highly selective photocatalytic CO 2 reduction to ethylene in pure water by Nb 2O 5 nanoparticles with enriched surface –OH groups under simulated solar illumination

Photocatalytic CO₂ reduction to valuable chemical compounds could be a promising approach for carbon neutral practice. In this work, a simple and robust thermal decomposition process was developed with ammonium carbonate as both the precipitation agent and sacrificial template to produce fine Nb₂O₅ nanoparticles with a rich existence of surface -OH groups. It was found by Density Functional Theory (DFT) calculations and experiments that the rich existence of surface -OH groups enhanced the adsorption of both reactants (CO₂ and H₂O molecules) for the photocatalytic CO₂ reduction on these fine Nb₂O₅ nanoparticles, and a highly selective conversion of CO₂ to a high value chemical compound of ethylene (~ 68 mmol·g^-1·h^-1 with ~ 100% product selectivity) was achieved under simulated solar illumination without the usage of any sacrificial agents or noble metal cocatalysts. This synthesis process may also be readily applied as a surface engineering method to enrich the existence of surface -OH groups on various metal oxide-based photocatalysts for a broad range of technical applications.

Numerical simulations of flame spread in pine needle beds using simple thermal decomposition models

Computational fluid dynamics (CFD) models have increased in use for studying scenarios relevant to wildland fires, such as examination of the driving processes in flame spread in vegetative fuels. However, these tools utilize a complex set of submodels which require a large number of input parameters. Often the full set of fuel-specific parameters are not well-quantified and the user must rely upon the best available information. In this study, we examine the implications of using different simple models for thermal decomposition when simulating flame spread through a bed of dead pine needles in quiescent conditions. Model results using one set of common literature values are compared to those using data from milligram-scale characterizations of the fuel. An updated model for char oxidation is also implemented and tested. It was found that the literature values over-predicted mass loss rate by a factor of 2.4, while the fuel-specific values yielded predictions within the experimental uncertainty. A simple approach to decomposition modeling was also shown to be useful for investigating the role of bed structure on flame spread and the heat flux within the fuel bed.

The DeCAMFounder: nonlinear causal discovery in the presence of hidden variables

Abstract Many real-world decision-making tasks require learning causal relationships between a set of variables. Traditional causal discovery methods, however, require that all variables are observed, which is often not feasible in practical scenarios. Without additional assumptions about the unobserved variables, it is not possible to recover any causal relationships from observational data. Fortunately, in many applied settings, additional structure among the confounders can be expected. In particular, pervasive confounding is commonly encountered and has been utilised for consistent causal estimation in linear causal models. In this article, we present a provably consistent method to estimate causal relationships in the nonlinear, pervasive confounding setting. The core of our procedure relies on the ability to estimate the confounding variation through a simple spectral decomposition of the observed data matrix. We derive a DAG score function based on this insight, prove its consistency in recovering a correct ordering of the DAG, and empirically compare it to previous approaches. We demonstrate improved performance on both simulated and real datasets by explicitly accounting for both confounders and nonlinear effects.

Comparative analysis of new approximate analytical method and Mohand variational transform method for the solution of wave-like equations with variable coefficients

In a current article, two novel analytical approaches are compared for analytical analysis of wave like equations with variable coefficients, which describe the evolution of stochastic phenomena. One is the New Approximate Analytical Method which based on Caputo–Riemann operator with simple decomposition procedure. This new method directly provides a fractional order series form solution which fastly converge to exact form solution for integer order. The second is Mohand Variational Iteration Transform Method which base on iteration procedure with Mohand Transform. The Mohand Variational Iteration Transform Method provides series form solution without using any decomposition, discretization, and He’s polynomial. The solution in series form converges directly to the exact solution for integer orders. The comparative analysis validates that Mohand Variational Iterative Method has less computational work and simple procedure without using any decomposition, discretization, and He’s polynomial procedure as compared to New Approximate Analytical Method.

Efficient and Portable Einstein Summation in SQL

Computational problems ranging from artificial intelligence to physics require efficient computations of large tensor expressions. These tensor expressions can often be represented in Einstein notation. To evaluate tensor expressions in Einstein notation, that is, for the actual Einstein summation, usually external libraries are used. Surprisingly, Einstein summation operations on tensors fit well with fundamental SQL constructs. We show that by applying only four mapping rules and a simple decomposition scheme using common table expressions, large tensor expressions in Einstein notation can be translated to portable and efficient SQL code. The ability to execute large Einstein summation queries opens up new possibilities to process data within SQL. We demonstrate the power of Einstein summation queries on four use cases, namely querying triplestore data, solving Boolean satisfiability problems, performing inference in graphical models, and simulating quantum circuits. The performance of Einstein summation queries, however, depends on the query engine implemented in the database system. Therefore, supporting efficient Einstein summation computations in database systems presents new research challenges for the design and implementation of query engines.

Research on friction state monitoring method of liquid film seal during start-up based on acoustic emission

This paper proposes a method based on acoustic emission to achieve nondestructive testing of the friction state of a liquid film seal. However, the acoustic emission signal is susceptible to noise, and extracting the feature signal is challenging. To address these issues, we propose a signal processing method based on singular value decomposition and adaptive variational mode decomposition (SVD-AVMD). Additionally, we investigate the acoustic emission mechanism of the liquid film seal using the strength theory of shear strain energy and CEB contact model. We design a liquid film seal friction experiment based on acoustic emission technology to extract the characterization signal of the friction state of the liquid film seal using SVD-AVMD. Finally, we apply a convolutional neural network to identify the friction state of the liquid film seal. The results demonstrate that SVD-AVMD has a significantly better ability to capture the center frequency of each mode component and to recover each mode component compared to simple variational mode decomposition. SVD-AVMD can filter out background noise while retaining the most useful information to the maximum extent possible. Furthermore, the root mean square of the extracted AE signal is consistent with the description of the AE mechanism, achieving the goal of studying and judging the friction state of the liquid film seal end face. Finally, we show that combining the convolutional neural network and acoustic emission time-frequency characteristics can improve the recognition accuracy of the friction state of the liquid film seal.