Suitable Reactions Research Articles

Assessment of Edge Modification of Nanographene.

Carboxy groups on the edges of nanographene (NG) enable functionalization for realizing NG-organic hybrid materials. Therefore, assessment of the edge-functionalization of the electronic structures of NGs is valuable for the rational design of functional carbon materials. In this study, the structures of model NGs comprising 174 carbon atoms with armchair edges and various functional groups at the edges were computed. To achieve the greatest possible similarity between the computed structure and the real one, the carbon framework was designed based on experimental observations. The functional groups can be accessed via suitable chemical reactions. The computations predicted that although the conversion of carboxyl groups with electron-withdrawing/donating groups influences the orbital energies, the HOMO-LUMO (H-L) gap is not significantly affected, except in a few cases. Among the evaluated examples, π-extension had the greatest influence on the H-L gap. Interestingly, for the Pd2+-coordinated NG, the participation of the low-lying LUMO localized on Pd2+ in the surface-to-metal transitions seemingly narrowed the H-L gap, and a surface-to-ligand transition was observed.

Accelerating the production of formate radicals for nitrate purification via a redox-regulated photocatalysis route

The treatment of nitrate (NO3−) pollutants is crucial for human health in the context of environmental pollution. Synergistic photocatalytic redox technology is feasible for reducing low-concentrated NO3− to achieve environmental purification. As combined redox reactions proceed in one photocatalysis system, incorporating suitable oxidative reactions is one potential strategy to enhance photocatalytic NO3− reduction efficiency. Herein, almost 100 % removal efficiency of NO3− and superior N2 selectivity of 93.99±0.45 % is achieved through the precise matching of synergistic oxidative half-reactions. The crucial role of active radicals is revealed. Specifically, the conversion efficiency of NO3− is determined by the cooperation of formate radicals (•CO2−), which are generated from the selective oxidation of formaldehyde, acetaldehyde or glyoxal. Among them, the optimum yield of •CO2− radicals are reached from the formaldehyde oxidative reaction. It is clarified that this research will contribute to a deep understanding of the removal and purification of NO3− in wastewater.

Efficient Reaction‐Based Approaches for Gas‐Phase Enthalpy of Formation Prediction and their Application to Large (C32) Polycyclic Aromatic Hydrocarbons

AbstractIn this work, an efficient and generally applicable scheme for the automatic generation of the minimal set of independent model reactions to be used for the calculation of enthalpies of formation is presented. A post‐processing procedure targeting the selection of the most suitable model reactions by assigning them larger weights is suggested. The developed computational protocol exploiting high‐level ab initio calculations and accurate reference enthalpies of formation retrieved from the Active Thermochemical Tables reproduces with chemical accuracy well‐established enthalpies of formation of 15 relatively small (C10–C24) polycyclic aromatic hydrocarbons (PAHs). A promising alternative single‐reaction strategy encompassing all reference species is outlined. Both methods are then applied to predict for the set of 43 larger (C32) PAHs, and revealed significant deviation (Mean Unsigned Error, MUE = 23.2 kJ mol−1) compared to the earlier theoretical results obtained from B3LYP calculations with subsequent group‐based empirical corrections. In the absence of the experimental data, these evaluations of are expected to be more realistic as the approach employed in this work demonstrated better performance on the benchmarking set of 15 smaller PAHs with the MUE of 1.3–1.5 versus 6.2 kJ mol−1.

Magnetic-gold nanoparticle-mediated paper-based biosensor for highly sensitive colorimetric detection of food adulteration

Food adulteration presents a significant challenge due to the evasion of legal oversight and the difficulty of identification. Addressing this issue, there is an urgent need for on-site, rapid, visually based small-scale equipment, along with large-scale screening technology, to enable prompt results without providing opportunities for dishonest traders to react. Colorimetric reactions offer advantages in terms of speed, visualization, and miniaturization. However, there is a scarcity of suitable colorimetric reactions for food adulteration detection, and interference from colored food impurities and easily comparable color results affects accuracy. To overcome limitations, this study introduces a novel approach utilizing polydopamine magnetic nanoparticles to enrich DNA in food samples, effectively eliminating interfering components. By employing gold nanoparticles to generate magnetic-gold nanoparticles, a single magnetic bead achieves simultaneous enrichment, impurity removal, and detection. The use of paper-based biosensors and visualization equipment allows for the visualization and digital analysis of results, achieving a low detection limit of 4.59 nmol mL−1. The method also exhibits high accuracy and repeatability, with a RSD ranging from 1.6 % to 4.0 %. This innovative colorimetric method addresses the need for rapid, miniaturized, and large-scale detection, thus providing a solution for food adulteration challenges.

Psychometric Test for Medical Consultancy System

Abstract: Preliminary investigations delve into the possibility of employing chatbots in psychiatric therapy to aid behavior modification through assessments and chatbot interventions. Nevertheless, these platforms overlook the mental conditions of users, neglecting interviews, continuous surveillance, or ethical factors. We propose that enhancing emotion detection could boost user contentment among individuals seeking mental health assistance. Our suggestion encompasses an interactive psychiatric counseling service employing sophisticated natural language comprehension and multimodal emotion detection. This method permits ongoing and delicate observation of emotional fluctuations. Moreover, an instance-oriented reaction model, integrating moral discernment, facilitates suitable clinical reactions in psychiatric consultations.

Recent Developments in Copper‐Based Catalysts for Enhanced Electrochemical CO2 Reduction

AbstractThe drastic climate change imposing adverse environmental effects receives serious research attention for finding a suitable solution. The replacement of conventional fossil energy sources with renewable and sustainable energy sources is the potential route; and thus, manifests as a viable solution. Accordingly, the electrocatalytic carbon dioxide (CO2) reduction process coupled with the renewable energy source is an emerging strategy for adopting a sustainable approach. However, the existing challenges in designing suitable catalyst, support material, electrolyte, inadequate selectivity, and intermediate reactions of CO2 reduction demand substantial research advancement. Numerous studies reported for the CO2 reduction process highlight the importance of catalyst design and product selectivity. Importantly, the copper‐based catalysts, capable in the output of multi‐carbon products, are reported as a “star” material. This review; therefore, focuses on catalyst design strategies, unique structural/morphological features, and product selectivity of diverse copper‐based catalysts. The outstanding findings of copper‐based catalysts and the corresponding products are critically discussed with adequate figures of merits. The impact of structural/morphological features on product selectivity is discussed in detail. The future scope and author perspectives on copper‐based catalysts for the feasible electrocatalytic CO2 reduction application are summarized.

Total Synthesis of Four Classes of Daphniphyllum Alkaloids.

The macrodaphniphyllamine-type, calyciphylline A-type, daphnilongeranin A-type, and daphnicyclidin D-type alkaloids are four structurally related classes of Daphniphyllum alkaloids. On the basis of a systematic analysis of the biogenetic network of these classes, we developed synthetic strategies centered on the C4-N and C1-C8 bonds of calyciphylline A, which took full advantage of the suitable substrates, reactions, and pathways that are altered from their counterparts in the postulated biogenetic network. Through this generalized biomimetic approach, we achieved the first synthesis of 14 Daphniphyllum alkaloids from the four subfamilies.

Efficient purification of a nitrate and chlorate mixture in water via photoredox activated intermediate coupling-decoupling pathway

Nitrate (NO3-) is a widespread contaminant that threatens human health and ecological safety. Meanwhile, the disinfection byproducts chlorate (ClO3-) is generated inevitably in conventional wastewater treatment. Therefore, the contaminants mixture of NO3- and ClO3- are universal in common emission units. Photocatalysis technology is a feasible approach for the synergistic abatement of contaminant mixture, where matching suitable oxidation reactions is a potential strategy to improve the photocatalytic reduction reactions. Herein, formate (HCOOH) oxidation is introduced to facilitate the photocatalytic reduction of the NO3- and ClO3- mixture. As a result, high purification efficiency of NO3- and ClO3- mixture are achieved, evidenced by 84.6% e--dependent removal of the mixture at a reaction time of 30 min, with 94.5% N2 selectivity and 100% Cl- selectivity, respectively. Specifically, by the close combination of in-situ characterizations and theoretical calculations, the detailed reaction mechanism is revealed, in which the intermediate coupling-decoupling route from NO3- reduction and HCOOH oxidation is established by the chlorate-induced photoredox activation, leading to the significantly enhanced efficiency for the wastewater mixture purification. The practical application of this pathway is established for simulated wastewater to show its wide applicability. This work provides new insights into photoredox catalysis technology for its environmental application.

Natural adsorbent of corncob (Zea mays L.) powder capability in purification of used cooking oil with shaking operation based on turbidity

In the purification process of used cooking oil, an adsorption technique utilizing corncob as the adsorbent can be applied. To optimize the purification, the shaking speed, adsorption time, and turbidity vary. Therefore, this study was conducted in batch operation to determine the effect of suitable adsorption and reactions on shaking speed with variations in mass, size, and the contact time of the adsorbent for 5 h. During the experiment, the used cooking oil was subjected to the adsorption process using 100 mL of adsorbent, with mass variations of 2 g, 3 g, and 4 g. The surface morphology of the adsorbent was characterized by modeling corncob and the oil components of chemical compounds contained in the used cooking oil were examined before and after purification. The parameters observed were measured using Scanning Electron Microscope-Energy Dispersive X-Ray (SEM-EDX), Gas Chromatography-Mass Spectrometry (GCMS), Fourier-Transform Infrared Spectroscopy (FTIR), and Brunauer-Emmett-Teller (BET). The results showed that the adsorbent with a size of 100 mesh, a shaking speed of 140 rpm, and a mass of 4 g exhibited a higher surface area and greater adsorption capacity. Furthermore, the adsorbent also influenced the turbidity value of the used cooking oil, and prolonged contact between the adsorbent and oil led to an increase in turbidity, particularly when using adsorbents of sizes 50 and 70 mesh.

A Human-Centered and Adaptive Robotic System Using Deep Learning and Adaptive Predictive Controllers

The rise of single-person households coupled with a drop in social interaction due to the coronavirus disease 2019 (COVID-19) pandemic is triggering a loneliness pandemic. This social issue is producing mental health conditions (e.g., depression and stress) not only in the elderly population but also in young adults. In this context, social robots emerge as human-centered robotics technology that can potentially reduce mental health distress produced by social isolation. However, current robotics systems still do not reach a sufficient communication level to produce an effective coexistence with humans. This paper contributes to the ongoing efforts to produce a more seamless human-robot interaction. For this, we present a novel cognitive architecture that uses (i) deep learning methods for mood recognition from visual and voice modalities, (ii) personality and mood models for adaptation of robot behaviors, and (iii) adaptive generalized predictive controllers (AGPC) to produce suitable robot reactions. Experimental results indicate that our proposed system influenced people’s moods, potentially reducing stress levels during human-robot interaction.

Effect of Magnesium as Biomaterial in Biodegrdation

Since many years ago, the study of engineering materials—which includes metal alloys, composites, polymers, ceramics, and metallic glasses—has advanced significantly, particularly in the area of biomaterials. Due to their excellent biocompatibility and unique mechanical qualities, alloys derived on magnesium (Mg) now have a wide array of uses in the biomedical sector. By combining Mg with other elements including nanoparticles, polymers, Zinc, Yttrium and Manganese are included with capability to perform suitable reactions. Addictive manufacturing allows the porosity of the scaffolds to be adjusted, allowing for fine-tuning of the scaffold degradation performance and cell response. Specialized equipment will be required to print magnesium in an inert atmosphere while maintaining safe material handling. As a result, due to inherent characteristics of magnesium mixes, such as a higher vapour pressure and a stronger inclination to oxidize, the additive manufacture of disposable magnesium-based implants presents certain challenges. Inadvertent degradation, structural failure, hydrogen evolution, alkalization, and cytotoxicity are caused by the high corrosion rate of Mg-based implants. A number of methods, such as the design of a magnesium alloy and the alteration of the surface properties, are employed to tailor the degradation rate since it is envisaged that biodegradable Mg-based implants will demonstrate regulated degradation and satisfy the needs of specific applications. Biodegradation mechanism along with its controlling aspects and improvement measures with respect to biodegradation are reviewed in this paper.

Thermal properties of spark plasma sintered Inconel 625 modified by titanium zirconium mixed carbide

Nickel-based superalloys are an important group of advanced engineering materials used for high-temperature and/or corrosive environments. Lately, in order to improve their mechanical properties, carbide reinforcement is added to a metal matrix, creating composites of improved hardness, shear resistance, or tensile resistance. In order to preserve the wide applicability of those materials, thermal properties, and corrosion resistance must also be evaluated. Presented research evaluated the thermal properties of Inconel 625—(Ti,Zr)C0.85 composites obtained at 1000 °C by spark plasma sintering process. Sinters containing 5–20 mass% of carbide phases were initially characterized in terms of their density, phase composition, and microstructure. A thorough examination of their thermal properties included: dilatometry, laser flash analysis, and differential scanning calorimetry. Conducted experiments revealed that a growing amount of carbide reinforcement decreased the thermal diffusivity and conductivity of samples. Other materials had a nonlinear correlation with that parameter. Performed analysis allowed to make an initial evaluation of oxidation resistance at 800 °C. It was shown that above 5 mass% addition of (Ti,Zr)C0.85 there is a considerable mass gain change shown by samples during the first hour of oxidation before passive layers were created. Suitable chemical reactions were predicted in order to describe the oxidation process of obtained composites.

Characterization of the improved functionality in soybean protein-proanthocyanidins conjugates prepared by the alkali treatment

This study investigated the changes in functionality, structure, and protein composition in soybean protein-proanthocyanidins (SPI-PC) conjugates prepared by the alkali method with dose-ranging PC molecular grafting (0.5, 1, 2 mg/mL). Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) demonstrated that more >180 kDa polymers were formed in SPI-PC conjugates with the increase of PC levels. The structural analysis showed that conjugation to PC altered the molecular structure of SPI, appearing the protein-unfolding with PC level-dependent. Liquid chromatography tandem MS analysis (LC/MS-MS) demonstrated that the composition of protein and peptides, as well as allergen and epitopes, were changed after SPI was conjugated with PC. The antioxidant activity of SPI-PC conjugates was increased with the increase of PC levels because high PC contents caused more hydroxyl groups in polyphenols to introduce into the SPI. Emulsifying properties were increased with the increase of PC levels, probably resulting from the unfolding of proteins, increase of random coil and decrease of surface hydrophobicity. Western-bolt and enzyme-linked immunosorbent assay (ELISA) showed a reduced IgE binding capacity in SPI-PC conjugates with PC level-dependent, which was attributed to the reduction of major soybean allergens and the masking or destruction of epitopes induced by structural changes. However, the foaming properties varied from PC levels. Overall, this study demonstrated that SPI-PC conjugates formed using the alkali method have a potential application as hypoallergenic soybean products or functional ingredients in foods. In future studies, suitable reactions (e.g., PC levels) are required to be investigated in SPI-PC conjugates for balancing various functional properties. • PC increased emulsifying, foaming and antioxidant properties in SPI. • The IgE binding capacity in SPI was reduced with the increase in PC levels. • Protein modifications and structural changes in SPI varied from PC levels. • Suitable PC levels are required to balance various functionality in conjugates.

EnzymeMap: curation, validation and data-driven prediction of enzymatic reactions.

Enzymatic reactions are an ecofriendly, selective, and versatile addition, sometimes even alternative to organic reactions for the synthesis of chemical compounds such as pharmaceuticals or fine chemicals. To identify suitable reactions, computational models to predict the activity of enzymes on non-native substrates, to perform retrosynthetic pathway searches, or to predict the outcomes of reactions including regio- and stereoselectivity are becoming increasingly important. However, current approaches are substantially hindered by the limited amount of available data, especially if balanced and atom mapped reactions are needed and if the models feature machine learning components. We therefore constructed a high-quality dataset (EnzymeMap) by developing a large set of correction and validation algorithms for recorded reactions in the literature and showcase its significant positive impact on machine learning models of retrosynthesis, forward prediction, and regioselectivity prediction, outperforming previous approaches by a large margin. Our dataset allows for deep learning models of enzymatic reactions with unprecedented accuracy, and is freely available online.

Exploring Optimal Reaction Conditions Guided by Graph Neural Networks and Bayesian Optimization.

The optimization of organic reaction conditions to obtain the target product in high yield is crucial to avoid expensive and time-consuming chemical experiments. Advancements in artificial intelligence have enabled various data-driven approaches to predict suitable chemical reaction conditions. However, for many novel syntheses, the process to determine good reaction conditions is inevitable. Bayesian optimization (BO), an iterative optimization algorithm, demonstrates exceptional performance to identify reagents compared to synthesis experts. However, BO requires several initial randomly selected experimental results (yields) to train a surrogate model (approximately 10 experimental trials). Parts of this process, such as the cold-start problem in recommender systems, are inefficient. Here, we present an efficient optimization algorithm to determine suitable conditions based on BO that is guided by a graph neural network (GNN) trained on a million organic synthesis experiment data. The proposed method determined 8.0 and 8.7% faster high-yield reaction conditions than state-of-the-art algorithms and 50 human experts, respectively. In 22 additional optimization tests, the proposed method needed 4.7 trials on average to find conditions higher than the yield of the conditions recommended by five synthesis experts. The proposed method is considered in a situation of having a reaction dataset for training GNN.

Vicinal ketoesters - key intermediates in the total synthesis of natural products.

This review summarizes examples for the application of vicinal ketoesters such as α-ketoesters, mesoxalic esters, and α,β-diketoesters as key intermediates in the total synthesis of natural products utilizing their electrophilic keto group as reactive site. Suitable key reactions are, e.g., aldol additions, carbonyl ene reactions, Mannich reactions, and additions of organometallic reagents. The vicinal arrangement of carbonyl groups allows the stabilization of reactive conformations by chelation or dipole control.

Recent Advances in Photocatalytic Oxidation of Methane to Methanol.

Methane is one of the promising alternatives to non-renewable petroleum resources since it can be transformed into added-value hydrocarbon feedstocks through suitable reactions. The conversion of methane to methanol with a higher chemical value has recently attracted much attention. The selective oxidation of methane to methanol is often considered a “holy grail” reaction in catalysis. However, methanol production through the thermal catalytic process is thermodynamically and economically unfavorable due to its high energy consumption, low catalyst stability, and complex reactor maintenance. Photocatalytic technology offers great potential to carry out unfavorable reactions under mild conditions. Many in-depth studies have been carried out on the photocatalytic conversion of methane to methanol. This review will comprehensively provide recent progress in the photocatalytic oxidation of methane to methanol based on materials and engineering perspectives. Several aspects are considered, such as the type of semiconductor-based photocatalyst (tungsten, titania, zinc, etc.), structure modification of photocatalyst (doping, heterojunction, surface modification, crystal facet re-arrangement, and electron scavenger), factors affecting the reaction process (physiochemical characteristic of photocatalyst, operational condition, and reactor configuration), and briefly proposed reaction mechanism. Analysis of existing challenges and recommendations for the future development of photocatalytic technology for methane to methanol conversion is also highlighted.

Engineering Self-Powered Electrochemical Sensors Using Analyzed Liquid Sample as the Sole Energy Source.

Many healthcare and environmental monitoring devices use electrochemical techniques to detect and quantify analytes. With sensors progressively becoming smaller—particularly in point‐of‐care (POC) devices and wearable platforms—it creates the opportunity to operate them using less energy than their predecessors. In fact, they may require so little power that can be extracted from the analyzed fluids themselves, for example, blood or sweat in case of physiological sensors and sources like river water in the case of environmental monitoring. Self‐powered electrochemical sensors (SPES) can generate a response by utilizing the available chemical species in the analyzed liquid sample. Though SPESs generate relatively low power, capable devices can be engineered by combining suitable reactions, miniaturized cell designs, and effective sensing approaches for deciphering analyte information. This review details various such sensing and engineering approaches adopted in different categories of SPES systems that solely use the power available in liquid sample for their operation. Specifically, the categories discussed in this review cover enzyme‐based systems, battery‐based systems, and ion‐selective electrode‐based systems. The review details the benefits and drawbacks with these approaches, as well as prospects of and challenges to accomplishing them.

Oxygen extraction from lunar dry regolith: Thermodynamic numerical characterization of the carbothermal reduction

In Situ Resource Utilization constitutes an enabling technology for future, long-term human space exploration. The potentiality of oxygen extraction from dry lunar regolith by means of carbothermal reduction is analyzed in this work. Focus is placed on the scarcely investigated temperature range 1273–1373K, looking at the possibility to operate below the regolith melting point. The first aspect to consider is to properly identify candidate species and reaction paths that can lead to oxides reduction. This is achieved by modeling the Gibbs free energy ΔG∘ trend for suitable reactions, looking at which species can react in the temperature range of interest among the minerals and oxides present in the regolith. A more detailed investigation is presented for the SiO2 and TiO2 oxides families, being both particularly attractive: the former for its ubiquitous presence in the lunar soil, the latter for its importance in past ISRU literature and its local peaks of concentration in specific areas on the Moon. Finally, a preliminary indication on the expected contribution of each species to the total yield is given.

Modelling and simulation of downdraft biomass gasifier: Issues and challenges

Modelling of a downdraft biomass gasifier involves modelling of several phenomena such as drying, pyrolysis, thermochemical reactions, fluid and solid flow and heat transfer. Transient modelling of such a gasifier taken up in the present work is more challenging due to widely different time scales of the phenomena leading to stiff equations which are highly non-linear. Transient modelling also requires selection of suitable global reactions and their kinetic constants. The kinetic data presented by different researchers for several of the important reactions were found to have inconsistencies in values and units, which posed a major challenge and had to be resolved through careful tracing back of the data to original references. There are also uncertainties in the value of specific surface area of char, which is a very important factor in modelling rates of heterogenous reactions. Most of the issues and challenges faced in transient modelling of a gasifier have not been discussed in the literature. The method of igniting the gasifier for transient simulation is another aspect on which literature is silent, as is the case with modelling radiation in the packed bed of such gasifiers. The present work highlights these issues and challenges in the context of development of a 1D transient model of a downdraft biomass gasifier along with a discussion on the approach used for addressing them. The simulations from the model developed with careful choice of information from the literature show good agreement with experimental results reported in the literature.