Molecular Functional Groups Research Articles

Experimental study on the influence of CO2 injection on coal spontaneous combustion characteristics and molecular functional groups

The study aims to investigate changes in the spontaneous combustion characteristics of coal and its microscopic reaction mechanisms in a CO2 atmosphere. Programmed heating experiments and thermogravimetric analysis were conducted to examine index gas and combustion parameters in both air and CO2 atmospheres. Molecular functional group changes were analyzed via in situ infrared experiments, and their correlation with index gases was assessed using the grey correlation method. The results revealed that CO2 injection during the initial heating stage increased the production of CO, CH4, and C2H4. However, beyond 160 °C, CO2 effectively reduced the oxidation activity of functional groups and significantly inhibited the production of indicator gas. The characteristic temperature range of coal samples exhibited a 'lag' phenomenon under the influence of CO2, resulting in a prolonged combustion stage with reduced mass loss and heat discharge. Following CO2 injection, the -OH concentration significantly decreased, while C=O gradually increased. Additionally, the concentrations of -COOH, aliphatic hydrocarbons, and aromatic hydrocarbons initially increased with rising temperature and then declined. The contribution of these groups to gas generation followed the sequence of oxygen-containing functional groups > aliphatic hydrocarbons > -OH > aromatic hydrocarbons.

Biosynthesis of novel MnO<SUB>2</SUB> nanocapsules via <I>C. spinosa</I> extract and honeybee-derived chitosan: exploring antibacterial and anticancer properties

This investigation delves into the integration of Capparis spinosa extract (CSLe) onto manganese dioxide nanoparticles (MnO2NPs) and chitosan derived from honeybees (CSH) in a nanostructured configuration. The resultant nanocomposites, namely CSLe@MnO2NPs and CSH/CSLe@MnO2NPs, underwent thorough characterization through various analytical techniques. UV-Vis spectroscopy unveiled distinctive features, such as ligand-to-metal charge transfer and photoluminescence, affirming the successful chitosan-functionalization of the MnO2NPs, thereby differentiating them from their pristine counterparts. FTIR spectra corroborated the binding of chitosan and identified crucial molecular functional groups. SEM-EDX analysis revealed the morphological properties, addressing non-uniform sizes in the as-calcined MnO2NPs by the uniform coating of CSH on CSLe@MnO2NPs, while EDX confirmed the presence of essential elements. TEM and SAED provided insights into the spherical morphology, crystalline structure, and lattice planes of these nanoparticles. Size distribution measurements highlighted distinctions between CSLe@MnO2NPs and CSH/CSLe@MnO2NPs. The nanomaterials underwent evaluation for their antimicrobial properties against a spectrum of Gram-negative and Gram-positive bacterial strains, with CSH/CSLe@MnO2NPs exhibiting the highest bactericidal activity. Additionally, they demonstrated low minimum inhibitory concentration (MIC) values, especially against S. aureus (MIC as low as 12.5 µg/ml). Their efficacy extended to anti-biofilm formation, significantly diminishing biofilm development in a dose-dependent manner, a pivotal factor in addressing biofilm-related infections. The study also scrutinized their cytotoxicity against normal Vero and PC3 prostate cancer cells, revealing potential anticancer properties. Dose-dependent reductions in cell viability were observed for both normal and cancer cells. In conclusion, these findings underscore the versatility and promise of CSH/CSLe@MnO2NPs in diverse biomedical applications, including antibacterial, anti-biofilm, and anticancer therapies.

Early Mortality Prediction in Intensive Care Unit Patients Based on Serum Metabolomic Fingerprint.

Predicting mortality in intensive care units (ICUs) is essential for timely interventions and efficient resource use, especially during pandemics like COVID-19, where high mortality persisted even after the state of emergency ended. Current mortality prediction methods remain limited, especially for critically ill ICU patients, due to their dynamic metabolic changes and heterogeneous pathophysiological processes. This study evaluated how the serum metabolomic fingerprint, acquired through Fourier-Transform Infrared (FTIR) spectroscopy, could support mortality prediction models in COVID-19 ICU patients. A preliminary univariate analysis of serum FTIR spectra revealed significant spectral differences between 21 discharged and 23 deceased patients; however, the most significant spectral bands did not yield high-performing predictive models. By applying a Fast-Correlation-Based Filter (FCBF) for feature selection of the spectra, a set of spectral bands spanning a broader range of molecular functional groups was identified, which enabled Naïve Bayes models with AUCs of 0.79, 0.97, and 0.98 for the first 48 h of ICU admission, seven days prior, and the day of the outcome, respectively, which are, in turn, defined as either death or discharge from the ICU. These findings suggest FTIR spectroscopy as a rapid, economical, and minimally invasive diagnostic tool, but further validation is needed in larger, more diverse cohorts.

Polymeric innovations in drug delivery: Enhancing therapeutic efficacy

Drug delivery is the method or process of administering pharmaceutical compounds to achieve a therapeutic effect in humans or animals. Drug delivery technologies are designed to modify the release, absorption, distribution, and elimination of drugs to enhance therapeutic effectiveness, safety, and patient adherence. Innovative drug delivery systems provide a variety of approaches, such as oral, injectable, implantable, pulmonary, nasal, transmucosal, transdermal, and topical routes, along with options for delivering proteins and peptides. Polymers, due to their large molecular structure and diverse functional groups, play a pivotal role in these systems. Progress in polymer science has paved the way for the development of advanced drug delivery platforms. To optimize polymer-based drug delivery, it is essential to carefully evaluate both surface and bulk properties during the design process. This review explores the use of natural and synthetic polymers in oral drug delivery systems. Natural polymers include protein-based polymers like collagen, albumin, and gelatin, and polysaccharides such as alginate, chitosan, dextran, gums, hyaluronic acid, starch, and cellulose. Synthetic polymers are classified into biodegradable types, which include polyesters such as polylactic acid (PLA), polyglycolic acid (PGA), polyhydroxybutyrate-co-valerate (PHBV), polycaprolactone (PCL), and poly(lactide-co-glycolide) (PLGA). Additionally, they encompass poly anhydrides like poly sebacic acid and poly adipic acid. Non-biodegradable synthetic polymers include silicones, cellulose derivatives, synthetic carbonates, acrylics, and others like vinyl chloride polymer and copolymers, styrene acrylonitrile polymer (SAN), acrylonitrile butadiene styrene polymer (ABS), and polystyrenes. This review focuses on summarizing recent progress in polymer-based drug delivery systems, emphasizing their capability to improve therapeutic effectiveness and promote patient adherence.

Chemical feature-based machine learning model for predicting photophysical properties of BODIPY compounds: density functional theory and quantitative structure-property relationship modeling.

Boron-dipyrromethene (BODIPY) compounds have unique photophysical properties and have been applied in fluorescence imaging, sensing, optoelectronics, and beyond. In order to design effective BODIPY compounds, it is crucial to acquire a comprehensive understanding of the relationships between the structures of BODIPY and the corresponding photoproperties. Fifteen molecular descriptors were identified to be strongly correlated with the maximum absorption wavelength. The developed ML/QSPR model exhibited good predictive performance, with coefficients of determination (R2) of 0.945 for the training set and 0.734 for the test set, demonstrating robustness and reliability. A posterior analysis of some of the selected descriptors in the model provided insights into the structural features that influence BODIPY compound properties; meanwhile, it also emphasizes the importance of molecular branching, size, and specific functional groups. This work shows that applied combined cheminformatics and machine learning approach is robust to screen the BODIPY compounds and design novel structures with enhanced performance. In the present study, all the BODIPY models studied were fully optimized, and the corresponding absorption spectrum was obtained at DFT/TDDFT//B3LYP/6-311G(d,p) level. All the above calculations were executed by the Gaussian 16 program. Based upon the theoretical computational results, the machine learning-based quantitative structure-property relationship (ML/QSPR) model was employed for predicting the maximum absorption wavelength (λ) of BODIPY compounds by combining hand-crafted molecular descriptors (MD) and explainable machine learning (EML) techniques using Scikit-learn python library. A dataset of 131 BODIPY compounds with their experimental photophysical properties was used to generate a diverse set of molecular descriptors capturing information about the size, shape, connectivity, and other structural features of these compounds using Chemaxon and Alvadesc software. A genetic algorithm (GA) variable selection together with the multi-linear regression (MLR) method were applied to develop the best predictive model using the Genetic Selection python library.

Unlocking the intermediate-phase evolution in perovskite crystallization with an operando infrared FEWS sensor.

The intermediate phase produced by the complexation of metal ions and solvent molecules usually occurs in the crystallization process of perovskite single crystal or film. Effective in situ monitoring of intermediate-phase evolution is beneficial to the control of crystal quality. However, it is difficult to realize. In this work, infrared fiber evanescent wave spectroscopy (FEWS) was raised to monitor the intermediate-phase evolution in real time and non-destructively using GeAsSeTe chalcogenide optical fibers. The vibrational and rotational dynamics of specific molecular functional groups was operando captured, reflecting a perovskite precursor of different states. Taking BM2PbBr4 (BM = benzimidazole) perovskite as an example, the shift of the stretching vibration of -C=O groups in DMF (N,N-dimethylformamide) toward low wavenumbers and then recovered toward original position probed the complexion of Pb2+ and carbonyl groups into (DMF)2BMPbBr3 intermediate phase and then decomplexing to precipitate BM2PbBr4 perovskite crystal. Some anomalous emergence of new vibrational bands associating with -C-N and -N-H bonds suggest the variation of DMF-BMBr hydrogen bonds during intermediate-phase evolution. This technique provides new, to the best of our knowledge, insights into the control of perovskite crystallization processes and pushes the development of high-quality perovskite materials for high-performance photovoltaic or optoelectronic devices.

A perspective on the algae-derived dissolved organic matter and its dynamic influence on the aggregation of nanoplastics in eutrophic waters

The aggregation behavior of nanoplastics (NPs) is crucial in determining their fate in aquatic environments. Dissolved organic matter (DOM), characterized by its complex molecular structure and diverse functional groups, can spontaneously absorb on the surface of NPs, thus altering their colloidal stability. In eutrophic waters, DOM primarily originates from metabolic byproducts released by phytoplankton, and its molecular composition and hydrophilic properties change dynamically as the progression of algal blooms. This perspective aims to summarize the heterogeneity of DOM during the initiation, outbreak and recession of algal blooms. And we investigate the influence of molecular-level variations in DOM composition on the aggregation behavior of NPs. Additionally, this study provides insights into the underlying mechanisms relating to the interactions between DOM and NPs. Ultimately, it tackles the challenges and future directions, highlighting the necessity for comprehensive studies to understand the fate of NPs in eutrophic waters.

Transparent Machine Learning Model to Understand Drug Permeability through the Blood-Brain Barrier.

The blood-brain barrier (BBB) selectively regulates the passage of chemical compounds into and out of the central nervous system (CNS). As such, understanding the permeability of drug molecules through the BBB is key to treating neurological diseases and evaluating the response of the CNS to medical treatments. Within the last two decades, a diverse portfolio of machine learning (ML) models have been regularly utilized as a tool to predict, and, to a much lesser extent, understand, several functional properties of medicinal drugs, including their propensity to pass through the BBB. However, the most numerically accurate models to date lack in transparency, as they typically rely on complex blends of different descriptors (or features or fingerprints), many of which are not necessarily interpretable in a straightforward fashion. In fact, the "black-box" nature of these models has prevented us from pinpointing any specific design rule to craft the next generation of pharmaceuticals that need to pass (or not) through the BBB. In this work, we have developed a ML model that leverages an uncomplicated, transparent set of descriptors to predict the permeability of drug molecules through the BBB. In addition to its simplicity, our model achieves comparable results in terms of accuracy compared to state-of-the-art models. Moreover, we use a naive Bayes model as an analytical tool to provide further insights into the structure-function relation that underpins the capacity of a given drug molecule to pass through the BBB. Although our results are computational rather than experimental, we have identified several molecular fragments and functional groups that may significantly impact a drug's likelihood of permeating the BBB. This work provides a unique angle to the BBB problem and lays the foundations for future work aimed at leveraging additional transparent descriptors, potentially obtained via bespoke molecular dynamics simulations.

Growth of L-asparagine monohydrate organic single crystals: An experimental and DFT computational approach for nonlinear optical applications

Good optical quality of L-asparagine monohydrate (C4H8N2O3.H2O) organic single crystal has been grown by adopting natural slow evaporation process at room temperature from aqueous solutions. The lattice parameters obtained by powder X-ray diffraction data revealed orthorhombic crystal system of the harvested crystal. The morphology and planes of the crystal have been identified. The molecular vibrations and functional groups have been specified by Fourier transform infrared (FTIR) spectroscopy studies. Energy dispersive X-ray (EDX) study has been availed to find out the elements constituting the crystal. Scanning electron microscopy, (SEM), provided the surface morphology of the crystal. The dependence of dielectric properties on frequency and temperature have been investigated and the electronic polarizability (α) has been determined. UV–vis spectral analysis shows that the crystal possesses good optical transmittance in the visible part of the energy spectrum. The optical band gap and the Urbach energy have been determined from lower absorption edge. Third order nonlinear susceptibility χ(3), nonlinear refractive index (n2), and linear susceptibility χ(1) have been calculated by Miller's generalized rule. The first-principle computation of band structure of electrons and the electron density of states have been discussed, which suggest that the crystals possess direct band gap. Density Functional Theory (DFT) with B3LYP function by Gaussian09W software was utilized to calculate HOMO-LUMO energy gap as well as non-linear optical parameters namely, linear polarizability (α), hyperpolarizability (β and γ) and dipole moment (μ) of L-asparagine monohydrate crystal. All the findings prove that L-asparagine monohydrate is a promising NLO crystal.

Hemoglobin targeting potential of aminocarb pesticide: Investigation into dynamics, conformational stability, and energetics in solvent environment

Aminocarb (AMC), a carbamate pesticide, due to its prevalent usage exhibits increased accumulation in the environment affecting both insects and humans. It enters the human body via food grains and be transported through bloodstream. AMC's chemical structure, containing specific molecular frameworks and functional groups, enables it to bind with proteins like albumin and hemoglobin. Given that molecules with similar architecture are known to bind with hemoglobin, we aimed to explore Aminocarb's binding capability and the potential mechanism or mode of its interaction with hemoglobin. Hb being a tetramer with a profound interface between amino acid chains offers multiple binding sites. It is therefore important to investigate the structural aspects of binding of AMC by employing various spectroscopic and in-silico methods. The surface of the α1 chain near the α1β2 interface emerges as the preferred binding site for AMC, primarily due to its conformational restrictions. In its bound state, AMC tends to maintain a relaxed conformation, closely resembling its globally optimized geometry, and resides in close proximity to the α1 chain via multiple hydrophobic contacts and water bridge as observed in molecular dynamics (MD) simulations. Fluorescence quenching experiments showed moderate binding strength (7.7 × 10⁴ L M⁻1 at 288 K, 7.8 × 10⁴ L M⁻1 at 298 K, 7.9 × 10⁴ L M⁻1 at 308 K) and spontaneous binding, driven by hydrophobic and van der Waals interactions, as indicated by enthalpy (0.80–0.91 kJ mol⁻1), entropy (0.0970–0.0974 kJ mol⁻1), and Gibbs free energy (−27.13 to - 29.08 kJ mol⁻1). Circular dichroism experiments reveal no major structural changes in Hb. Quantum chemical calculations and MD simulations reveal conformation-dependent energy differences, enhancing our understanding of AMC's binding mechanism to Hb.

Preliminary investigation of the effects of high-temperature thermal activation on the activity of graphite tailings and the mechanical properties of graphite tailings mortar

The disposal of graphite tailings, a byproduct of graphite mining, poses significant recycling challenges. This study explores eco-friendly cement mortar by partially replacing sand with activated graphite tailings, focusing on waste utilization. Activating the pozzolanic properties of graphite tailings at various thermal activation temperatures converts them into high-quality binder materials. Quantitative assessments of graphite tailings' activity, conducted through ion leaching tests and raw material diffraction analysis, produce an activity index Ka, evaluating their performance under different thermal activation temperatures. Variable testing of substitution rates and activation temperatures examines changes in mechanical properties, microstructure, and pore distribution of the graphite tailings mortar. Optimum results reveal a 30 % substitution rate and a 750°C activation temperature. Graphite tailings, as pozzolanic binder materials, enhance mortar strength by improving particle size distribution, reducing "micro-area bleeding" and "boundary effects". After thermal activation, reactive minerals on the tailings' surface undergo secondary hydration reactions with cementitious products, modifying chemical structures and micro-morphology at the interface. Semi-quantitative XRD analysis and FT-IR spectroscopy characterize the ensuing chemical reactions, unveiling molecular bond and functional group changes. The superior performance of thermally activated graphite tailings primarily stems from increased active SiO2 and Al2O3 post-activation. The active SiO2 and Al2O3 reacts with cementitious products to form C-(A)-S-H gels, filling micro-pores at the tailings-mortar interface, which is the key characteristic that enables graphite tailings to serve as high-quality pozzolanic binder aggregates. A predictive model for compressive strength, utilizing reference models from solid waste mortar concrete, quantifies the positive impact of chemical activation. The theoretical results obtained from this predictive model closely align with the experimental findings.

In vitro dynamic digestion properties and fecal fermentation of Dictyophora indusiata polysaccharide: Structural characterization and gut microbiota

The in vitro dynamic digestive model more realistically simulates the human digestive system compared to static digestive model. In this study, the dynamic in vitro stomach-intestine digestive system and fecal fermentation was used to investigate the dynamic digestion properties and fermentation properties of Dictyophora indusiata polysaccharide. The results showed that there were no significant changes in molecular weight, functional groups and surface morphology after the in vitro dynamic simulated digestion, indicating that D. indusiata polysaccharide maintained a relatively stable structure during the dynamic in vitro salivary-gastrointestinal digestion. In addition, D. indusiata polysaccharide improved the abundance of beneficial bacteria, including Blautia, Coprobacter and Fusicatenibacter. It is remarkable that D. indusiata polysaccharide significantly increased the level of acetic acid and propionic acid. In conclusion, these results suggested that D. indusiata polysaccharide was a potential source of prebiotics, which provides a basis for the development of D. indusiata polysaccharide in the food or medical field.

Synthesis and Characterization of Several Mannich Bases Derived from 2-(4-methylpiperazin-1-yl)acetohydrazide

This investigation involves the reaction of 2-(4-methylpiperazin-1-yl)acetohydrazide (1) with phenylisothiocyanate (2) to produce a urea derivative chemical (3). Compound 3, a 1,2,4-triazole derivative, is produced from Compound 2 through an intramolecular ring closure reaction that presumably involves the hydrazide group. The next step is to use various amines to conduct a Mannich reaction on the 1,2,4-triazole derivative. Compound 3, which has an active methylene group, is combined with formaldehyde and either a primary or secondary amine in the Mannich reaction. As a result, a β-aminocarbonyl molecule is formed. Mass spectrometry, infrared spectroscopy, nuclear magnetic resonance (1H and 13C), and other analytical methods are used to ascertain the structures of the compounds that are produced. These methods are useful for determining the molecular structure, atomic connectivity, and functional groups.

Molecular characteristics of dissolved organic matter regulate the binding and migration of tungsten in porous media

Tungsten (W) is an emerging contaminant that poses potential risks to both the environment and human health. While dissolved organic matter (DOM) can significantly influence the W's environmental behavior in natural aquifers, the mechanisms by which DOM's molecular structure and functional group diversity impact W binding and migration remain unclear. Using molecular weight-fractionated soil and sediment DOM (<1 kDa, 1–100 kDa, and 100 kDa–0.45 μm), this study systematically investigated the relationship between DOM molecular characteristics and tungstate (WO42−) binding properties using multiple spectroscopic methods, including FTIR, fluorescence spectroscopy and XPS. The migration behavior of WO42− in porous media was also investigated through quartz sand column experiments. Results revealed that approximately 75 % of W was controlled by DOM, with over 50 % binding to low molecular weight DOM (<1 kDa). Tungsten bound to medium-high molecular weight DOM (1–100 kDa, >100 kDa) showed a greater propensity for retention, with the >100 kDa fractions demonstrating stronger selective binding to W, exhibiting distribution coefficients (Kmd) of 6.11 L/g and 10.69 L/g, respectively. Further analysis indicated that W primarily binds with aromatic rings, phenolic hydroxyls, polysaccharides, and carboxyl groups in DOM, potentially affecting DOM structural stability and consequently influencing W migration characteristics. Free W migration in quartz sand was primarily controlled by Langmuir monolayer adsorption, leading to local enrichment (Da = 6.83, Rd = 86.98). When bound to DOM, W's migration ability significantly increased (Rd = 8–10), with adsorption shifting to a Freundlich multilayer model, primarily controlled by convective transport (Npe = 27–62> > 1.96), while adsorption effects weakened (Da ≈ 1). This study, for the first time, systematically reveals the regulatory mechanisms of DOM molecular characteristics on tungsten's environmental behavior. It offers crucial parameter support for constructing tungsten migration models and provides important guidance for tungsten pollution risk assessment and remediation strategies.

Re-understanding and mitigating hydrogen release chemistry toward reversible aqueous zinc metal batteries

Interfacial H2 release severely limits the reversibility and feasibility of aqueous Zn metal batteries for large-scale energy storage. Different from the conventional perception that H2 release mainly originates from the competition between hydrogen evolution reaction and Zn plating process, we herein surprisingly find that non-negligible H2 is also generated during stripping due to the accelerated chemical corrosion of the newly exposed Zn surface. To address this issue, we systematically screened the organic additives with different molecular structures and functional groups. Interestingly, a positive correlation between the adsorption strength of additives and the ability to inhibit the interfacial hydrogen release is found. Taking cysteamine (MEA) as a model additive, a gradient solid electrolyte interphase (SEI) is in situ formed at the Zn surface, acting as a chemical “barrier” to isolate interfacial water molecules from electrode surface consequently enable a higher Coulombic efficiency (>99.5%, 4000 cycles) compared with that of MEA-free electrolyte (98.1%, 189 cycles). This work provides a new understanding of the interfacial hydrogen release mechanism and the criteria for selecting additives for aqueous Zn metal anodes.

Sea conch (Rapana venosa) peptide hydrolysate regulates NF-κB pathway and restores intestinal immune homeostasis in DSS-induced colitis mice.

Inflammatory bowel disease (IBD) is a chronic inflammatory condition of the gastrointestinal tract. Sea conch peptide hydrolysate (CPH) was produced by enzymatic digestion of fresh conch meat with trypsin enzyme. To analyze the molecular composition, functional groups, and structural morphology of the hydrolysate, we employed liquid chromatography-mass spectrometry (LC-MS), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). Results confirmed that crude protein could be effectively digested by enzymes to generate peptides. In this study, we evaluated the bioactivities of CPH on dextran sulfate solution (DSS)-induced colitis in mice. The findings demonstrated that CPH supplementation improved body weight, food and water intake, and colon length. The therapeutic efficacy and immunoregulatory effect of CPH were further determined. Our results exhibited that CPH treatment significantly ameliorated pathological symptoms by enhancing intestinal integrity, mucin production, and goblet cell count. Moreover, the immunoregulatory effect of CPH on mRNA expression levels of different pro- and anti-inflammatory cytokines was determined. Results exhibited a decrease in the expression of pro-inflammatory cytokines and an increase in anti-inflammatory cytokines in the colon. Additionally, the CPH administration modulates the nuclear factor kappa B (NF-κB) pathway, preventing DNA damage and cell death. Assays for apoptosis and DNA damage revealed that CPH reduced oxidative DNA damage and apoptosis. These findings highlight the immunomodulatory and treatment amelioration effect of CPH in reducing the severity of colitis.

Molecular sieves hybrid metal–organic framework for efficient simultaneously capturing carbon dioxide and collecting water from air

Water scarcity and climate change associated with global warming are two common challenges facing humankind. The simultaneous removal of water and carbon dioxide (CO2) from the air has become a popular candidate strategy to address these challenges. In this paper, molecular sieve@MOF composites were synthesized in-situ for the first time by adding ZSM-5 and HZSM-5 during the preparation of Mg/DOBDC. A comprehensive review of the air capture CO2 and water harvesting properties of the novel composites was also presented. Primarily, the effects of molecular sieve category and ratio on the composite were systematically investigated. Subsequently, the crystal structure, morphological composition and textural properties of the composites were characterized. Finally, CO2/H2O co-adsorption performance, adsorption and desorption cycle stability and adsorption mechanism were investigated. The results demonstrated that the metal nodes of Mg/DOBDC interacted with the molecular sieve surface functional groups to increase the metal active sites and porosity. The obtained 0.5HZSM-5@Mg/DOBDC MOF showed the maximum CO2 uptake of 15.90 mmol/g (0.1 MPa, 25 °C), which was 3.66 times the amount of Mg/DOBDC.

Microstructural analysis and prediction of strength response of black expansive soil

Amelioration studies using waste residues are gaining popularity in the construction sector, as they offer dual effective management and utilization of waste residue. This practice is of interest to the growth of any nation due to environmental protection and the economic advantages it offers. For that reason, this present-day study seeks the sustainable utilization of calcium carbide residue (CCR) and palm oil fuel residue (POFR) in expansive soil amelioration. After an in-depth characterization of the parent soil, several number of California bearing ratio (CBR) tests was performed on the parent and additive-treated soil. For this purpose, a total of twenty mixes were prepared possessing various combinations of CCR, POFR, water, and soil. CBR testing for soaked and unsoaked conditions was carried out to assess the strength performance and effectiveness of the additives. Through this approach, optimal California bearing ratio (CBR) values of 41 and 61% were achieved at trial runs designated Y4 for soaked and unsoaked conditions, indicating a strong mechanical performance. These experimental outcomes were subsequently validated using statistical indicators such as ANOVA (F crit > F) and student t-test (t crit > t stat). A step further, microstructural testing such as transmission electron microscopy (TEM), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FTIR) were performed on the natural soil and soil treated with the optimal level of additives obtained at trial runs designated Y4. The SEM micrographs evidence the transformation of the loosely packed structure of untreated soil into a dense soil composite. Similarly, diverging molecular functional groups were recorded in the FTIR examination with the incorporation of blended CCR-POFR. Finally, the findings suggest that carefully designed mixtures of CCR and POFR can offer a sustainable, efficient approach to soil amelioration, potentially revolutionizing construction practices.

Advancing plant-based meat analogs: Composite blend of pulse protein reinforcing structure with fibrous mushroom, jackfruit seed powder and carboxymethyl cellulose.

In this study, Indian pulse proteins from cowpeas, yellow peas, green gram, and horse gram were used to create plant-based meatball analogs. The nutritional composition, molecular functional groups, color, and texture of meatball analogs T1, T2, and T3 and mutton meatballs were thoroughly analyzed. T1 had highest protein (51%) compared to control (19%), T2 (45%), and T3 (36%), but fiber content (1.26%) was less in T1 compared to control (2.86%), T2 (3.33%), and T3 (3.49%). The more is fibrous raw materials; lower will be the hardness of meat analogs. T1 had consistent fracturability, hardness, cohesiveness, and adhesiveness, and was superior in springiness, gumminess, resilience, and chewiness compared to T2, T3, and control. Sensory evaluation results reported that T1 was more consistent with control sample in terms of color, texture, juiciness, and overall acceptability and no significant difference was reported among the two (p > .05). The L* and b* values of T1 were more consistent with control compared to other two. Potato starch, salt, spice mix, coriander leaves, beet root pulp, jackfruit seed powder, rose water, carboxy methyl cellulose and rehydrated mushrooms showed a positive impact on sensory and textural attributes. The Fourier transform infrared (FTIR) spectra revealed that the protein fractions were not affected by the processing conditions. FTIR results confirm the presence of secondary structural components such as α-helix, β-sheet, and β-turn. The interaction between the starchy fibrous material and protein fractions were identified clearly via FTIR. The T1 meat analog was superior in terms of color, organoleptic and textural properties compared to T2 and T3 and more close to mutton meatballs. These results will open up the new horizons in this area and pave the way for the large production and marketing of plant based meat analogs, which will reduces the health and sustainable raising issues from consumption of mutton meat.

Sucrose-derived porous carbon catalyzed lignin depolymerization to obtain a product with application in type 2 diabetes mellitus

As the most important phenolic biopolymer in nature, lignin shows promising application potentialities in various bioactivities in vivo and in vitro, mainly including antioxidant, anti-inflammatory, hypolipidemic, and antidiabetic control. In this work, several carbon-based solid acids were synthesized to catalyze the fragmentation of organosolv lignin (OL). The generated lignin fragments, with controllable molecular weight and functional groups, were further evaluated for their application in the prevention and treatment of type 2 diabetes mellitus (T2DM). The results suggested that the urea-doped catalyst (SUPC) showed a more excellent catalytic performance in producing diethyl ether insoluble lignin (DEIL) and diethyl ether soluble lignin (DESL). In addition, the lignin fragments have a good therapeutic effect on the cell model of T2DM. Compared with the insulin resistance model, DEIL obtained by catalytic depolymerization of OL with SUPC could improve the glucose consumption of insulin-resistant cells. Moreover, low-concentration samples (50 μg/mL) can promote glucose consumption (19.7 mM) more than the traditional drug rosiglitazone (17.5 mM). This work demonstrates the prospect of depolymerized lignin for the prevention and treatment of T2DM and provides a new application field for lignin degradation products.