Constituents Of Mixture Research Articles

Eutectogels: The Multifaceted Soft Ionic Materials of Tomorrow.

Eutectogels, a rising category of soft materials, have recently garnered significant attention owing to their remarkable potential in various domains. This innovative class of materials consists of a eutectic solvent immobilized in a three-dimensional network structure. The use of eco-friendly and cost-effective eutectic solvents further emphasizes the appeal of these materials in multiple applications. Eutectogels exhibit key characteristics of most eutectic solvents, including environmental friendliness, facile preparation, low vapor pressure, and good ionic conductivity. Moreover, they can be tailored to display functionalities such as self-healing capability, adhesiveness, and antibacterial properties, which are facilitated by incorporating specific combinations of the eutectic mixture constituents. This perspective article delves into the current landscape and challenges associated with eutectogels, particularly focusing on their potential applications in CO2 separation, drug delivery systems, battery technologies, biocatalysis, and food packaging. By exploring these diverse realms, we aim to shed light on the transformative capabilities of eutectogels and the opportunities they present to address pressing industrial, academic, and environmental challenges.

AGREEMIP: The Analytical Greenness Assessment Tool for Molecularly Imprinted Polymers Synthesis.

Molecular imprinting technology is well established in areas where a high selectivity is required, such as catalysis, sensing, and separations/sample preparation. However, according to the Principles of Green Chemistry, it is evident that the various steps required to obtain molecularly imprinted polymers (MIPs) are far from ideal. In this regard, greener alternatives to the synthesis of MIPs have been proposed in recent years. However, although it is intuitively possible to design new green MIPs, it would be desirable to have a quantitative measure of the environmental impact of the changes introduced for their synthesis. In this regard, this work proposes, for the first time, a metric tool and software (termed AGREEMIP) to assess and compare the greenness of MIP synthesis procedures. AGREEMIP is based on 12 assessment criteria that correspond to the greenness of different reaction mixture constituents, energy requirements, and the details of MIP synthesis procedures. The input data of the 12 criteria are transformed into individual scores on a 0-1 scale that in turn produce an overall score through the calculation of the weighted average. The assessment can be performed using user-friendly open-source software, freely downloadable from mostwiedzy.pl/agreemip. The assessment result is an easily interpretable pictogram and visually appealing, showing the performance in each of the criteria, the criteria weights, and overall performance in terms of greenness. The application of AGREEMIP is presented with selected case studies that show good discrimination power in the greenness assessment of MIP synthesis pathways.

Unravelling molecular interactions in various alcohol-water binary mixtures using femtosecond laser-induced thermal lens spectroscopy

The FTLS (femtosecond laser-induced thermal lens spectroscopy) technique was utilized to explore the photothermal response and heat transfer dynamics in various water-based binary mixtures containing monohydric and polyhydric alcohols. Our results highlight the significant influence of intermolecular hydrogen bonding interactions and the molecular properties of the mixture constituents on the photothermal behavior of both pure solvents and their binary mixtures. Notably, we observed distinct effects of water inclusion on the heat transfer characteristics of monohydric alcohols (such as methanol and ethanol) compared to polyhydric alcohols (such as ethylene glycol (Etg) and glycerol (Gly)). This discrepancy can be attributed to the formation of unique molecular structures via hydrogen bonding interactions, which effectively hinders the natural molecular dynamics of short-chain length alcohols. Conversely, polyhydric alcohols like Etg and Gly exhibit contrasting photothermal characteristics due to their extensive intermolecular hydrogen bonding. Our investigations reveal that molecular attributes, such as chain length and the number of hydroxyl (-OH) groups, play a crucial role in altering the heat transfer mechanisms and thermal lens signal characteristics of the mixtures. These findings emphasize the sensitivity of FTLS as a probe for understanding the effects of intermolecular hydrogen bonding interactions on the molecular dynamics and thermo-optical properties of aqueous-alcoholic solutions.

A study on the effect of mixture constituents on washout resistance, mechanical, and transport properties in the context of underwater 3D concrete printing

Underwater layered concrete depositions, such as those obtained using 3D printing, demonstrate washout resistance, which influences layer stability, transport properties, and inter-layer mechanical strength, thus impacting its final properties. This work first focuses on understanding the compositional effect of 3D printable mortars on their washout resistance. Conventional methods for assessing washout resistance of underwater concrete are ill-suited to 3D printable materials due to different mix designs and material deposition techniques. To address this, a new protocol considering layered deposition during 3D printing was developed to evaluate the washout resistance. Interrogation of the pore structure of the printed specimens (in air and underwater environment) via water absorption and sorptivity test further establishes the influence of washout on water transport properties. The study also investigates the impact of hydroxypropyl methylcellulose (HPMC) and polyvinyl alcohol (PVA) fiber dosages on mechanical properties, including compressive strength, interlayer split tensile strength, and flexural strength. Results indicate that HPMC addition significantly improves the washout resistance, whereas PVA fibers have detrimental effect. However, optimizing the PVA fiber dosage enhances both mechanical and water transport properties of underwater printed specimen via permeable void and capillary pore structure refinement.

Pollution Characteristics and Ecological Impact of Screening Analysis of Fishing Port Sediments from Dalian, North China.

Environmental pollution from synthetic chemical mixtures has significant adverse impacts on marine ecosystems. However, identifying the main constituents of chemical mixtures that pose ecological threats is challenging due to the necessity of an integrated workflow for comprehensive identification and toxicological prioritization of pollutants. Here, an all-in-one mass spectrometric strategy integrating target, suspect, and nontarget analysis was used to investigate organic pollutants of concern in fishing port sediments, with 355 pollutants (32 from target analysis, 118 from suspect screening and 205 from nontarget analysis) identified in 11 categories. The chemical classes of polycyclic aromatic hydrocarbons (PAHs), pesticides, and intermediates were the extensively detected chemical classes. The ecological risks of absolutely quantified pollutants (i.e., 16 parent PAHs, 7 organophosphate esters (OPEs), 10 pesticides and 4 benzotriazole ultraviolet absorbers) were assessed using toxicity-weighted concentration ranking, with o,p'-DDT being the major contributor. Under the toxicological priority index (ToxPi) framework, an extended ranking of all identified pollutants was achieved by combining instrument response and detection frequency, with a priority control list of 15 pollutants obtained, of which benzo[ghi]perylene (BghiP) and p,p'-DDE had the highest risk priority. Due to frequent detection rates and significant environmental risks, routine monitoring of petroleum pollutants is considered essential. This study presents a general workflow that includes comprehensive identification and prioritization of pollutants, facilitating chemical management and ecological risk assessment.

Probabilistic deep learning prediction of natural carbonation of low-carbon concrete incorporating SCMs

This study develops a probabilistic neural network (PNN) based on 2165 laboratory data incorporating mixture constituents and environmental conditions parameters to estimate the natural carbonation depth of concrete. The high prediction accuracy and certainty level of the model, achieving a testing R2 of 0.95 and negative log-likelihood of 1.82, enabled performing numerical simulations of carbonation depth and rate evolution of 576 binary and ternary blends for 12 years of exposure under different climatic conditions of two cities, Toronto and Vancouver. Mixtures containing ordinary Portland cement (OPC), fly ash, ground granulated blast furnace slag (GGBFS), and limestone calcined clay with water-to-binder ratios of 0.35–0.55, OPC replacement levels of 0–0.6, and binder contents of 350–500 kg/m3 were studied. Results indicated that OPC-GGBFS binary binders and OPC-GGBFS-fly ash ternary binders showed the lowest natural carbonation rates of 1.03–4.78 and 1.10–3.76 mm/year0.5, respectively. The uncertainty quantification of model's predictions can facilitate informed and confident decision-making for designing durable and sustainable concrete mixtures.

Comparative functional characterization and in vitro immunological cross-reactivity studies on Daboia russelii and Craspedocephalus malabaricus venom.

Snake venom is a complex mixture of organic and inorganic constituents, including proteins and peptides. Several studies showed that antivenom efficacy differs due to intra- and inter-species venom variation. In the current study, comparative functional characterization of major enzymatic proteins present in Craspedocephalus malabaricus and Daboia russelii venom was investigated through various in vitro and immunological cross-reactivity assays. The enzymatic assays revealed that hyaluronidase and phospholipase A2 activities were markedly higher in D. russelii. By contrast, fibrinogenolytic, fibrin clotting and L-amino acid oxidase activities were higher in C. malabaricus venom. ELISA results suggested that all the antivenoms had lower binding potential towards C. malabaricus venom. For D. russelii venom, the endpoint titration value was observed at 1:72 900 for all the antivenoms. In the case of C. malabaricus venom, the endpoint titration value was 1:2700, except for Biological E (1:8100). All these results, along with the avidity assays, indicate the strength of venom-antivenom interactions. Similarly, the western blot results suggest that all the antivenoms showed varied efficacies in binding and detecting the venom antigenic epitopes in both species. The results highlight the need for species-specific antivenom to better manage snakebite victims.

Enhancing sustainability in self-compacting concrete by optimizing blended supplementary cementitious materials

Within concrete engineering, the uptake of self-compacting concrete (SCC) represents a notable trend, delivering improved workability and placement efficiency. However, challenges persist, notably in achieving optimal performance while mitigating environmental impacts, particularly in cement consumption. However, simply reducing the cement content in the mix design can directly compromise the structural-concrete requirements. Towards these challenges, global trends emphasize the utilization of appropriate waste materials in blended concrete. This study explored a promising strategy by integrating supplementary cementitious materials (SCMs) to contribute to the United Nations' Sustainable Development Goals (SDGs) in addition to the engineering contributions. It suggests an optimal combination of Metakaolin (MK) and Limestone Powder (LP) to partially substitute cement. The research methodology employs the response surface method (RSM) to systematically explore the ideal ingredient ratios. Through a comprehensive analysis of orthogonal array of 16 mixes, encompassing both mixture and process variables, this study aims to explain the effects of MK and LP addition on the rheological and mechanical properties of SCC with varying cement replacement levels. In terms of mixture constituents, the total composition of cement, MK, and LP was fixed at 100%, while coarse aggregate (CA), fine aggregate (FA), and the water-to-binder ratio were held as process variables. In order to assess the rheological properties of the mix-design, various tests including slump flow, L-box, and sieve segregation were conducted. Additionally, to evaluate mechanical strength, samples were tested for compressive strength at both 7 and 28 days. Findings from the experiments reveal higher concentrations of MK result in reduced workability and hardened properties. Through RSM-based designed experimentation covering both rheological and mechanical aspects, it is observed that the optimal cement replacement level lies between 40 and 55%. The findings of this study contribute to the advancement of sustainable and structurally robust concrete practices, offering insights into the optimal utilization of SCMs to meet both engineering requirements and environmental sustainability goals.

Deep-Learning Driven, High-Precision Plasmonic Scattering Interferometry for Single-Particle Identification.

Label-free probing of the material composition of (bio)nano-objects directly in solution at the single-particle level is crucial in various fields, including colloid analysis and medical diagnostics. However, it remains challenging to decipher the constituents of heterogeneous mixtures of nano-objects with high sensitivity and resolution. Here, we present deep-learning plasmonic scattering interferometric microscopy, which is capable of identifying the composition of nanoparticles automatically with high throughput at the single-particle level. By employing deep learning to decode the quantitative relationship between the interferometric scattering patterns of nanoparticles and their intrinsic material properties, this technique is capable of high-throughput, label-free identification of diverse nanoparticle types. We demonstrate its versatility in analyzing dynamic surface chemical reactions on single nanoparticles, revealing its potential as a universal platform for nanoparticle imaging and reaction analysis. This technique not only streamlines the process of nanoparticle characterization, but also proposes a methodology for a deeper understanding of nanoscale dynamics, holding great potential for addressing extensive fundamental questions in nanoscience and nanotechnology.

Fate of inhaled electronic nicotine delivery systems (ENDS) puff constituents in the human respiratory tract

Published research on the fate of a puff from electronic nicotine delivery systems (ENDS) in the lungs is limited; more information would better inform human exposure and potential adverse outcomes from ENDS use. An ENDS puff is a mixture of multiple constituents in droplet and vapor form, including propylene glycol, vegetable glycerin, nicotine, water, and flavor chemicals. Understanding the complexity of the puff aids in developing mechanistic models that can account for the physical, physiological, and thermodynamic processes of the puff while traveling and depositing in lung airways. Previously, we developed a mathematical model to predict deposition and uptake of ENDS constituents in the oral cavity. In this study, we formulated the model for lung airways to extend to the entire respiratory tract and made adjustments to mechanisms such as phase change and nicotine protonation to study effects on droplet pH and nicotine evaporation. We conducted model simulations for two representative inhalation profiles relevant to ENDS users: mouth-to-lung and direct-to-lung inhalation. Simulation results showed that vapor uptake during ENDS use was the primary mechanism of the overall tissue dose for higher vapor pressure constituents. Nicotine protonation was unaffected by the ratio of propylene glycol to vegetable glycerin but changes to vanillin molarity impacted droplet pH and free nicotine fraction. The largest uptake and deposition occurred in the deep lung, where constituents more efficiently reach the arterial blood. Predicted total respiratory tract retention of higher vapor pressure constituents such as nicotine, propylene glycol, and benzaldehyde were 94–95%, whereas retention of lower volatility constituents such as vegetable glycerin and vanillin was 82–83%. Results also indicated regional uptake differences for the constituents evaluated in the two inhalation scenarios. Predictions from the ENDS deposition model can be linked to physiologically based pharmacokinetic (PBPK) models to determine the fate of puff constituents such as nicotine in other tissues and organs of the body and provides further basis for evaluating flavor chemicals and puff constituents based on user-specific exposure characteristics as well as internal dose to inform risk assessment of ENDS.

Asphalt Concrete Production Technology Using Oil Sludge from Zhaik Munay LLP

Oil sludge exhibits a compositional similarity to bitumen, a pivotal constituent in asphalt concrete mixtures. This similarity underscores the potential applicability of oil waste in the production of asphalt concrete, serving not only as an organic binder to fortify indigenous soils but also as a binding agent for the fabrication of organomineral mixtures. The incorporation of oil sludge in road construction endeavors holds promise for the conservation of natural resources, the amelioration of the environmental landscape, and a concurrent reduction in the cost of construction materials. The focus of this study encompasses a comprehensive examination of the physical and mechanical properties pertaining to asphalt concrete of Grade I, Type B. To enhance the performance attributes of asphalt concrete, an additive in the form of oil sludge sourced from ZhaikMunay LLP (Uralsk) was introduced. Various proportions of oil sludge, namely 5%, 10%, and 15%, were incorporated into the asphalt concrete mixture. The utilization of 5% oil sludge elicited negligible alterations in the properties of the asphalt concrete. However, with a 15% addition of oil sludge, discernible reductions were observed in maximum compressive efficiency (0.03% by volume) and shear resistance, indicated by the internal friction coefficient efficiency (0.01% by volume).

Spectroscopic and Computational Study of ZnCl2-Methanol Low-Melting-Temperature Mixtures.

Alcoholic electrolyte mixtures have wide applications in industries. In this study, a series of mixtures composed of ZnCl2 and methanol (MeOH) with ZnCl2 mol % from 6.7 to 25 were prepared, and their spectral, structural, and thermodynamic properties were studied using infrared (IR) spectroscopy, differential scanning calorimetry (DSC), and density functional theory (DFT) calculations. The DFT-assisted analysis of excess spectra, supported by 2D-correlation spectroscopy, led to the identification of the major constituents of ZnCl2-MeOH mixtures, namely, MeOH monomer, MeOH dimer, and ZnCl2-3MeOH complex, produced after dissociation of MeOH trimer which represents the bulk methanol. The Hirshfeld charge analysis showed that in the competition between the O-H···Cl hydrogen bond (H-bond) and Zn ← O coordination bond to transfer charge in ZnCl2-MeOH complexes, the latter always dominates, making MeOH positively charged. The phase diagram of the binary system showed the presence of V-shaped glass transition temperatures (Tg), characteristic of low-melting mixture solvents (LoMMSs). The present study provides insights into the microscopic properties of the system and sheds light on the understanding of the general principles to prepare deep-eutectic solvents (DESs) or LoMMSs using inorganic salts and alcoholic compounds.

Geoffrey Eglinton. 1 November 1927—11 March 2016

Where have all life's molecules gone? Recycled to carbon dioxide every one! Ah! But look—a hardy few remain As biomarkers imprisoned in the fossil domain (G. Eglinton & R. Pancost, Immortal molecules ( 47 )) Geoffrey Eglinton trained as an organic chemist at the University of Manchester working on acetylene synthesis, and the Eglinton reaction signifies his discovery of an effective method for their oxidative coupling. At the University of Glasgow, he recognized the potential of analytical advances to open new frontiers for characterization of molecular constituents of complex mixtures and continued to adopt and adapt instrumental techniques throughout his career. Eglinton's exploration of plant leaf waxes, and their taxonomic profiles, prompted forays in organic geochemistry, seeking molecular evidence for ancient life. Thus, his focus shifted to studying ‘chemical fossils’ preserved in the rock record and pioneering identification and interpretation of their compositions. At the University of Bristol, he established the Organic Geochemistry Unit and steered its rise to international prominence as a leading centre for research, a legacy that continues today. He served in planning the Apollo sampling programme and led research that explicated the carbon chemistry of the Moon. Major advances in using molecules as witnesses of the origins and fate of sedimentary organic matter bear the hallmarks of his insightful contributions and commitment to interdisciplinary, collaborative ventures. They include deciphering how environmental controls and thermal transformations govern the composition of petroleum hydrocarbons, and validating the ability for molecules to afford invaluable evidence of past climates. Eglinton's research accomplishments were recognized by many prestigious awards, including a Royal Gold Medal of the Royal Society, the V. M. Goldschmidt Medal of the Geochemical Society, the Wollaston Medal of the Geological Society, and the Dan David Prize.

Mixed stones: urinary stone composition, frequency and distribution by gender and age

Proper analysis of urinary stone composition is a cornerstone for diagnosis, targeted treatment and recurrence prevention of urolithiasis. The aim of this study was to determine the composition, frequency and distribution of mixed stones according to gender and age of patients. A total of 42,519 urinary stones from 30,311 men and 12,208 women submitted between January 2007 and December 2020 were studied. Most urinary calculi consisted of two components (50.9%), followed by stones of a single constituent (27.1%) and three-component stones (21.9%), while four-component stones were only rarely identified (0.1%). Among all stones, 49.8% consisted of whewellite (COM), weddellite (COD), and mixtures of COM and COD, 33.8% were pure carbonate apatite (CA) and mixtures of CA with COM and/or COD, while 7.6% were composed of uric acid anhydrous (UAA), uric acid dihydrate (UAD), and mixed UAA and UAD. The remaining 8.8% of calculi were rare single-component stones and rare mixtures of various constituents. The number of stone components was inversely associated with age (p < 0.001). The proportion of men decreased significantly with the number of stone constituents, from 3.01:1 for single-component stones to 1.0:1 for four-component urinary calculi (p < 0.001). The vast majority of urinary calculi consisted of two or more components in varying proportions. While age was inversely associated with the number of stone constituents, the proportion of women increased significantly from single-component to four-component urinary calculi. A significant proportion of mixed stones could present a challenge for diagnosis and targeted recurrence prevention.

Mid-infrared evidence for iron-rich dust in the multi-ringed inner disk of HD 144432

Context. Rocky planets form by the concentration of solid particles in the inner few au regions of planet-forming disks. Their chemical composition reflects the materials in the disk available in the solid phase at the time the planets were forming. Studying the dust before it gets incorporated in planets provides a valuable diagnostic for the material composition. Aims. We aim to constrain the structure and dust composition of the inner disk of the young Herbig Ae star HD 144432, using an extensive set of infrared interferometric data taken by the Very Large Telescope Interferometer (VLTI), combining PIONIER, GRAVITY, and MATISSE observations. Methods. We introduced a new physical disk model, TGMdust, to image the interferometric data, and to fit the disk structure and dust composition. We also performed equilibrium condensation calculations with GGchem to assess the hidden diversity of minerals occurring in a planet-forming disk such as HD 144432. Results. Our best-fit model has three disk zones with ring-like structures at 0.15, 1.3, and 4.1 au. Assuming that the dark regions in the disk at ~0.9 au and at ~3 au are gaps opened by planets, we estimate the masses of the putative gap-opening planets to be around a Jupiter mass. We find evidence for an optically thin emission (τ &lt; 0.4) from the inner two disk zones (r &lt; 4 au) at λ &gt; 3 µm. Our silicate compositional fits confirm radial mineralogy gradients, as for the mass fraction of crystalline silicates we get around 61% in the innermost zone (r &lt; 1.3 au), mostly from enstatite, while only ~20% in the outer two zones. To identify the dust component responsible for the infrared continuum emission, we explore two cases for the dust composition, one with a silicate+iron mixture and the other with a silicate+carbon one. We find that the iron-rich model provides a better fit to the spectral energy distribution. Our GGchem calculations also support an iron-rich and carbon-poor dust composition in the warm disk regions (r &lt; 5 au, T &gt; 300 K). Conclusions. We propose that in the warm inner regions (r &lt; 5 au) of typical planet-forming disks, most if not all carbon is in the gas phase, while iron and iron sulfide grains are major constituents of the solid mixture along with forsterite and enstatite. Our analysis demonstrates the need for detailed studies of the dust in inner disks with new mid-infrared instruments such as MATISSE and JWST/MIRI.

Impact strength and weight loss of fiber-reinforced concrete exposed to elevated temperatures

The article presents experimental results that were directed to explore the effect of weight loss on the residual repeated impact strength of concrete exposed to high temperatures. The experimental program included the testing of shallow cylindrical specimens under repeated impact loading following the ACI 544-2R test setup recommendations. Five concrete mixtures were prepared for this purpose to evaluate the influence of two major parameters, which are the concrete design compressive strength and the volumetric content of fiber. Three plain mixtures with three levels of design strength including normal-strength and high-strength concrete were prepared in addition to two fibrous mixtures that have the same constituents of the normal-strength mixture. Hooked-end steel fibers were used in the fibrous mixtures at two volumetric contents of 0.5 and 1.0 %. In addition to the repeated impact strength, the compressive strength was also tested using 100 mm cubes. A group of six shallow cylinders and three cubes was tested at room temperature without heating as a reference group from each mixture, while three similar groups were subjected to 200, 400 and 600 °C then tested after natural cooling. The weight loss was recorded for all heated specimens. The results showed that both the specimen weigh and the repeated impact exhibited a trend of continuous decrease with the increase of temperature. However, the repeated impact suffered serious damages after exposure to 200 °C and almost faded after exposure to the higher temperatures, while reasonable decreasing weight losses were recorded at all temperatures.

An inverse fitting strategy to determine the constrained mixture model parameters: application in patient-specific aorta.

The Constrained Mixture Model (CMM) is a novel approach to describe arterial wall mechanics, whose formulation is based on a referential physiological state. The CMM considers the arterial wall as a mixture of load-bearing constituents, each of them with characteristic mass fraction, material properties, and deposition stretch levels from its stress-free state to the in-vivo configuration. Although some reports of this model successfully assess its capabilities, they barely explore experimental approaches to model patient-specific scenarios. In this sense, we propose an iterative fitting procedure of numerical-experimental nature to determine material parameters and deposition stretch values. To this end, the model has been implemented in a finite element framework, and it is calibrated using reported experimental data of descending thoracic aorta. The main results obtained from the proposed procedure consist of a set of material parameters for each constituent. Moreover, a relationship between deposition stretches and residual strain measurements (opening angle and axial stretch) has been numerically proved, establishing a strong consistency between the model and experimental data.

Through the Smoke: An In-Depth Review on Cigarette Smoking and Its Impact on Ocular Health.

Smoking is a widespread and pervasive habit, impacting health across various care settings, including acute care, subacute care, home-based care, and long-term care. Smoking is a serious global public health concern that has been related to many chronic diseases. However, the effect of smoking on eye disorders has been less studied. Cigarette smoke contains a complex mixture of harmful constituents, including nicotine and toxic chemicals, which permeate the bloodstream, affecting ocular tissues. The oxidative stress and inflammation induced by smoking are central to its detrimental effects on ocular health. Age-related macular degeneration (AMD), a leading cause of vision loss, exhibits a strong association with smoking. Research consistently demonstrates that smokers face a heightened risk of both early and advanced AMD. Cataracts, another prevalent ocular condition, develop earlier and progress more rapidly in smokers. The oxidative stress on the lens and reduced antioxidants among smokers contribute to the increased severity of cataracts. Moreover, thehealth of the eyes may be compromised by smoking-related chemicals that reduce blood flow and/or hasten thrombus formation in ocular capillaries thus increasing the chance of acquiringglaucoma, cataracts, AMD, and Graves' eye disease. Beyond individual health concerns, the societal implications of smoking on ocular health are substantial, including increased healthcare costs and diminished quality of life for affected individuals. Understanding the underlying mechanisms can provide insights into potential therapeutic interventions for preventing and managing smoking-related ocular damage. Given the global prevalence of smoking, raising awareness about the ocular risks associated with smoking is crucial for promoting eye health. The review underscores the urgent need for comprehensive anti-smoking initiatives and smoking cessation programs to alleviate the burden of ocular diseases associated with smoking.

Application of Cold Sintering Process for Stabilizing Heavy Metals in Municipal Solid Waste Incineration Fly Ash

Municipal solid waste incineration fly ash (MSWI FA) consists predominantly of compounds comprising elements such as calcium, aluminum, silicon, sodium, and others. Additionally, it encompasses a complex mixture of heavy metals, chlorides, sulfates, organic pollutants, and other constituents. The effective and economically viable treatment of MSWI FA poses a formidable challenge for resource cycling at the current stage. In this research report, we adopt a novel low-temperature sintering method called the “Cold Sintering Process” (CSP) as a means to immobilize heavy metals within the fly ash. By utilizing a Taguchi orthogonal array method, we will adjust five control factors in the CSP, including sintering temperature, uniaxial pressure, sintering time, initial water addition, and sodium carbonate dosage. The leaching of cadmium from the fly ash, as measured by the Toxicity Characteristic Leaching Procedure (TCLP), will serve as the quality indicator of products. Through the application of CSP, MSWI FA was transformed into structurally stable ceramic blocks, and the heavy metals within the blocks were effectively immobilized. The results of the experiments showed that MSWI FA under the conditions of a temperature of 300 °C, uniaxial pressure of 312 MPa, sintering time in 60 min, 25 wt% water addition, and 9 wt% Na2CO3 addition could effectively reduce the leaching of cadmium by 77.71%, lead by 21.14%, zinc by 42.37%, and chromium by 99.99%, as compared to the original MSWI FA TCLP results. The X-ray Diffraction (XRD) results indicate that during the CSP, fly ash forms phases such as calcium silicate, rankinite, hydrogrossular, anorthite, and marilite. These phase transformations are considered beneficial for preventing the leaching of internal heavy metals. Scanning Electron Microscopy-Energy Dispersive X-ray Spectroscopy (SEM-EDS) results reveal that CSP is advantageous for compacting the overall structure, and EDS results further demonstrate that some of the Pb and Zn are carried out from the interior of the blocks, with uneven distribution on the surface of fly ash particles. The aforementioned experimental results serve as preliminary indications of CSP’s capability to stabilize detrimental components within high-purity fly ash. Future research endeavors may entail the refinement of material proportions, modification of experimental parameters, and other methodologies, thus facilitating potential scalability to industrial applications. Such developments align with the overarching goal of resource utilization.

Machine Learning Analysis of Raman Spectra To Quantify the Organic Constituents in Complex Organic-Mineral Mixtures.

Important decisions in local agricultural policy and practice often hinge on the soil's chemical composition. Raman spectroscopy offers a rapid noninvasive means to quantify the constituents of complex organic systems. But the application of Raman spectroscopy to soils presents a multifaceted challenge due to organic/mineral compositional complexity and spectral interference arising from overwhelming fluorescence. The present work compares methodologies with the capacity to help overcome common obstacles that arise in the analysis of soils. We created conditions representative of these challenges by combining varying proportions of six amino acids commonly found in soils with fluorescent bentonite clay and coarse mineral components. Referring to an extensive data set of Raman spectra, we compare the performance of the convolutional neural network (CNN) and partial least-squares regression (PLSR) multivariate models for amino acid composition. Strategies employing volume-averaged spectral sampling and data preprocessing algorithms improve the predictive power of these models. Our average test R2 for PLSR models exceeds 0.89 and approaches 0.98, depending on the complexity of the matrix, whereas CNN yields an R2 range from 0.91 to 0.97, demonstrating that classic PLSR and CNN perform comparably, except in cases where the signal-to-noise ratio of the organic component is very low, whereupon CNN models outperform. Artificially isolating two of the most prevalent obstacles in evaluating the Raman spectra of soils, we have characterized the effect of each obstacle on the performance of machine learning models in the absence of other complexities. These results highlight important considerations and modeling strategies necessary to improve the Raman analysis of organic compounds in complex mixtures in the presence of mineral spectral components and significant fluorescence.