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An approach to identifying fibers and evolved compounds from flame resistant fabrics
Flame resistant (FR) fabrics have use in civilian, military, and industrial applications. Herein, this work describes the development of a methodology aimed at identifying the fiber composition of blended fabrics of unknown composition. Here, FR fabrics used in military uniforms, are studied using a combination of X-ray photoelectron spectroscopy (XPS), isothermal thermogravimetric analysis (TGA) and pyrolysis gas chromatography/mass spectrometry (Py-GC-MS). Elemental analysis of the fabrics using XPS yielded a preliminary determination of the composition of the polymer(s) and aided in the identification of FR additives. TGA and Py-GC-MS experiments were used for subsequent compound identification. In TGA the temperature of mass loss events was compared to reference materials, and in Py-GC-MS the pyrolysis products of the blended FR fabrics were compared to those from a series of potential parent fibers. It was possible to discern the composition of the parent fibers and the type of FR treatment added to fabrics because the thermal decomposition chemistry did not significantly change by blending the fibers to make fabrics.
Structure-activity relationship of Pt catalyst on engineered ceria-alumina support for CO oxidation
In heterogeneous catalysis, the promotion of low temperature activity and enhancement of thermal stability simultaneously especially for precious metal catalysts is always highly demanded but very challenging. In this work, we report a novel Pt catalyst on ceria-alumina (CeO 2 /Al 2 O 3 ) support (Pt/CA-T) engineered by a two-step ceria deposition strategy, exhibiting superior thermal stability and low-temperature carbon monoxide (CO) oxidation activity after activation. Pt single sites anchored to engineered CeO 2 edge sites are much more stable than that to CeO 2 (111) surface, and such stable single sites can be transformed into highly active Pt clusters for efficient low-temperature CO oxidation. Active site identification indicates that the CO oxidation activity of different Pt sites follows such sequence: Pt cluster step sites ≈ Pt cluster terrace sites > Pt cluster corner sites $\gg$ Pt single sites on CeO 2 . The excellent low temperature activity of activated Pt/CA-T catalyst for CO oxidation is associated with its abundant Pt cluster step and terrace sites as well as rich Pt-CeO 2 interfaces, which facilitate the adsorption of active CO species and superior oxygen activation/transfer ability. The present study provides new insights into the structure–activity relationship of Pt-CeO 2 -Al 2 O 3 catalyst, which can also guide the preparation of other highly robust supported catalysts for important industrial applications.
Scattering insights into shear-induced scission of rod-like micelles
Understanding the scission of rod-like micelles under mechanical forces is crucial for optimizing their stability and behavior in industrial applications. This study investigates how micelle length, flexibility, and external forces interact, offering insights into the design of micellar systems in processes influenced by mechanical stress. Although significant, direct experimental observations of flow-induced micellar scission using scattering techniques remain scarce. Small angle neutron scattering (SANS) is used to explore the shear response of aqueous cetyltrimethylammonium bromide (CTAB) solutions with sodium nitrate. Rheological tests show shear thinning with no shear banding, ensuring a uniform flow field for reliable interpretation of scattering data. As shear rate increases, the scattering spectra show angular distortion, which is analyzed using spherical harmonic decomposition to characterize flow-induced scission and micelle orientation under shear. Two analysis steps are used: a model-independent spectral eigendecomposition reveals a decrease in micellar length, while regression analysis quantifies the evolution of the length distribution and mean length with shear rate. Additionally, micelle alignment increases with shear, quantified by the orientational distribution function. In conclusion, these findings provide experimental evidence for flow-induced alignment and scission, offering a new framework for understanding shear-induced phenomena in micellar systems.
Bioconversion and quantification of humic substances from low rank coals using indigenous fungal isolates
The major portion of coals around the world belongs to lignitic origin, and burning of these Low Rank Coals (LRCs) not only produces energy, but also release harmful gases into the environment. On the other hand, coal is a mega source of fossilized carbon compounds that can be utilized in diverse range of carbon/organic industries. Biosolubilization is one of the safest and economical way to consume these LRCs for the harvest of humic and fulvic acids for industrial applications. Present studies deal with solubilization of coal samples from five different mines of Punjab, Pakistan by indigenously isolated fungus. From a number of indigenously isolated fungal isolates, an isolate NF-2 was selected based on its solubilization potential on solid Sabouraud medium. The intensity of coal biosolubilization was found maximum for coal sample B-1 (59%) and lowest for sample B-26 (17.2%) with 5% (w/v) pulp density at 28 ± 2°C. Humification index value (A 4 /A 6 ) for biosolubilized products of coal sample B-1 was found 6.18 which was much higher than the A 4 /A 6 value (4.27) of commercially available standard humic acid. Biosolubilized products from coal sample B-1 was found maximum with 8681 mg/L humic acid concentration as determined by standard curve method. FTIR and FE-SEM analyses of sample B-1 treated with NF-2 confirmed the structural break down and solubilization of coal. Molecular identification of fungal isolate NF-2 showed 100% similarity to Aspergillus flavus. Here, this work paves the way to utilize lignitic coals for the environment friendly and low-cost production of humic acids by fungal biosolubilization as an alternate to chemical methods.
Valorization of waste polyolefins to butene, unsaturated fatty alcohols, and branched alkenes using CO 2 and plasma catalyst
Butene, branched alkenes, and short-chain unsaturated fatty alcohols are among the chemicals that have a wide range of industrial applications in the production of fuels, chemicals, and polymers. In this work, we produced these valuable commodity chemicals from waste plastics using a single-step plasma-catalytic process at atmospheric pressure CO 2 . The study shows that combining non-thermal plasma and zeolite could convert polyolefins at a temperature of 200 °C within 15 minutes, producing liquids rich in C 5 and C 6 branched alkenes and C 6 -C 8 unsaturated fatty alcohols. Additionally, gaseous products include a high yield of butene. Comparative studies indicate that combining CO 2 plasma with zeolite synergistically increases reaction rates and alters product compositions. Product selectivity was strongly dependent on reaction conditions, including plasma power, gas flow rates, reactor temperature, and catalyst loading. Furthermore, this process was applicable to common polyolefins and post-consumer polyethylene, indicating that the plasma catalytic approach has promising potential to valorize waste plastics and greenhouse gas CO 2 into versatile chemicals.
Thermodynamic modeling of aqueous polyelectrolyte solutions with mixed-valent counterions
Thermodynamic modeling of aqueous polyelectrolyte solutions with salts is of significant interest for many industrial applications. Here this study applies the polyelectrolyte Nonrandom Two-liquid activity coefficient model to aqueous polyelectrolyte solutions with mixed-valent counterions. A modified Delocalized Binding Theory was proposed to determine the polyion condensation fractions of the mixed counterions. This modified theory accounts for the electrostatic binding of the counterions on the polyion, the dissociation entropy of the counterions, and the electrostatic interactions between the uncondensed ionic species. Given the polyion condensation fractions, the critical value of Manning’s parameter ξ and the amounts of uncondensed polyions and counterions can be calculated along with the activity coefficients of mobile ions. The model successfully correlates experimental data for various aqueous polyelectrolyte systems with mixed-valent counterions.
Sustainable recovery of Rare Earth Elements (REEs) from coal and coal ash through urban mining: A Nature Based Solution (NBS) for circular economy
The demand for rare earth elements (REEs) has surged in recent years, driven by their crucial role in various industrial applications and their uneven geological distribution. As a result, urban mining from secondary resources, particularly coal and coal ash, has gained traction as a sustainable solution within a circular economy framework. This study highlights the significant presence of REEs in coal and coal ash, revealing that certain samples contain REE concentrations that rival traditional ores. Notably, coal ash has the potential to yield approximately 312,000 tons of REEs annually, far exceeding global demand. The research delves into advanced techniques for analyzing REEs, including elemental, isotopic, and mineralogical studies. Additionally, it explores innovative extraction methods such as the use of green solvents, nature-based solutions, and bioleaching and biosorption. By leveraging coal and its byproducts as secondary resources, this study underscores the opportunity to reduce dependence on conventional mining, enhancing the sustainability of REE recovery. Here, a comprehensive literature review was conducted to highlight technological advancements and emerging opportunities that can address current challenges in this field.
Antimony redox processes in the environment: A critical review of associated oxidants and reductants
The environmental behavior of antimony (Sb) has recently received greater attention due to the increasing global use of Sb in a range of industrial applications. Although present at trace levels in most natural systems, elevated Sb concentrations in aquatic and terrestrial environments may result from anthropogenic activities. The mobility and toxicity of Sb largely depend on its speciation, which is dependent to a large extent on its oxidation state. To a certain extent, our understanding of the environmental behavior of Sb has been informed by studies of the environmental behavior of arsenic (As), as Sb and As have somewhat similar chemical properties. However, recently it has become evident that the speciation of Sb and As, especially in the context of redox reactions, may be fundamentally different. Therefore, it is crucial to study the biogeochemical processes impacting Sb redox transformations to understand the behavior of Sb in natural and engineered environments. Currently, there is a growing body of literature involving the speciation, mobility, toxicity, and remediation of Sb, and several reviews on these general topics are available; however, a comprehensive review focused on Sb environmental redox chemistry is lacking. Herein, we provide a review of research conducted within the past two decades examining the redox chemistry of Sb in aquatic and terrestrial environments and identifies knowledge gaps that need to be addressed to develop a better understanding of Sb biogeochemistry for improved management of Sb in natural and engineered systems.
Enhancing phase identification in waste-to-energy fly ashes: Role of Raman spectroscopy, background fluorescence, and photobleaching
Waste-to-Energy (WTE) facilities incinerate ~11% (~ 222 Mt) of global solid waste, generating bottom and fly ashes. Landfilling these ashes is costly, and risks releasing contaminants into the environment. Instead, using WTE ashes in secondary industrial applications can circumvent such environmental risks. However, their secondary use is restricted by their inconsistent mineralogy, which may vary due to fluctuating waste composition and combustion conditions. Therefore, there is a need for rapid and reliable monitoring of WTE fly ash mineralogy. Here, we evaluate the employment of Raman spectroscopy for that purpose. Our initial investigation of 12 unique WTE fly ashes resulted in excessive fluorescence, rendering key Raman peaks obscure. To address this issue, we report that a mere 2 minutes of photobleaching can significantly reduce this fluorescence, facilitating the detection of calcite, calcium sulfate, zincite, and carbon – phases previously undetectable in original spectra. Furthermore, these results show the potential of Raman spectroscopy for rapid monitoring of WTE fly ash mineralogy, which could be beneficial in diverting these ashes from landfill.
Pd-CeO 2 catalyst facilely derived from one-pot generated Pd@Ce-BTC for low temperature CO oxidation
Due to the capacity to offer abundant catalytic sites within porous solids featuring high surface areas, metal-organic frameworks (MOFs) and their derivatives have garnered considerable attention as prospective catalysts in environmental catalysis. Here, to promote the industrial application of MOFs, there is an urgent need for an effective and environmental-friendly preparation approach. Breaking through the limitation of the traditional two-step preparation method that Pd was introduced to the already prepared Ce-BTC (Pd/Ce-BTC, BTC = 1, 3, 5 benzenetricarboxylate), in this work, we present a novel one-pot solvothermal method for synthesizing the Pd material supported by Ce-BTC (Pd@Ce-BTC). After pyrolysis in N 2 flow or air flow, Pd-CeO 2 catalysts derived from Pd@Ce-BTC exhibited much higher CO oxidation activity than those from Pd/Ce-BTC. Moreover, Pd/Ce-BTC and Pd@Ce-BTC pyrolyzed in N 2 flow (Pd/Ce-BTC-N and Pd@Ce-BTC-N) could better catalyze the oxidation of CO than Pd/Ce-BTC and Pd@Ce-BTC pyrolyzed in air flow (Pd/Ce-BTC-A and Pd@Ce-BTC-A). Further characterizations revealed that the abundant surface Ce 3+ species, rich surface adsorbed oxygen species and superior redox properties were the main reasons for the superior CO oxidation activity of Pd@Ce-BTC-N.
Generalizability analysis of tool condition monitoring ensemble machine learning models
Tool condition monitoring (TCM) is an essential research area for the optimization and automation of metal machining processes, and could help manufacturers reduce costs, production time, machine downtime, energy use, and part scrappage. However, TCM systems developed in prior studies have struggled to reach the high level of generalizability which is necessary for industrial applications. This study addresses TCM system generalizability to new machining conditions, how variations in machining and environmental conditions may be used to improve model generalizability, and ensemble machine learning techniques for TCM. Further, milling tool life experiments were conducted using various machining conditions, and the processes' sound, spindle power, and axial load signals were collected. Different machine learning models were evaluated for the prediction of tool wear levels, including four individual models and five ensemble models. Changes in cutting speed were found to display a large effect on model performance, while the chip load showed some effect, and the feed rate had little effect. A simulated noise data augmentation technique for model improvement is applied within TCM for the first time, and resulted in increased model generalizability and reduced overfitting. Across several performance metrics the extremely randomized trees ensemble machine learning model generally performed the best for this application, achieving a leave-one-group-out cross validation accuracy score of 92.4 %, a 10-fold cross validation score of 98.9 %, and an averaged accuracy across 11 generalizability tests of 87.3 %.
Gas tungsten arc welding and post weld heat treatment effects on microstructure and mechanical property of castable nanostructured alloy steel
Here, this paper details the first study of castable nanostructured alloy (CNA) steel gas tungsten arc weldability and the post-weld heat treatment (PWHT) effects. Effects of welding heat input, thermal cycles, and PWHT on microstructures, microhardness distributions, room temperature tensile properties, and fracture characteristics are discussed. Results show that CNA steel exhibits excellent weldability (i.e., no indication of welding defects and reasonable tensile properties). The welded joint exhibited heterogeneous microstructures with δ-ferrite as well as large microhardness variation and fluctuation. The welded joint yield and ultimate tensile strengths were similar to those of the base metal, but the elongations decreased by 30 %. However, with normalization and tempering PWHT, the δ-ferrite was eliminated, microstructure was modified, hardness was unified, and joint ductility was restored. The study indicated that the CNA reduced-activation ferritic-martensitic steel owns excellent weldability, and PWHT is needed for the industrial application of welded structures.
Probing solid-state reaction through microstrain: A case study on synthesis of LiCoO 2
Solid-state reaction has been widely adopted as a low-cost and scalable approach to synthesize inorganic materials for industrial applications. However, a special caution must be paid to carefully control the synthesis condition in order to obtain final products with desired structure and physical/chemical properties. In this work, LiCoO 2 was investigated as a model material to illustrate the complexity of the solid-state reaction, as well as its condition control. Taking the advantage of the high flux and high penetration capability of synchrotron X-ray source, in-situ high-energy X-ray diffraction was deployed to investigate the structural evolution of materials during the solid-state reaction while ex-situ high-resolution X-ray diffraction was utilized to quantify the residual microstrains of LiCoO 2 . It is shown that the microstrain is a sensitive indicator to probe the completeness of the solid-state reactions, and that it also provides a more quantitative way to establish the structure-property relationship of materials. It can serve as a sensitive indicator for the rational design of synthesis process for functional materials.
Characterization of ultrafast-laser ablation of micro-structures in Li-ion battery anode and cathode materials: Morphology, rate, and efficiency
Characterization of the rate and quality of ultrafast-laser ablation of Li-ion battery (LIB) electrode materials is presented for a collection of common and next-generation electrodes. Laser ablated micro-structures on the surface of LIB electrodes have been shown to provide dramatic enhancement of high-rate capability and electrode wetting. However, industrial adoption is hampered by a lack of data enabling informed choice of laser parameters and predicting process throughput. Here this work bridges this gap by providing characterization of the ablation process at more laser parameters (laser fluence and number of pulses used) than are currently available in the literature. Further, we expand on previous graphite and lithium iron phosphate (LFP) ablation work by extending ablation characterization to new LIB materials, providing high data resolution and adopting new characterization metrics which are relevant for industrial application of this technology. Ablated pores are characterized by their ablated depth, volume, and how the depth and volume ablation rate changes as a function of pore depth. Finally, we provide a detailed characterization of the morphology of laser ablated micro-structures which informs how material and laser parameters affect the quality of laser-processed electrodes.
Impurity gas monitoring using ultrasonic sensing and neural networks: forward and inverse problems
Ultrasonic sensing is a non-invasive technique for monitoring impurity gas composition in various industrial applications where safety and regulatory compliance are crucial. In this study, ultrasonic sensing and neural networks were used to analyze impurity gases (i.e., air and argon) in helium. An experimental platform was established to acquire ultrasonic data. In the forward problem, an artificial neural network (ANN) model was used to forecast the response and time-of-flight (TOF) based on the excitation, and argon and air concentrations. The inverse problem was solved using a convolutional neural network (CNN) to predict the argon and air concentrations given the ultrasonic response and excitation. The results showed that the ANN accurately predicted the ultrasonic response and the change in TOF with concentration. As the air concentration was increased from 0 to 9.8%, the TOF sensitivity to detect argon decreased by 39.8% and 16.1% from ANN and sound speed theory, respectively. The CNN demonstrated high accuracy in predicting concentrations for inputs in the testing dataset. The application of the trained CNN indicated that it over-predicts air concentration while under-predicting the argon concentration. To improve accuracy, the predicted air and argon concentrations should be corrected by -0.992% and 1.027% bias, respectively.
Modeling of nitrogen fixation and polymer production in the heterotrophic diazotroph Azotobacter vinelandii DJ
Nitrogen fixation is an important metabolic process carried out by microorganisms, which converts molecular nitrogen into inorganic nitrogenous compounds such as ammonia (NH 3 ). These nitrogenous compounds are crucial for biogeochemical cycles and for the synthesis of essential biomolecules, i.e. nucleic acids, amino acids and proteins. Azotobacter vinelandii is a bacterial non-photosynthetic model organism to study aerobic nitrogen fixation (diazotrophy) and hydrogen production. Moreover, the diazotroph can produce biopolymers like alginate and polyhydroxybutyrate (PHB) that have important industrial applications. However, many metabolic processes such as partitioning of carbon and nitrogen metabolism in A. vinelandii remain unknown to date. Genome-scale metabolic models (M-models) represent reliable tools to unravel and optimize metabolic functions at genome-scale. M-models are mathematical representations that contain information about genes, reactions, metabolites and their associations. M-models can simulate optimal reaction fluxes under a wide variety of conditions using experimentally determined constraints. Here we report on the development of a M-model of the wild type bacterium A. vinelandii DJ (iDT1278) which consists of 2,003 metabolites, 2,469 reactions, and 1,278 genes. We validated the model using high-throughput phenotypic and physiological data, testing 180 carbon sources and 95 nitrogen sources. iDT1278 was able to achieve an accuracy of 89% and 91% for growth with carbon sources and nitrogen source, respectively. This comprehensive M-model will help to comprehend metabolic processes associated with nitrogen fixation, ammonium assimilation, and production of organic nitrogen in an environmentally important microorganism.
Mapping TpPa-1 covalent organic framework (COF) molecular interactions in mixed solvents via atomistic modeling and experimental study
Complex solvent environments continue to limit the widespread adoption of organic solvent nanofiltration (OSN) in many chemical industry applications. In this paper we employ a commercially available covalent organic framework (COF), TpPa-1, and force field models to molecularly map separation performance of TpPa-1 membrane in mixed solvents. To minimize time and length scale mismatch between atomistic modeling and experiments, solvent permeance was normalized with water in modeling and experimental results to enable direct comparison. Model outputs, such as organic solvent permeance and solute rejection rate, matched well with filtration results. Since the atomistic models assume that all mass transfer is via through-pore transport, the discrepancies between modeling and experimental results provide insights on the effect of linear polymer defects, adsorption and interstitial mass transfer on polycrystalline COF membrane performance. In sum, force field models can serve as digital twins of COF membranes to simulate separation processes while capturing the effects of COF structure, chemistry, and crystallinity on membrane performance in complex organic solvent environments. Finally, this approach will provide insight into future COF design and synthesis for persisting separation challenges.