Search Results (1,256)

Size-exclusion chromatography (SEC) or gel-permeation chromatography (GPC) is an essential tool for determining the molecular weight and polydispersity of water-soluble polymers, including biopolymers used in hydrogels, sealants, bioinks, and other biomedical materials. However, aqueous SEC of polyelectrolytes, i.e., charged polymers, is often complicated by non-size interactions among polymer chains, porous column beads, pore surfaces, frits, tubing, and mobile phase. Salt addition to eluent is commonly used to screen these interactions, but the minimum salt concentration required to restore reliable SEC behavior remains poorly defined, and excessive salt may introduce tailing, refractive-index artifacts, deposits, or instrument concerns. In this study, aqueous SEC with refractive index (RI) and right-angle light scattering (RALS) detection was used to evaluate the effect of salt ($\mathrm{N}{\mathrm{a}}_2\mathrm{S}{\mathrm{O}}_4$) concentration on poly(ethylene oxide) (PEO), a nominally neutral reference standard polymer, and sodium alginate as a model anionic biopolymer. PEO retained a single bell-shaped peak across the tested salt range, but its elution volume and SEC/RALS-derived molecular weights varied slightly with salt concentration, showing that even a nominally neutral reference polymer is affected by mobile-phase conditions. Alginate showed much stronger salt dependence: eluent at very low salt concentration produced broad, noisy, and convoluted chromatograms, whereas increasing salt concentration progressively narrowed the main peak. The first condition that produced a clear, approximately symmetric RI/RALS main peak was $6.25\times {10}^{-3} \mathrm{M}$ $\mathrm{N}{\mathrm{a}}_2\mathrm{S}{\mathrm{O}}_4$, identifying it as the minimum effective salt concentration for this alginate/column/instrument system. To rigorously validate these observations, we propose a set of both qualitative and quantitative peak analyses that objectively confirm the optimal mobile-phase conditions. Ultimately, these results provide a practical workflow for identifying the minimum effective salt concentration required for reliable SEC analysis of water-soluble polymers. Full article

The growing demand for sustainable construction materials has led to significant advancements in ultra-high-performance concrete (UHPC), with a particular focus on geopolymer-based systems as an alternative to conventional cementitious binders. This review explores the latest developments in sustainable Ultra-High-Performance Geopolymer Concrete (UHPGPC) by analysing key material composition, mechanical, durability and microstructural properties. The incorporation of ground granulated blast furnace slag (GGBFS), silica fume (SF), and fly ash (FA) has demonstrated notable improvements in compressive strength, durability, and workability. Additionally, the use of activators such as sodium silicate and sodium hydroxide optimizes geopolymerization, resulting in a denser microstructure and enhanced mechanical performance. This review highlights the critical role of fibre reinforcement in UHPGPC, where steel fibres (SFs) and hybrid fibres significantly enhance compressive and tensile strength, as well as crack resistance. The inclusion of waste materials such as rice husk ash and recycled glass promotes sustainability by reducing CO₂ emissions while maintaining structural integrity. However, higher waste-glass content may adversely affect bonding due to its smooth surface texture. The findings highlight the potential of UHPGC as a high-performance, eco-friendly alternative to traditional cement-based UHPC. By integrating industrial by-products and alternative activation techniques, UHPGPC can contribute significantly to the global shift towards sustainable and low-carbon construction materials. Full article

Background/Objectives: Exercise is increasingly recognized as a modulator of host–microbiome interactions, yet its role in irritable bowel syndrome (IBS) remains poorly characterized. Methods: In this prospective, single-arm, before-and-after interventional study, we used an integrated multi-omics approach based on metataxonomics and metabolomics to assess the effects of a structured 12-week moderate aerobic exercise program in 80 patients with mild-to-moderate IBS, stratified by Global Physical Capacity Score (GPCS). Biochemical and inflammatory markers have been gathered. Results: Exercise did not alter overall microbial diversity but selectively enriched short-chain fatty acid (SCFA)-producing taxa and remodeled the volatile organic compound (VOC) profile toward a more efficient metabolic state. Notably, conventional biochemical and inflammatory markers failed to distinguish response subgroups, whereas GPCS stratification revealed distinct microbial and metabolomic trajectories. Individuals with higher baseline physical capacity had higher acetate levels and lower levels of VOCs associated with dysbiosis and oxidative stress. Conclusions: Our results suggest that baseline physical capacity is a primary determinant of the microbiome’s responsiveness to exercise, challenging the reliance on static biochemical profiling. Despite the lack of a control group and the exploratory nature of some metabolomic signals, this study provides a framework for precision exercise interventions in IBS. Our work identifies GPCS as a clinically relevant stratification tool. The full trial protocol is registered on ClinicalTrials.gov under the identifier NCT05453084. Full article

Cerebral amyloid angiopathy (CAA) is a prevalent cerebral small vessel disease (CSVD) and prominent cause of vascular contributions to cognitive impairments and dementia (VCID). CAA is characterized by progressive accumulation of fibrillar amyloid β in cerebral vessel walls, leading to vascular wall degeneration, thrombotic occlusions, microbleeds, and perivascular inflammation causing cognitive deficits. Underlying mechanisms of CAA progression are poorly understood, and there exist no validated diagnostic biomarkers or therapeutic targets for CAA. Here, we performed proteomic mass spectrometry analysis of whole brain tissue and cerebral microvessel enriched fractions from the rTg-DI rat, a validated and well-characterized preclinical rat model of CAA, to identify potential targets and diagnostic markers related to vasculopathy progression in CAA. There were 92 increased and 104 decreased proteins identified in the rTg-DI whole brain samples, while 29 proteins were identified as increased in the enriched cerebral microvessel fractions. We identified several significant differentially expressed proteins in both sample types with commonly elevated proteins including HTRA1, APOE, APOD, CLU, MDK, and LAP3. This study also confirmed the differential expression of several proteins previously reported in isolated brain regions of rTg-DI rats, including ANXA3, APOD, APOE, CLU, CST3, CTSS, CTSD, CTSZ, GFAP, GSTA3, HTRA1, and ITGB2, with APOE, CLU, HTRA1, and GPC1 all reported in human CAA cases. We thus provide a “high-confidence” list of potential marker candidates from proteomic analysis in the rTg-DI rat model of capillary CAA type-1. Full article

The increasing generation of ceramic waste from manufacturing defects, construction activities, and demolition operations poses significant environmental and waste management challenges worldwide. This study presents a preliminary investigation into the incorporation of ceramic waste aggregates (CW) as partial and full replacement for natural coarse aggregates in fly ash-based geopolymer concrete (GPC) under water-curing conditions. Five mix compositions were prepared with ceramic waste aggregate replacement levels of 0%, 20%, 40%, 60%, and 100%. Fresh and hardened properties were evaluated using flow table and early-age compressive strength tests at 7 and 14 days. The 20% replacement mix achieved the best compressive strength value of 5.52 MPa at 14 days, slightly exceeding the control GPC mix (5.09 MPa) among the limited mixtures investigated in this preliminary study. However, higher replacement levels resulted in reduced compressive strength, which may be associated with increased porosity, weaker aggregate–matrix bonding, and limitations related to the adopted water-curing regime. Workability remained within acceptable flow ranges for most mixes, although reduced flowability was observed for the 40% replacement. The comparatively low strength values obtained across all mixtures may largely be associated with the absence of heat curing and the inclusion of additional water to improve workability, both of which likely limited the geopolymerization efficiency. Based on the comparatively low compressive strength values obtained, the investigated mixtures, in their current form, are only suitable for low-strength or non-structural applications rather than structural concrete applications. Overall, this study provides preliminary insights into the influence of ceramic waste coarse aggregates on the workability and early-age compressive strength behavior of fly ash-based geopolymer concrete under the adopted experimental conditions. Further optimization of the curing regimes, mix design parameters, and long-term mechanical and durability performance is necessary before broader engineering applicability can be established. Full article

Riveting quality inspection is critical for ensuring structural integrity and safety in aerospace, automotive, and civil engineering, as rivet defects during the riveting process may cause catastrophic failures in structural connections. This study focuses on the detection method for multi-type rivet head defects and aims to improve the performance of feature extraction and classification for various head defects. The research is carried out to develop a lightweight classification network with a Dynamic Screening Self-Attention (DSSA) mechanism for 3D point clouds. To achieve the rivet head dataset, we employ Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering to extract each target head data from the dataset of riveted plates. The head dataset can be further simplified using the Non-Maximum Eigenvalue Curvature Method (NMECM). In this way, redundant information can be reduced. The PointGhost network is then designed for the classification of head defects. It contains a sampling module with a Virtual Block Sampling (VBS) mechanism that reduces the computational complexity. In addition, there exists a feature extraction module with a Grouped Pointwise Convolution Ghost (GPC-Ghost) lightweight model that performs local and global feature learning, together with the DSSA mechanism to enhance the riveted head defects. Lastly, the severity levels of rivet protrusion and indentation are quantified using Principal Component Analysis (PCA) and the Total Least Squares (TLS) fitting algorithm. In terms of the experiment, six popular lightweight models are compared, wherein GPC-Ghost shows more significant performance, achieving a 4.31% higher mean accuracy than PointNet++, with less computational cost of 0.66 GFLOPs. Based on the comparative analysis of six attention mechanisms and seven classification networks, the PointGhost model possesses the highest mean accuracy of 99.49%, with an average misclassification rate of 1.19%. The method can balance the accuracy and efficiency effectively, demonstrating its potential for engineering inspection. Full article

Suberinic acids (SA) derived from birch outer bark are renewable feedstocks for bio-based polyols suitable for rigid polyurethane (PU) foams. Three SA fractions were prepared under different depolymerization conditions: acidification at pH 1 (SA1), pH 5 (SA2), and FeCl₃-assisted treatment (SA3), and their chemical composition was analysed by GC–MS, Py–GC/MS, and GPC–RID. Polyols derived from tall oil fatty acids (TOFA) or epoxidized TOFA with trimethylolpropane were used as the sole polyol components in foam formulations. The SA fractions differed in molecular weight distribution, affecting polyol processability. All foams exhibited similar limiting oxygen index (19–20) and cone calorimetry results, showing no statistically significant differences in flammability. This indicates that variations in depolymerization conditions, including polyphenolic content and removal of higher-molecular-weight fractions during FeCl₃ treatment, do not dominate fire performance under the studied conditions. SA3-based polyols showed the lowest viscosity and produced foams with optimal mechanical and thermal properties, while SA1 offered higher yield with comparable performance. These results demonstrate the feasibility of converting SA fractions into functional polyols for rigid PU foams and highlight the FeCl₃-treated SA3 fraction and SA1 as the most promising candidates for further development. Full article

Sunlight-driven UV weathering is a major transformation pathway of environmental microplastics, promoting surface oxidation, molecular degradation, embrittlement, and progressive fragmentation toward smaller size fractions. However, comparisons across studies remain difficult because weathering is often described using descriptors that probe different aspects of degradation without being clearly distinguished. Surface-sensitive oxidation metrics, such as carbonyl or oxidation indices (CI/OI), are frequently emphasized, whereas fragmentation and embrittlement are more directly governed by bulk molecular-weight loss, mechanical weakening, and particle-size evolution. This review examines UV weathering of common polymers through a coupled chemico-mechanical perspective relevant to the micro-to-nano transition. We distinguish surface chemical descriptors, bulk molecular and mechanical descriptors, and fragmentation-related metrics, and critically assess the analytical methods used to measure them, including FTIR, Raman spectroscopy, GPC/SEC, thermal methods, mechanical testing, and particle-size analyses. We argue that no single metric is sufficient to describe weathering progression, and that meaningful interpretation requires joint reporting of oxidation state, Mn/Mw changes, mechanical deterioration where available, and particle-size distribution as a function of cumulative or spectrum-weighted UV dose. We further propose a minimal QA/QC reporting framework including UV metadata, temperature, oxygen availability, blanks, replicates, recovery tests, and matrix-specific detection limits. By separating what different methods actually probe and linking them to fragmentation mechanisms, this review provides a more operational basis for interpreting UV-aged microplastics in environmental sampling and biomonitoring. Full article

A novel water treatment agent, ethylenediamine–benzenesulfonic acid-modified polyaspartic acid (PASP-S), was controllably synthesized using an amino ring-opening reaction. The controllable synthesis methods, conditions for polymerization degree, and the molecular weight of the new polymer were explored. The structure was characterized using Fourier-transform infrared spectroscopy (FT-IR), ¹H nuclear magnetic resonance (¹H-NMR), and gel permeation chromatography (GPC). The scale inhibition, corrosion inhibition, and fluorescence properties of the new polymer, as well as the corresponding mechanisms, were investigated using static scale inhibition tests, electrochemical measurements, X-ray photoelectron spectroscopy (XPS), density functional theory (DFT), and frontier molecular orbital (FMO) theory. The results indicate that PASP-S exhibits strong Ca²⁺ chelation ability and can effectively inhibit CaCO₃ and CaSO₄ scaling. At 50 mg/L, the scale inhibition efficiency for Ca₃(PO₄)₂ reaches 99.50%. At 30 mg/L, its corrosion inhibition efficiency is 33.19% higher than that of PASP. Unexpectedly, the polymer shows remarkable selective antibacterial activity. At 100 mg/mL, the inhibition rate against Escherichia coli (E. coli) is 71%, while no obvious inhibition is observed for Bacillus cereus. A good linear relationship is found between fluorescence intensity and concentration. Mechanistic studies demonstrate that PASP-S adsorbs on the scale surface, suppressing crystal growth and distorting crystal morphology. Meanwhile, it forms a protective film on the electrode surface, thus reducing the dissolution and corrosion of carbon steel. Full article

Geopolymer gel concrete (GPC) is a kind of environmentally friendly concrete, which has become a potential alternative material to replace ordinary concrete. Traditional mix design of GPC is carried out under experimental conditions, which is time-consuming and labor-intensive. Geopolymer concrete (GPC) is intended for use in hydraulic structures, which are often exposed to water environments. Water flow exerts significant abrasion and erosion on these structures. If the abrasion resistance (AR) of the material is poor, the service life and service quality of hydraulic structures will be substantially reduced under the action of water flow. Therefore, AR is a key performance indicator for GPC in hydraulic engineering applications. This abrasion resistance can be enhanced by using fibers (for example, steel fibers, polyvinyl alcohol (PVA) fibers, and basalt fibers) and nanomaterials. Furthermore, there is a complex nonlinear relationship between the proportions of fibers and nanoparticles added and the properties of GPC. In this study, the circular ring test method and the underwater steel ball test method were conducted to investigate the AR of nano-SiO₂ (NS) and hybrid fiber (NHF) reinforced geopolymer gel concrete (NHF-GPC). A backpropagation (BP) neural network (BPNN) model optimized by the Grey Wolf Optimizer (GWO) (GWO-BPNN) is established to predict the abrasion resistance strength (ARS) and the abrasion rate of NHF-GPC based on the circular ring test method. In addition, the ARS, abrasion rate, and average abrasion depth (AAD) based on the underwater steel ball test method were also predicted. The results indicate that the GWO-BPNN model demonstrates superior performance over the standard BPNN, exhibiting higher prediction accuracy, better fitting performance, and faster convergence speed. Specifically, for the circular ring test method abrasion rate prediction, GWO-BPNN reduced the root mean square error (RMSE) by 30.3% and lowered the mean absolute percentage error (MAPE) to 8.4%. The GWO-BPNN model established in this study can provide efficient and reliable theoretical support for the optimization of the NHF-GPC mix design. Full article

Single-use polypropylene (PP) food containers represent a rapidly growing waste stream characterized by compositional heterogeneity and microstructural defects. Conventional reactive compatibilization using isotactic maleic anhydride-grafted PP (iPP-g-MA) provides rigid crystalline anchoring but lacks the interfacial flexibility to accommodate complex micro-defects. Herein, we propose a defect-tolerant compatibilization strategy by developing a binary iPP-g-MA/aPP-g-MA masterbatch for real post-consumer rPP derived from food-service containers. The amorphous aPP-g-MA component is proposed to provide a compliant interfacial environment that accommodates stress concentrations associated with microscale defects, whereas the iPP-g-MA component contributes crystalline anchoring with the recycled PP matrix. This soft/hard interfacial architecture is supported by grafting-degree analysis, GPC, XRD, DSC crystallization behavior, and SEM fracture morphology. The 1:1 mass-ratio binary formulation shows a marked improvement in elongation at break to 200%, representing a 203% increase relative to the single-component iMA system. The notched Charpy impact strength is enhanced to 8.98 kJ m⁻², while tensile strength is retained at 20.9 MPa within the typical strength–ductility trade-off of polymer toughening. TGA shows no premature degradation within the melt-processing window, indicating adequate thermal stability for melt reprocessing. This study provides a compositionally tunable, data-supported route for high-value upcycling of heterogeneous post-consumer polyolefins. From an application viewpoint, the improved ductility-impact balance makes the material relevant to injection-moulded semi-structural products such as storage crates, appliance housings, and automotive interior panels. Full article

The coordination polymerization of 1,3-butadiene with half-metallocenes/MAO catalysts is a versatile route to polybutadiene, yet the kinetic impact of catalyst preparation remains poorly understood. This work compares CpTiCl₃/MAO systems prepared either in situ or by aging as a function of [MAO]/[Ti], temperature, and the presence of residual trimethylaluminum (TMA) in MAO. Aged catalysts display markedly higher activity than in-situ systems, achieving up to 99% conversion at [MAO]/[Ti] = 250 (vs. 34% in situ) while maintaining similar molecular weights and cis-1,4 microstructure (76–77%). Because the in-situ and aged systems were evaluated at different titanium concentrations, this activity difference should be interpreted as arising from both catalyst pre-conditioning and differences in effective Ti concentration. Time-resolved GPC coupled with chromatogram deconvolution reveals two coexisting macromolecular populations, associated with kinetically distinct chain-growth contributions. For aged systems, the corresponding apparent propagation rate constants remain of the same order of magnitude throughout the reaction, consistent with persistent catalytic heterogeneity rather than progressive site deactivation. The role of residual trimethylaluminum (TMA) in commercial MAO is clarified: TMA accelerates initial activation and enhances chain transfer processes, lowering molecular weight and broadening dispersity, but does not measurably affect cis-1,4 selectivity, which is governed by the ligand environment of CpTiCl₃. Overall, thermal aging and MAO conditioning emerge as effective tools to tune the kinetic behavior of CpTiCl₃/MAO catalysts without compromising microstructural control in polybutadiene synthesis. Full article

Hydrolyzed polyacrylamide stabilized oil-in-water emulsions are highly persistent because the polymer strengthens both continuous-phase rheology and the oil–water interfacial film, making demulsification difficult in polymer-flooding produced liquids. Here, an hydrolyzed polyacrylamide degrading bacterium, Delftia lacustris EPDB-8, was isolated, and its ability to destabilize hydrolyzed polyacrylamide-containing emulsions was investigated from molecular, bulk rheological, and interfacial perspectives. EPDB-8 effectively degraded HPAM, causing marked reductions in total organic carbon, total nitrogen, absolute zeta potential, and polymer molecular weight, with an approximately 63-fold decrease after 7 days. SEM, FT-IR, and GPC analyses showed that biodegradation proceeded through deamidation and random chain scission, collapsing the polymer network and generating low-molecular-weight fragments. Driven by bacterial hydrolyzed polyacrylamide degradation, these structural alterations disrupted the viscoelastic composite interfacial film formed by hydrolyzed polyacrylamide and indigenous surface-active species, directly causing emulsion stabilization to shift from polymer-assisted viscous and steric protection to a less effective asphaltene-dominated interfacial structure and thereby accelerating droplet aggregation, coalescence, and phase separation. Although bacterial cells exerted a transient particle-assisted interfacial effect, long-term emulsion stability remained governed by polymer integrity. This study establishes a mechanistic link between hydrolyzed polyacrylamide biodegradation and the rheological and interfacial evolution governing emulsion breakdown, providing a cost-effective and environmentally benign biological strategy for demulsification and treatment of polymer-flooding produced water. These findings offer practical guidance for the design of microbial-based produced-water treatment systems and contribute to the sustainable management of oilfield wastewater generated during enhanced oil recovery operations. Full article

This study investigates the prediction of carbon emissions from fly ash and ground granulated blast furnace slag-based geopolymer concrete (GPC) using advanced ensemble machine learning (ML) techniques. Although ML has been extensively utilized to model GPC’s mechanical performance, its application in estimating environmental impacts, specifically carbon emissions, is limited. The research employs six ensemble ML models, such as random forest, gradient boosting, extreme gradient boosting (XGB), CatBoost, and light gradient boosting machine (LGBM), including versions optimized using ant colony optimization (ACO). Among them, the ACO-enhanced XGB model demonstrated the highest predictive accuracy with a coefficient of determination (R²) of 0.97, with low prediction errors (MAE = 3.92, RMSE = 6.17). However, cross-validation and uncertainty analyses indicate that the performance differences among top models are relatively small. Conversely, LGBM exhibited the least predictive reliability. Feature importance analysis revealed that curing parameters, specifically initial curing time, curing temperature, and the dosage of dry sodium hydroxide, had the most influence on carbon emissions. To evaluate model robustness and interpretability, Monte Carlo simulation and Gaussian white noise analyses were conducted. Results confirmed that CatBoost and ACO–gradient boosting (ACO-GB) demonstrated greater stability under varying and noisy conditions, whereas XGB-based models, although highly accurate, were comparatively more sensitive to input variability. Overall, the research establishes a data-driven, efficient framework for quantifying carbon emissions in GPC, highlighting the importance of evaluating both predictive accuracy and model robustness, advancing sustainable material design through intelligent modelling. Full article

The buffalo is an important agricultural species due to its many productive characteristics, which encourage its use worldwide. Uncovering the processes of selective sweeps is critical for a comprehensive understanding of genomic mechanisms that influence phenotypic differentiation in buffalo. This study aims to refine signatures of selection in Bulgarian (BUL), Hungarian (HUN), and Romanian (ROM) buffalo breeds using runs of homozygosity (ROHs), the integrated haplotype score (iHS), the standardized log-ratio of the integrated site-specific extended haplotype homozygosity (EHH) between pairs of breeds test (Rsb), and cross-population EHH (XP-EHH) approaches. The SNP dataset of 160 genotypes from BUL, HUN, and ROM buffalo breeds was genotyped using the Axiom^® Buffalo Genotyping Array 90K from Affymetrix. By combining the ROH, iHS, Rsb, and XP-EHH methods, we identified many important genomic regions and candidate genes associated with milk production (SLC24A2, TMEM132C, and ALCAM), reproduction (CSMD1, NTS, PLIN2, GPC5, and FSHR), growth (MYOM2, CLN8, and RRAGA), immune response (METTL25, MLLT3, NAALADL2, and GAB2), and adaptation (ADAMTSL1) in BUL, HUN, and ROM buffalo breeds. Our findings highlighted selection signals and genes related to important economic traits in the BUL, HUN, and ROM buffalo breeds, providing promising candidate genes for further research and inclusion in conservation and selection plans for these breeds. Full article