Stone Dust Research Articles (Page 1)

The Combined Role of Silanols and Oxidative Stress in Determining Engineered Stone Dust Toxicity

This study presents a novel approach to optimizing concrete block production by utilizing coconut shells and coconut shell ash as sustainable alternatives to stone dust and cement. Using classical experimental-mathematical modeling and exponential regression analysis, the optimal material proportions and their influence on the mechanical strength of concrete blocks were determined. Chemical composition tests indicated that incinerating coconut shells enhanced key pozzolanic components—silicon oxide, iron oxide, sulfur oxide, and potassium oxide—significantly improving the ash's pozzolanic reactivity and the durability of the concrete mix. The results reveal a critical threshold for incorporating these materials: beyond certain proportions, compressive strength declines. Specifically, strength ranged from 16.14 to 26.77 N/mm² at 100% replacement and from 0.32 to 1.54 N/mm² at 75% replacement. A non-linear regression model based on 42 observation points was developed, with 39 used for model training. The model accurately predicts performance and indicates an ideal mix consisting of 82.58% stone dust, a water–cement ratio of 0.6039, 82.5% cement, 17.5% coconut shell ash, and 17.42% coconut shells. This mix achieved a compressive strength of 3.17 N/mm² after 14 days of curing, exceeding the required 2.9 N/mm². The practical implications of these findings suggest that this mixture is cost-effective, utilizing low-cost coconut shells instead of more expensive materials like cement and stone dust. This approach promotes the use of sustainable materials in concrete production, supports waste management and conservation efforts, and aligns with circular economy practices in construction. Furthermore, the developed non-linear regression model serves as a valuable tool for integrating agricultural byproducts into concrete block production, providing a reliable method for predicting concrete performance and enabling better material selection in future applications. Generally, this study offers recommendations for identifying the optimal concrete block mix, enhancing circular economy practices, and minimizing dependence on non-renewable resources

Aim: Artificial Stone Dust (ASD) exposure has been identified as a significant health risk for workers, leading to oxidative stress, inflammatory responses, and potential systemic autoimmune diseases due to its high crystalline silica content. The aim of this study is to identify the impact of ASD on the permeability of alveolar epithelial cells and the mechanisms underlying particle translocation across the alveolar membrane remain unexplored. Methods: The acute toxicological effects of ASD on human bronchial submucosal gland cells CALU-3 cells in vitro were investigated to assess its impact on epithelial barrier integrity, in comparison to crystalline silica particles (Min-U-Sil®5). Results: Exposure to ASD increased oxidative stress, evidenced by heightened Reactive Oxygen Species (ROS) levels and Heme Oxygenase-1 (HO-1) gene expression in CALU-3 cells, exceeding effects observed with Min-U-Sil®5. Notably, ASD exposure resulted in a significant decrease in Transepithelial Electrical Resistance (TEER), indicating compromised epithelial barrier integrity, especially at higher concentrations (3.7 mg,18.5 mg and 37 mg) after 24, 48 and 72 h. These findings were not paralleled by a decrease in cell viability, underscoring a specific effect on cellular barrier function rather than cytotoxicity. Conclusions: Our study reveals that ASD induces oxidative stress and disrupts epithelial barrier integrity in vitro, potentially contributing to systemic translocation of particles and subsequent health effects. These findings underscore the need for a rigorous protective measure for workers and highlight potential biomarkers of ASD-induced cellular damage.

ABSTRACT Engineered stone (ES) fabrication generates respirable dust containing crystalline silica (CS), linked to accelerated silicosis outbreaks. Mechanisms underlying this toxicity, particularly the role of particle aging, remain unclear. In the occupational setting, workers are exposed to engineered stone dust (ESD) upon generation by cutting and grinding ES; however, ESD-initiated toxicity is frequently studied in labs using aged particles. This study aimed to compare radical generation and in vitro cytotoxicity of fresh versus aged ESD. Three different respirable ES types (ES A: 60% CS; B: 20%; C: 0%), granite (30%), and Min-u-Sil 5 (MS5, 99.5%) were generated using an automated cutting system and analyzed either freshly stored under N2 at −80°C or after aging in air at room temperature for 2 weeks. RAW 264.7 macrophages were exposed to particles (10 µg/well, 100 µg/ml, 31.25 µg/cm2, 24 hr), and viability, apoptosis, necrosis, and intracellular reactive oxygen species (ROS) were measured. Fresh ESD/granite exhibited significantly higher electron paramagnetic resonance (EPR) radical signals than aged counterparts and MS5. Fresh ES/granite reduced macrophage viability, while aged materials/MS5 did not. Apoptosis increased with all particles where fresh/aged difference occurred only in ES B. Necrosis rose markedly with fresh ES A. Intracellular ROS was elevated by some materials, but N-acetylcysteine (NAC) antioxidant failed to prevent cytotoxicity induced by fresh particles. In conclusion, freshly generated ESD displayed greater radical-generating capacity and distinct cytotoxic effects compared to aged ESD, influenced by factors beyond CS content. ROS-independent mechanisms appear crucial for acute cytotoxicity. These findings indicate particle aging as a critical factor in ESD toxicological assessment.

The research investigates how quartz powder addition affects the physical and mechanical properties of concrete paving blocks made from cement, fine aggregate, water, and coarse aggregate mixtures. Quartz powder was evaluated as a waste material-effective alternative to conventional stone dust, with testing conducted on compressive strength, abrasion resistance, bulk density, absorption, and visual inspection parameters. The findings demonstrate that the optimal 10% quartz powder addition significantly improves paving block properties, achieving Class B according to Indonesian National Standard SNI 03-069, which requires a minimum compressive strength of 17 MPa and designates suitability for pedestrian traffic areas, residential walkways, and light vehicular applications. The enhanced performance is attributed to the high silica content in quartz powder, which strengthens cementitious matrix bonds, while Class B ensures compliance with construction specifications for moderate-stress environments, providing adequate durability and structural performance for typical urban infrastructure applications. This research validates quartz powder as a viable, waste material admixture that maintains required quality standards while potentially reducing manufacturing costs through waste material utilization.

Silica and other harmful elements such as lead (Pb2+), manganese (Mn2+), cadmium (Cd2+), aluminium (Al3+), zinc (Zn2+), iron (Fe2+) are present in stone industries which include stone reshaping for different economical purposes. These toxicants cause serious diseases in workers in the industry and affect their endocrine system by unbalancing their hormonal concentration. Objective: This randomized case-control investigation was carried out to assess the deleterious effects of toxins and stone dust released from the stone industry, on thyroid (Tri-iodothyronine; T3 and tetra-iodothyronine; T4) and its regulatory hormone (Thyroid stimulating hormone; TSH) of the workers exposed to these fine dust particles daily during their work. Method: For this purpose, a comparison was made for hormones of the thyroid gland between workers exposed to dust toxins (experimental group) and the control group (never exposed to such pollutants daily). Blood samples for the experimental and control groups were collected from Marble Market, Ichra, Lahore and Quaid-e-Azam Campus, Punjab University, Lahore, respectively for the analysis of T3, T4, and TSH by using ELISA kits. Results: For statistical analysis of the results Unpaired t-test (Cl: 95%, P < 0.05) was used. A non-significant decrease in T3 and T4 concentration and a non-significant increase in TSH concentration were observed in the experimental group as compared to the control group. Conclusion: Current study concluded that people who are routinely exposed to stone dust and toxins are more likely to have changes in the endocrine system due to an imbalance in the hormonal system. Workers in such industries should always adopt protective measures to avoid inhalation of toxic particles.

Sustainable pavement construction has become increasingly important in India due to rapid infrastructure growth and the depletion of natural aggregates. This study investigates the performance of Dense Bituminous Macadam (DBM-II) mixes incorporating Reclaimed Asphalt Pavement (RAP) at 10%, 20%, and 30% replacement levels, with different fillers (stone dust, fly ash, and Ground Granulated Blast Furnace Slag) and bitumen VG-30 as binders. Laboratory investigations were carried out to evaluate the mechanical and durability characteristics of the mixes through Marshall Stability, Retained Marshall Stability (RMS), Indirect Tensile Strength (ITS), Tensile Strength Ratio (TSR) and Cantabro Abrasion Loss (CAL) and Resilient Modulus (MR). Results indicated that virgin binder demand reduced with RAP incorporation, although Marshall Stability decreased slightly due to the stiff aged binder in RAP. GGBS mixes consistently demonstrated superior stability, tensile strength, moisture resistance, and abrasion resistance compared to stone dust and fly ash mixes. All mixes satisfied MoRTH requirements for stability (>900 kg), RMS (>80%), TSR (>80%) and abrasion loss confirming their suitability for field applications. Overall, the findings establish that DBM-II mixes with up to 30% RAP, particularly when combined with GGBS filler, can deliver durable and environmentally sustainable pavement solutions.

Abstract To explore the fundamental mechanical mechanisms of the GFRP‐waste stone dust and alkali slag concrete‐steel double‐skin tubular columns (DSTC‐SCs), 15 composite columns were designed. The effects of parameter changes, like the thickness of GFRP and steel tubes, the hollow ratio, and loading methods, on the axial compression mechanical behavior of DSTC‐SCs were clarified. Additionally, mechanical performance indicators of the composite columns, such as failure characteristics, bearing capacity, and load–displacement curves, were acquired. The findings show that the change in the substitution rate of waste stone powder in the waste stone dust alkali slag concrete has a substantial influence on the axial compression performance of the columns. When the substitution rates of waste stone powder for slag and fine aggregates are 16% and 30%, respectively, the overall axial compression performance indicators of DSTC‐SCs are relatively optimal. As the hollow ratio of the columns rises, the bearing capacity decreases significantly. Based on the ultimate equilibrium theory approach, taking into account the non‐uniform confinement effect of the square steel tubes in the composite columns, an effective sectional confinement coefficient k0 was introduced, and the calculation formula for the axial compression bearing capacity of DSTC‐SCs was derived.

Modeling the impact of stone dust and hydrated lime in stabilizing high plasticity lateritic soil

Uncertainty During Ureteroscopy: Evaluating Surgeon Consensus on In Vivo Stone Dust After Laser Lithotripsy.

Optimizing properties of concrete containing stone dust and ceramic tile: a response surface methodology approach

This research utilized a multilayer perceptron model based on an artificial neural network (ANN) to improve concrete blocks by partially replacing stone dust and cement with coconut shell and coconut shell ash. From 35 experimental data points, 68.6 % were used for training and 31.4 % for testing. Chemical analyses showed that coconut shell ash, rich in silicon and other oxides, enhanced pozzolanic reactions as a cement substitute. Results indicated that higher proportions of coconut shell ash and shells reduced density, making the mixture lighter but potentially affecting structural integrity. However, increased replacement levels improved workability, shown by higher slump values. The curing period significantly impacted strength, with longer times leading to increased strength due to continued hydration. An optimal compressive strength of 14.17 N/mm2 was achieved with a 5 % replacement after 70 days. The ANN model demonstrated a sum square error of 0.198 and a high correlation coefficient of 0.983 when predicting strength. Optimal conditions for achieving a compressive strength of 33.4 N/mm2 were identified, underscoring the cost-effectiveness of using coconut byproducts instead of traditional materials.

Asphalt plant reclaimed powder is a common solid waste in road engineering. Reusing reclaimed powder as filler holds significant importance for environmental protection and resource conservation. The key factors affecting the feasibility of reclaimed powder reuse are its acidity/alkalinity and cleanliness. Traditional evaluation methods, such as the methylene blue test and plasticity index, can assess reclaimed powder properties to guide its recycling. However, these methods suffer from inefficiency, strong empirical dependence, and high variability. To address these limitations, this study proposes a rapid and precise evaluation method for reclaimed powder properties based on Fourier transform infrared spectroscopy (FTIR). To do so, five field-collected reclaimed powder samples and four artificial samples were evaluated. Scanning electron microscopy (SEM), X-ray fluorescence spectroscopy (XRF), and X-ray diffraction (XRD) were employed to characterize their microphase morphology, chemical composition, and crystal structure, respectively. Subsequently, FTIR was used to establish correlations between key acidity/alkalinity, cleanliness, and multiple characteristic peak intensities. Representative infrared characteristic peaks were selected, and a quantitative functional group index (Is) was proposed to simultaneously evaluate acidity/alkalinity and cleanliness. The results indicate that reclaimed powder primarily consists of tiny, crushed stone particles and dust, with significant variations in crystal structure and chemical composition, including calcium carbonate, silicon oxide, iron oxide, and aluminum oxide. Some samples also contained clay, which critically influenced the reclaimed powder properties. Since both filler acidity/alkalinity and cleanliness are affected by clay (silicon/carbon ratio determining acidity/alkalinity and aluminosilicate content affecting cleanliness), this study calculated four functional group indices based on FTIR absorption peaks, namely the Si-O-Si stretching vibration (1000 cm-1) and the CO32- asymmetric stretching vibration (1400 cm-1). These indices were correlated with conventional testing results (XRF for acidity/alkalinity, methylene blue value, and pull-off strength for cleanliness). The results show that the Is index exhibited strong correlations (R2 = 0.89 with XRF, R2 = 0.80 with methylene blue value, and R2 = 0.96 with pull-off strength), demonstrating its effectiveness in predicting both acidity/alkalinity and cleanliness. The developed method enhances reclaimed powder detection efficiency and facilitates high-value recycling in road engineering applications.

Compared with silicosis, there has been less research on other respiratory diseases in stone benchtop industry workers. Therefore, we explored respiratory symptoms, airflow obstruction and asthma, including associations with workplace dust exposure, in these workers. This study included voluntary participants from a stone benchtop industry screening programme conducted in Victoria, Australia, which included chest X-rays, respiratory function tests and a respiratory symptom questionnaire. Asthma status was determined based on self-report, and respiratory function tests were used to measure airflow obstruction. The associations between workplace dust exposure and respiratory symptoms were compared using logistic regression, adjusting for age, smoking status and silicosis. The prevalence of self-reported asthma in this cohort was 20% (90/450 workers). Workers with histories of high workplace dust exposure, even those without silicosis, were more likely to have self-reported asthma and to report respiratory symptoms. Those with obstruction but no bronchodilator response on respiratory function tests were more likely to report histories of high workplace dust exposure. For over half of workers with wheeze or difficulty breathing, symptoms improved at weekends and/or on holidays. There was a high prevalence of self-reported asthma in stone benchtop industry workers and an association between workplace dust exposure and airflow obstruction without bronchodilator response, as well as self-reported asthma, independent of silicosis. These findings suggest a potential role of artificial stone dust exposure in the development of obstructive lung disease, in addition to silicosis.

Coal dust explosions are a significant threat to underground coal mines. To reduce this risk, the combustible coal dust is mixed with stone dust to increase the total incombustible contents (TIC). Conventional TIC measurement methods rely on time-consuming laboratory analyses, involving extensive sample preparation and chemical testing. In contrast, near-infrared (NIR) spectroscopy has emerged as a rapid, non-destructive alternative for TIC prediction. However, existing machine learning models for analysing high-dimensional spectral data often require extensive preprocessing, increasing the analysis complexity. In this study, we present a residual - Convolutional Neural Network (CNN) based method for end-to-end analysis of raw near-infrared (NIR) spectral data to reduce preprocessing requirements while accurately classifying the TIC levels. The model was evaluated using 300 coal/stone dust samples, with 100 coal samples sourced from various Australian coal mines. The deep learning model, configured with optimal nine residual blocks, demonstrated high accuracy in predicting high TIC samples (TIC ≥ 85%), achieving misclassification rates of 0.05 on the training set and 0.14 on the testing set, respectively. Two challenges were identified: class imbalance and spectral overlap. The low TIC samples (TIC < 70%) accounted for only 9% of the total dataset (27 out of 300), resulting in poor prediction for this underrepresented class. Additionally, significant spectral similarity with distinct TIC values reduced the model’s generalization ability. Despite these challenges, our study demonstrates the potential for developing a reliable and efficient end-to-end deep learning framework for TIC prediction, which would allow for a significant reduction in preprocessing efforts.

ABSTRACTThe study's primary objective is to investigate the potential utilization of waste stone dust generated during the processing of red stone as a filler material in polymer matrix composites. The main aim of the work is to develop a new class of environmentally friendly composite materials. In the present work, composites are fabricated with an epoxy matrix, and their physical and mechanical properties are analyzed. The hand lay‐up method was used to prepare the epoxy/red stone dust composite, and different sets were developed with red stone dust loadings of 10, 20, 30, and 40 wt.%. The properties evaluated in the work are a function of the loading and size of the red stone dust. For that, two different sizes of red stone dust (3.5 μm and 8 μm) and, accordingly, two sets of composites were fabricated. The density, void content, and affinity towards moisture of the samples increased with red stone dust content. The highest tensile and flexural strength is registered at 20 wt.% of red stone dust. The percentage elongation decreases with filler loading, whereas the modulus increases. The hardness and compressive strength show an increasing trend. The study also found that composites with smaller particles exhibit superior properties.

Predictive modeling of compressive strength in pond ash bricks reinforced with cement and stone dust using machine learning

Suction devices such as flexible and navigable suction ureteral access sheath (FANS) are promising tools to reach the zero-fragment rate (ZFR) after flexible ureteroscopy (FURS) and laser lithotripsy. FANS could especially be useful for lower pole stones (LPS), avoiding postoperative retained stone dust. Using Machine Learning (ML) models, we aimed to predict the ZFR after FURS with FANS and secondarily the possible access to the lower pole (LP). Data from patients who underwent FURSin 25 centers worldwide were prospectively collected (Aug 2023-Jan 2024). Exclusion criteria were abnormal renal anatomy and ureteral stones. ZFR and LP access were respectively defined as the total absence of residual fragments on computed tomography at 1-month follow-up and the ability to place the FANS into a LP calyx. After data normalization and splitting (training-test (80-20%)), Eight ML models were evaluated to predict separately ZFR and LPS, using binary classification. A total of 390 patients were included with a median age of 49 (36-61)years. The median stone volume was 1440 (1006-2219)mm3. 70,5% and 59% of patients were first time stone formers and pre-stented. The FANS could access the LP in 75,1% of cases. At 1-month, the ZFR was 56,7% (221/390). Above all ML models, ExtraTreesClassifier presented the highest accuracy (0,83), F1-score(0,85) and AUC (0,83) for ZFR prediction from preoperative data. The LP access was best predicted by the RandomForestClassifier (accuracy (0,86), F1-score (0,91) and AUC (0,76)). Based on preoperative data and ML, we can accurately predict the ZFR and LP access during FURS with FANS. Our models could improve the elective indications for FANS utilization.

Using stone dust as an improvement material for cement gravel column

Pulsed Thulium:YAG Laser for Lithotripsy.