Search Results (144)

Deepwater drilling operations face critical challenges including narrow pore-fracture pressure windows, wellbore instability, and environmental concerns from drilling discharge. This paper presents a comprehensive systematic review of Riserless Mud Recovery (RMR) technology, tracing its evolution from its conceptual origins to its current applications, critically analyzing its technical limitations, and identifying future research directions. A systematic literature review was conducted covering peer-reviewed journals, SPE/IADC conference proceedings, industry technical reports, and independent academic studies from 1990 to 2025. Databases searched included Web of Science, Scopus, OnePetro, and Google Scholar, supplemented by Derwent Innovation Index for patents. After screening over 100 publications, approximately 60 references were selected following a two-step process excluding vendor-only promotional materials. Key findings reveal the following: (1) RMR technology has evolved through three distinct hardware generations—flexible hose systems, steel-pipe return lines with tandem pumps enabling deepwater breakthrough to 1419 m, and hybrid riser configurations for conceptual designs beyond 3000 m; (2) documented field benefits include 70% drilling fluid reduction, 9 days’ time savings per well, and successful mitigation of shallow geohazards across more than 1000 global well applications; (3) integration with casing-while-drilling and managed pressure cementing has enabled record-breaking performance of 1710 m in a single run; (4) independent academic validation confirms fatigue mechanisms affecting mud return lines; (5) systematic failure mode analysis identifies critical reliability issues in suction hoses, seals, and control systems; (6) quantitative economic analysis shows RMR cost-effectiveness depends on water depth, geological conditions, and environmental regulations. RMR technology has matured into a reliable drilling solution, yet its continued evolution requires addressing hardware limitations, developing dedicated well-control protocols, expanding to ultra-deepwater and emerging applications, and integrating digitalization for real-time optimization. Full article

The elemental composition of the vitreous humor may reflect physiological and pathological processes occurring in the eye. The objective of this study was to provide a complex multielemental analysis of human vitreous humor. Vitreous humor samples (n = 57) were collected post-mortem during autopsies. Inductively coupled plasma mass spectrometry (ICP-MS) was employed to quantify micro-, trace-, ultra-trace, and toxic elements. The study showed the occurrence of elements at the ppm (Na, K, P, Ca, Mg), ppb (Al, Rb, Zn, Fe, Sr, Cu), and ppt (Ce, La, Nd, Tb) levels. Hierarchical clustering using Ward’s method and k-means analysis revealed four distinct clusters, including two major clusters representing the baseline macro- and microelement profile characteristic for the studied population. Correlations between elements revealed statistically significant (p < 0.05) positive and negative correlations between elements with (I) chemical similarity Ce-La, Cs-Rb, Rb-K, Ca-P, Zn-Cu, and Cs-K; (II) a possible common environmental origin, Cd-P, and Rb-P; (III) involvement in similar biological processes as K-P; and (iv) a common geochemical origin and similar biological functions, i.e., Se-Zn. The study identified several quantitative trends in the demographic and medical characteristics of the participants. Alcohol users had significantly higher Zn concentrations than non-alcohol users; women had significantly higher Ca concentrations than men; higher BMI correlated positively with Cs and negatively with Be and Cr levels; and Cu, Sb, Cd, Se, and Ca concentrations increased with age. The presence of several toxic and potentially toxic elements was identified in the vitreous body: Al (>10 ppb); Cd, Cr, Pb, Ni, Mn; and Ba (<10 ppb); As, Hg, Sb, Tl, Bi, Be (<1 ppb). The study showed that, within a given geographic region, the accumulation profiles of toxic metals are quite homogeneous, indicating common sources of exposure. Full article

β-agonists are prohibited antibiotics that have raised concerns due to their illegal use in the livestock industry, posing potential toxicity risks to human health. For ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) analysis of β-agonists, effective sample pretreatment is a crucial and challenging process that dictates the overall reliability and sensitivity of the method. Thus, this study developed a reliable method utilizing dispersive solid-phase extraction (d-SPE) with NH₂-UiO-66 as a superior adsorbent, coupled with UPLC-MS/MS, to extract and quantify β-agonists in environmental water, swine urine, and milk. The synthesized NH₂-UiO-66 exhibited outstanding adsorption capacities (146.06–358.00 mg/g) towards the target analytes. The optimized method demonstrated excellent performance: low matrix effects (−13.10–15.30%), wide linearity (0.1–50 μg/L), low limits of detection (0.04–0.09 μg/L), and satisfactory recoveries (81.48–106.67%) with good precision (intra-day RSDs 1.51–6.24%; inter-day RSDs 2.06–10.96%). Adsorption mechanism studies revealed that the extraction process, which followed the Langmuir isotherm and pseudo-second-order kinetic models, was driven primarily by electrostatic interactions, π-π stacking, and hydrogen bonding. Moreover, the material could be reused up to 10 times, with satisfactory recoveries of 81.30% to 116.10%. The proposed NH₂-UiO-66-d-SPE-UPLC-MS/MS protocol is generic and provides a robust and practical solution for monitoring trace β-agonists in animal-derived foods and environmental samples, ensuring food safety and environmental health. Full article

The widespread application of glyphosate—the world’s most used herbicide—presents a significant environmental challenge due to its persistence and mobility within interconnected land–soil–water systems. This study addresses the limitations of traditional, discrete water monitoring by developing a predictive framework for glyphosate and its primary metabolite, aminomethylphosphonic acid (AMPA), in the agricultural context of Apulia, Southern Italy. The methodology integrates high-sensitivity analytical chemistry with advanced spatial intelligence. Water samples were analyzed using an optimized UHPLC–MS/MS framework with pre-column derivatization (FMOC-Cl), achieving an ultra-trace Limit of Quantification (LOQ) of 0.025 μg/L. To transition from point data to continuous spatial profiles, a hybrid Machine Learning (ML) architecture was implemented. The model utilized a suite of geospatial predictors, including land use (Corine Land Cover), Digital Elevation Models (DEMs), and slope characteristics extracted from river offset lines. A dual-modeling strategy was employed: Global Models (Random Forest, Gradient Boosting, and KNN) for regional trends and Individual Models for river segments exhibiting sufficient internal variability. Analytical findings (2018–2024) revealed that AMPA consistently exhibited higher mean concentrations than glyphosate, reaching peaks of 9.27 μg/L. This trend is primarily attributed to its superior environmental persistence and a half-life of up to 240 days, compared to the parent compound. Spatiotemporal analysis identified critical peaks in the second quarter for glyphosate and extreme surges in the fourth quarter for AMPA, particularly in the Cervaro basin. The Random Forest Regressor emerged as the most robust predictive tool, achieving a coefficient of determination (R²) of approximately 0.68 at the global scale and up to 0.75 for localized models where data density was sufficient. The integration of ML frameworks allows for the identification of contamination “micro-hotspots” and the mapping of probabilistic pollutant distribution along entire river reaches without additional sampling costs. This high-fidelity diagnostic tool provides a cost-effective strategy for environmental agencies to implement targeted mitigation and proactive water resource protection in Mediterranean agroecosystems. Full article

Polymerase chain reaction (PCR) is the primary method for virus detection; however, its complex preprocessing has prompted research into simpler immunoassay-based approaches. Among these, techniques using antibody-modified magnetic particles, exemplified by digital ELISA, provide ultra-high sensitivity comparable to PCR by efficiently capturing trace viruses and enabling concentration, washing, and transfer to microreactors. In this study, we evaluated the virus capture efficiency of antibody-modified magnetic particles based on quantitative PCR (qPCR). Influenza A virus (H1N1/A/Puerto Rico/8/1934) was tested with 1 μm magnetic beads modified with HA1 antibodies. As quantification becomes unreliable and difficult in an extremely low-concentration range near the detection limit of qPCR, low-concentration viral suspensions (10⁵ copies/mL) were mixed with particle dispersions (up to 5 × 10⁸ particles/mL) for 10 min, followed by magnetic separation and washing, and the remaining virus in each fraction was analyzed by qPCR. At the highest particle concentration, capture rates exceeded 80% relative to the initial suspension, indicating near-complete capturing when considering free nucleic acids. Time-course analysis showed that the capture rate reached saturation within 2 min, with approximately 90% of the saturation at 1 min. Furthermore, kinetic modeling of magnetic bead–virus binding reproduced experimental data. These findings demonstrate that short mixing times with high particle concentrations enable efficient virus capture, contributing to the development of rapid and highly sensitive immunoassay systems. Full article

Herbicides are widely used agrochemicals and are increasingly recognised as contaminants of emerging concern in aquatic environments due to their extensive application, environmental persistence, and potential ecological and human health impacts. Their determination in water presents significant analytical challenges, as these compounds occur at trace to ultra-trace levels and encompass a wide range of chemical properties, including highly polar and ionic species as well as transformation products. This review provides a critical overview of recent advances in separation technologies for the analysis of herbicides in water, based on peer-reviewed studies published between 2020 and 2025 retrieved from the PubMed and Scopus databases. The discussion focuses on developments in sample preparation, extraction strategies, chromatographic separation, and detection techniques, with particular attention to analytical performance and sustainability. The reviewed studies demonstrate that solid-phase extraction remains central to achieving the lowest detection limits, while miniaturised and greener extraction approaches are increasingly adopted to reduce solvent consumption and simplify workflows. Advances in chromatographic separation and detection, especially liquid chromatography coupled to tandem mass spectrometry, have further enhanced sensitivity and selectivity for a broad range of herbicides. Overall, this review highlights current analytical capabilities and emerging trends, outlining future directions for reliable and sustainable monitoring of herbicides in aquatic environments. Full article

The analysis of ground-penetrating radar data generally relies on the visual identification of structures on selected profiles and their interpretation in terms of buried features. In simple cases, inverse modelling of the acquired data set can facilitate interpretation and reduce subjectivity. These methods suffer from severe restrictions due to antenna resolution limits, which prevent the identification of tiny structures, particularly in forensic, stratigraphic, and engineering applications. Here, we describe a technique to obtain a high-resolution characterization of the underground, based on the forward modelling of individual traces (A-scans) of selected radar profiles. The model traces are built by superposition of Ricker wavelets with different polarities, amplitudes, and arrival times and are used to create reflectivity diagrams that plot reflection amplitudes and polarities versus depth. A thin bed is defined as a layer of higher or lower permittivity relative to the surrounding material, such that the top and bottom reflections are subject to constructive interference, determining the formation of an anomalous peak in the trace (tuning effect). The proposed method allows the detection of ultra-thin layers, well beyond the Rayleigh vertical resolution of GPR antennas. This approach requires a preliminary estimation of the instrumental uncertainty of common monostatic antennas and takes into account the frequency-dependent attenuation, which causes a spectral shift of the dominant frequency acquired by the receiver antenna. Such a quantitative approach to analyzing radar data can be used in several applications, notably in stratigraphic, forensic, paleontological, civil engineering, heritage protection, and soil stratigraphy applications. Full article

As rapidly developing consumer products, cosmetics confront challenges regarding safety, especially hazardous ingredients, like sex hormones. Prolonged exposure to trace sex hormones in cosmetics can inflict immeasurable damage to human health. To accurately detect the trace amounts of sex hormones in cosmetics, a reliable method was developed and validated using ultra-high performance liquid chromatography–mass spectrometry (UHPLC-MS/MS) with magnetic solid-phase extraction (MSPE) and isotope-labeled internal standards (IL-ISs). The conditions of sample pretreatment, chromatography, and mass parameters were systemically investigated. In the MSPE procedure, the commercial Fe₃O₄@HLB magnetic material was employed for sample pretreatment, which was beneficial for operation, as well as sample purification and analyte enrichment. The utilization of IL-ISs compensated for potential matrix effects and losses during sample preparation, thereby improving precision and accuracy. Based on the proposed MSPE technology, UHPLC-MS/MS can address the qualitative and quantitative analysis needs for target analytes in complex cosmetic matrices. At three fortification levels, recoveries were in the range of 71.7–116.2%, with a relative standard deviation (RSD) ranging from 1.6% to 8.3%. Furthermore, based on the method proposed here, a total of 116 batches of cosmetics were analyzed, and trace progestins and estrogens were discovered in 10 samples. The MSPE method, coupled with UHPLC-MS/MS using IL-ISs, was convenient, efficient, and feasible for detecting trace amounts of sex hormones in cosmetics. The method scored 0.66 (out of 1) on the AGREE metric, confirming its green profile. Based on the detected concentrations, a preliminary safety evaluation was performed to assess the potential health risks of residual progesterone in hair loss prevention cosmetics by calculating the margin of safety (MoS). Full article

Aflatoxin B₁ (AFB₁), a highly toxic and carcinogenic mycotoxin, poses significant public health risks due to its widespread contamination of staple food crops such as peanuts and maize. Although conventional lateral flow immunoassays (LFIAs) are widely employed for rapid on-site screening, their limited sensitivity frequently compromises accurate quantification at trace levels. To improve the analytical performance of LFIAs, we developed a novel time-resolved fluorescence-based lateral flow immunoassay (TRFN-LFIA) by integrating reverse artificial antigen labeling with time-resolved fluorescence signal amplification. This method enhances detection sensitivity and enables rapid, ultra-sensitive, visible, and quantitative determination of AFB₁ in peanut and maize samples. Under optimized conditions, the TRFN-LFIA achieved a visible limit of detection (vLOD) of 0.30 ng/mL (2.22 µg/kg), a quantitative limit of detection (qLOD) of 0.04 ng/mL (0.30 μg/kg), and a half-maximal inhibitory concentration (IC₅₀) of 0.09 ng/mL. Recoveries from spiked peanut and maize samples ranged from 81.33% to 117.86%, with coefficients of variation (CVs) below 13.04%. Analysis of 21 real samples (13 maize and 8 peanut samples) yielded results highly consistent with those obtained by liquid chromatography–tandem mass spectrometry (LC-MS/MS). Moreover, the method demonstrates significant advantages in terms of detection speed, cost-effectiveness, and operational convenience. Therefore, the results established the TRFN-LFIA method as a reliable and practical tool for on-site rapid detection of AFB₁ in contaminated food matrices, providing both a rapid and accurate approach for trace-level quantification and a novel strategy for enhancing the sensitivity of lateral flow immunoassays. Full article

In this study, electrodeposition (ED) combined with a spark discharge-facilitated laser-induced breakdown spectroscopy—referred to as SD-LIBS—was employed for highly sensitive detection of trace chromium (Cr) in aqueous solutions. First, trace Cr ions in water were preconcentrated on the outer surface of an ultra-pure aluminum base by ED, effectively addressing the issue of poor detection sensitivity when LIBS is applied directly to liquid samples. Subsequently, SD-LIBS was employed for the analysis of the chromium (Cr) deposited on the aluminum (Al) exterior, where the spark discharge significantly enhanced the emission intensities of the Cr (I) spectral lines centered at 425.43 nm, 427.48 nm, and 428.90 nm. Based on the calibration curves obtained from standard solutions, the limits for the detection (LoD) with respect to chromium (Cr) under different discharge voltages were determined. With the elevation of discharge voltage over the 0–2 kV range, the LoDs of the neutral chromium (Cr) line I corresponding to 425.43 nm and 428.90 nm, the concentration levels of 3.86 n and 5.13 ng decreased to 1.19 ng and 1.57 ng, respectively, with all values referenced per milliliter. These results demonstrate that spark discharge further improves the detection sensitivity of LIBS in aqueous environments. Overall, the combined ED+SD-LIBS approach exhibits excellent analytical performance for trace metal detection in water, offering promising potential for water quality monitoring applications. Full article

The Extra-Long Weir Construction method for deep foundation pit construction is crucial for urban underground development. However, as excavation projects become deeper and more complex, construction safety risks increase markedly. Existing monitoring technologies and numerical simulation models face persistent challenges: high uncertainty in risk occurrence, complex environmental interactions, and difficulties in extracting effective warning signals from multi-source data. To address these challenges, this study establishes a systematic risk evaluation framework comprising 6 primary and 29 secondary indicators through Fault Tree Analysis and develops a novel DL-MSD (Deep Learning and Multi-Source Data Prediction) model integrating CNN, ResUnit, and LSTM networks for spatiotemporal sequence analysis and multi-source data fusion. Validated using 6524 samples from the Jinji Lake Tunnel project, the model employs single-factor prediction for hazard source tracing and multi-factor fusion for comprehensive risk assessment. Results demonstrate exceptional performance: 90.2% average accuracy for single-factor warnings and 77.1% for multi-factor fusion, with, critically, all severe warnings (Level I risks) identified with zero omissions. Comparative analysis with T-S fuzzy neural networks, EWT-NARX, and Random Forest confirmed superior accuracy and computational efficiency. An integrated platform incorporating BIM and IoT technologies enables automated monitoring, intelligent prediction, and adaptive control. This study establishes a data-driven intelligent early warning framework that significantly improves prediction accuracy, timeliness, and reliability in deep foundation pit construction, marking a paradigm shift from reactive response to proactive prevention. The findings provide theoretical and methodological support for safety management in ultra-deep excavation projects, offering reliable decision-making evidence for enhancing construction safety and risk management. Full article

Aromatic amines such as 3-chloroaniline (3-CA) are toxic, persistent, and environmentally relevant water contaminants. Their reliable determination in aqueous systems has therefore become increasingly important. The monitoring of trace levels of these pollutants in complex water matrices typically necessitates a preconcentration step, most achieved via solid-phase extraction (SPE). However, conventional SPE sorbents often suffer from limited surface reactivity and slow adsorption kinetics, which compromise their performance at ultra-low concentrations. In contrast, nanomaterials offer a promising upgrade due to their high surface area, tunable chemistry, and rapid mass transfer behavior. In this work, mesoporous silica nanoparticles (MSNs) were synthesized via a green sol–gel route from sodium silicate precursor using polyethylene glycol template and then chemically functionalized with bisphenol A (BPA) to produce BPA-MSNs with π-rich and hydrogen-bonding active sites. Characterization using XRD, BET, FTIR, SEM/EDX, and TGA confirmed the successful synthesis and surface modification of the nanosorbent. BPA-MSNs achieved a maximum adsorption capacity of 30.2 mg/g toward 3-CA, fitting Langmuir and Jovanovic isotherm models. Kinetic analysis followed a pseudo-first-order model, indicating physisorption enhanced by π–π stacking and hydrogen bonding. The optimized dispersive SPE (D-SPE) method allowed a low detection limit (LOD = 0.016 mg/L), recovery of 73–85%, and precision below 5.3% RSD in tap, bottled, synthetic municipal wastewater and groundwater samples. The sorbent retained >90% efficiency over five reuse cycles, demonstrating strong potential as a reusable nanosorbent for preconcentration and remediation of aromatic amines in and treatment water analysis. Full article

Background: The introduction of the “trace” category in the Xpert MTB/RIF Ultra assay has significantly improved the sensitivity of molecular tuberculosis diagnostics. While it enhances sensitivity, especially in paucibacillary and extrapulmonary cases, its specificity remains debatable, making its interpretation outside select populations a topic of clinical uncertainty. Objectives: This study evaluates the diagnostic and clinical significance of trace-positive results obtained with the Xpert MTB/RIF Ultra assay in the context of a high-incidence TB setting, examining their association with clinical, imaging, and microbiological findings. Methods: A retrospective analysis was conducted on 65 samples with trace-positive Xpert Ultra results, collected over a six-year period from 59 distinct patients in a general hospital in Romania. Correlations were assessed with microscopy, culture, clinical features, imaging, treatment initiation, and prior TB history. A composite reference standard was used for diagnostic accuracy evaluation. Results: Of the 65 trace-positive samples, 29 (44.6%) were culture-positive and 5 (7.7%) were smear-positive. A high proportion of patients, 56 (94.9%), presented with TB-compatible symptoms, and 47 (79.6% of those with imaging) had highly suggestive radiological findings. Based on the composite reference standard, 47 patients (79.7%) were ultimately diagnosed with active TB. Anti-TB treatment was initiated in 44 patients (74.5%). Trace positivity was observed across various specimen types, including sputum, pleural fluid, and cerebrospinal fluid. Conclusions: In high TB burden environments, trace-positive Xpert Ultra results frequently reflect true disease when interpreted within the appropriate clinical and imaging framework. Our findings indicate that, in regions with high tuberculosis incidence such as Romania, trace-positive Xpert Ultra results may contribute meaningfully to clinical decision-making when interpreted alongside clinical and radiological findings, in alignment with current WHO guidance. Full article

Transmission lines often traverse mountainous regions prone to frequent geological hazards, making it of great practical significance to analyze the spatial distribution patterns of landslide traces along the transmission line corridors. This study focuses on the Hubei section of the ±800 kV ultra-high-voltage (UHV) transmission line from the upper reaches of the Jinsha River to Hubei. Based on high-resolution remote sensing imagery provided by Google Earth, a landslide traces inventory was constructed through visual interpretation. In addition, 13 factors, such as elevation, slope, aspect, relief, soil type and land cover, were selected to analyze the spatial distribution of landslides. The results indicate the following: (1) There are at least 18,598 landslides in the study area, with a total area of approximately 2671.82 km². The spatial distribution is uneven, exhibiting a general pattern of “dense in the west, sparse in the east”. The maximum landslide number density (LND) reaches 4.16 km⁻², and the maximum landslide area percentage (LAP) is 0.83%. (2) Landslides are predominantly distributed in areas with elevations of 278–1059 m, slope gradients of 20–30°, northwest and southeast aspects, surface roughness values of 400–600, Triassic and Jurassic strata, evergreen coniferous forest and sparse forest, as well as lixisols and ferrallitic soil. This study established a landslide traces database for the region, preliminarily revealing the distribution characteristics of landslides and their dominant controlling factors. It provides a scientific basis for geological hazard risk assessment and prevention for UHV transmission lines. Full article

Pharmaceuticals and personal care products (PPCPs) are recognized as emerging contaminants of concern, even at ultra-trace concentrations. However, the current detection systems are prohibitively expensive and typically rely on labor-intensive, lab-based workflows that lack automation in sample pretreatment. In this study, we developed a robotic and on-flow solid-phase extraction (ROF-SPE) system, fully integrated with online liquid chromatography-tandem mass spectrometry (LC-MS/MS), for the on-site and real-time monitoring of 16 PPCPs in wastewater effluent. The system automates the entire pretreatment workflow—including sample collection, filtration, pH adjustment, solid-phase extraction, and injection—prior to seamless coupling with LC–MS/MS analysis. The optimized pretreatment parameters (pH 7 and 10, 12 mL wash volume, 9 mL elution volume) were selected for analytical efficiency and cost-effectiveness. Compared with conventional offline SPE methods (~370 min), the total analysis time was reduced to 80 min (78.4% reduction), and parallel automation significantly enhanced the throughput. The system was capable of quantifying target analytes at concentrations as low as 0.1 ng/L. Among the 16 PPCPs monitored at a municipal wastewater treatment plant in South Korea, only sulfamethazine and ranitidine were not detected. Compounds such as iopromide, caffeine, and paraxanthine were detected at high concentrations, and seasonal variation patterns were also observed This study demonstrates the feasibility of a fully automated and on-site SPE pretreatment system for ultra-trace environmental analysis and presents a practical solution for the real-time monitoring of contaminants in remote areas. Full article