Potential Method Research Articles

Improved Discrimination of Mass Spectral Isomers Using the High-Dimensional Consensus Mass Spectral Similarity Algorithm.

This study employs a high-dimensional consensus mass spectral (HDCMS) similarity scoring technique to discriminate isomers collected using an electron ionization mass spectrometer. The HDCMS method was previously introduced and applied to the discrimination of mass spectra of constitutional isomers, methamphetamine and phentermine, collected with direct analysis real-time mass spectrometry (DART-MS). The method formulates the problem of discriminating mass spectra in a mathematical Hilbert space and is hence called "high dimensional." It requires replicate mass spectra to build a Gaussian model and evaluate the inner products between these functions. The resulting measurement variability is used as a signature by which to discriminate spectra. In this work, HDCMS is tested on electron impact ionization (EI) mass spectra of 7 terpene and terpene-related (C10H16 and C10H14) isomers with experimental retention indices that differ by less than 30 and with traditional cosine similarity scores greater than 0.9, on a scale of 0 to 1, when compared with at least one other compound in the test set. Using identical instrument parameters, 15 replicate gas chromatography-electron ionization-mass spectrometry (GC-EI-MS) spectra of each isomer were collected and separated into distinct library and query sets. The HDCMS algorithm discriminated each isomer, indicating the method's potential. Because the method requires replicate measurements, observations from a simple heuristic study of the number of replicates required to discriminate these isomers is presented. The paper concludes with a discussion of compound discrimination using HDCMS and the benefits and drawbacks of applying the method to EI-MS data.

Minimally invasive exoscope-assisted coccygectomy: A novel approach for chronic refractory coccydynia

BackgroundThis technical note presents a novel minimally invasive exoscope assisted coccygectomy to treat chronic refractory coccydynia. Traditional treatments often fail to provide adequate relief for this debilitating condition, prompting the need to explore surgical approaches. Case DescriptionA 40-year-old female patient with persistent pain unresponsive to conservative treatments underwent this advanced procedure. Utilizing the Synaptive exoscope-microscope system, the surgery allowed for precise dissection and removal of affected coccygeal segments, with a focus on minimizing skin and soft tissue disruption to optimize wound healing and surgical site pain. ConclusionPostoperative recovery showcased significant pain reduction and improved quality of life, emphasizing the method's potential for better outcomes and fewer complications. Despite the promising results, the limitations of a single-case study necessitate further research to establish long-term effectiveness across a broader patient population.

Dissolution of impurities in sodium gadolinium molybdate NaGd(MoO<sub>4</sub>)<sub>2</sub>

Impurity defects simulation in sodium-gadolinium molybdate NaGd(MoO4)2 was carried out using a method of interatomic potentials. The dissolution energies of tri-, di- and monovalent impurities were estimated. The dependences of the dissolution energy on the ionic radius of the impurity were plotted. For heterovalent substitutions, the most energetically favorable mechanism for charge compensation has been found, both due to intrinsic crystal defects and according to the conjugate isomorphism scheme. The positions of the most probable localization of defects are determined. The effect of disordering of sodium and gadolinium ions at equivalent positions on positional differences in the energy of defects is estimated. A comparison of the solubility of impurities in NaGd(MoO4)2 and its isostructural CaMoO4 indicates that, although isovalent substitutions are energetically more favorable than heterovalent ones, the mechanism of conjugate isomorphism, which ensures electrical neutrality, can equalize these processes.

Hybrid Optimization Path Planning Method for AGV Based on KGWO.

To address the path planning problem for automated guided vehicles (AGVs) in challenging and complex industrial environments, a hybrid optimization approach is proposed, integrating a Kalman filter with grey wolf optimization (GWO), as well as incorporating partially matched crossover (PMX) mutation operations and roulette wheel selection. Paths are first optimized using GWO, then refined with Kalman filter corrections every ten iterations. Moreover, roulette wheel selection guides robust parent path selection, while an elite strategy and partially matched crossover (PMX) with mutation generate diverse offspring. Extensive simulations and experiments were carried out under a densely packed goods scenario and complex indoor layout scenario, within a fully automated warehouse environment. The results showed that this hybrid method not only enhanced the various optimization metrics but also ensured more predictable and collision-free navigation paths, particularly in environments with complex obstacles. These improvements lead to increased operational efficiency and safety, highlighting the method's potential in real-world applications.

Impact of Mechanochemical Activation (MChA) on Characteristics and Dye Adsorption Behavior of Sawdust-Based Biocarbons.

The increase in environmental pollution due to the development of industry and human activity has resulted in intensive development of research on the possibility of its purification. A very effective method is the pollutants' adsorption from the air and water environment. For adsorption to be effective, materials with a specific structure and a well-developed surface decorated with numerous functionalities, e.g., biocarbons (BC), are necessary. An effective method of activating biocarbons is mechanochemical milling, an environmentally friendly procedure. This paper describes the possibility of using mechanochemical activation (MChA) of non-porous biocarbons to develop surface and porosity for their use in processes of pollutant adsorption. BC was characterized based on N2 adsorption, thermogravimetry (TGA), SEM/EDS imaging, Fourier (ATR-FTIR) and Raman spectroscopies, as well as titration using the Boehm method and determination of zeta potential. The adsorption capacity of BC for methylene blue (MB) was studied. It was proven that the solvent-free MChA made it possible to obtain microporous biocarbons, causing an intensive increase in the surface area and pore volume and the generation of oxygen functionalities. The biocarbons had predominantly acidic (mainly carboxylic) or basic functionalities and exhibited an amorphous structure. BC proved to be effective in adsorbing MB from aqueous solutions.

Multi-class plant hormone HILIC-MS/MS analysis coupled with high-throughput phenotyping to investigate plant-environment interactions.

Plant hormones are chemical signals governing almost every aspect of a plant's life cycle and responses to environmental cues. They are enmeshed within complex signaling networks that can only be deciphered by using broad-scale analytical methods to capture information about several plant hormone classes simultaneously. Methods used for this purpose are all based on reversed-phase (RP) liquid chromatography and mass spectrometric detection. Hydrophilic interaction chromatography (HILIC) is an alternative chromatographic method that performs well in analyses of biological samples. We therefore developed and validated a HILIC method for broad-scale plant hormone analysis including a rapid sample preparation procedure; moreover, derivatization or fractionation is not required. The method enables plant hormone screening focused on polar and moderately polar analytes including cytokinins, auxins, jasmonates, abscisic acid and its metabolites, salicylates, indoleamines (melatonin), and 1-aminocyclopropane-1-carboxylic acid (ACC), for a total of 45 analytes. Importantly, the major pitfalls of ACC analysis have been addressed. Furthermore, HILIC provides orthogonal selectivity to conventional RP methods and displays greater sensitivity, resulting in lower limits of quantification. However, it is less robust, so procedures to increase its reproducibility were established. The method's potential is demonstrated in a case study by employing an approach combining hormonal analysis with phenomics to examine responses of three Arabidopsis ecotypes toward three abiotic stress treatments: salinity, low nutrient availability, and their combination. The case study showcases the value of the simultaneous determination of several plant hormone classes coupled with phenomics data when unraveling processes involving complex cross-talk under diverse plant-environment interactions.

Improved fault detection and classification in PV arrays using stockwell transform and data mining techniques

The growing integration of photovoltaic (PV) systems into the power grid necessitates reliable fault detection and classification mechanisms to ensure operational efficiency and safety. Fault detection in photovoltaic (PV) arrays is crucial for maintaining optimal system performance and ensuring the reliability of solar power generation. This paper proposes a novel approach for fault detection in PV arrays by employing the Stockwell transform in combination with various data mining techniques. The Stockwell transform is an advanced time-frequency analysis tool that allows for enhanced feature extraction from time-series data. By applying the Stockwell transform to the PV array's operational data, valuable frequency-domain information is extracted, enabling the identification of subtle fault signatures. To effectively detect and classify various faults, different data mining techniques, such as support vector machines, decision trees, random forests, and k-nearest neighbours, are applied to the transformed data. Each technique's effectiveness in identifying faults is evaluated and compared, enabling the selection of the most suitable algorithm for the specific application. Experimental results demonstrate the effectiveness of the proposed fault detection approach, exhibiting high accuracy, sensitivity, and specificity in identifying various types of faults in PV arrays. Extensive simulations and experimental validations were conducted on various fault conditions, including partial shading, open-circuit faults, and degradation. The results demonstrate the proposed method's superior performance, achieving an accuracy of 99.61 %, precision of 99.75 % and F1 score of 98.73 %. These metrics significantly surpass traditional fault detection techniques, highlighting the method's potential for real-world deployment. The approach not only enhances the reliability of PV systems but also contributes to reducing maintenance costs and improving system efficiency. The combination of the Stockwell transforms with data mining techniques proposed here provides a robust and efficient framework for early detection of faults, enabling timely maintenance and minimizing energy losses.

Wave-Current Interaction Effects on the OC4 DeepCwind Semi-Submersible Floating Offshore Wind Turbine

In order to investigate the hydrodynamic performances of semi-submersible type floating offshore wind turbines (FOWTs), particularly the effect of body-wave-current interaction, the OC4 FOWT is considered in the presence of co-existing regular wave and uniform current fields. The wind loads are not considered at this stage. The problem is treated in the framework of potential-flow theory in the frequency domain, assuming waves of small steepness, and the solution is obtained by using a perturbation expansion method for the diffraction potential with respect to the normalized current speed. Analytical and numerical formulations have been used to treat the inhomogeneous free-surface boundary condition involved in the hydrodynamic problem formulation for the derivation of the associated perturbation potential. The hydrodynamic loads were obtained after evaluating the pressure field around the multi-body configuration using three different computer codes. The results from the three computer codes compare very well with each other and with the numerical predictions of other investigators. Finally, the mean second-order drift forces are calculated by superposing their zero-current values with the corresponding current-dependent first-order corrections, with the latter being evaluated using a ‘heuristic’ approach.

Review on Monoclonal Antibodies (mAbs) as a Therapeutic Approach for Type 1 Diabetes.

Monoclonal antibodies have been successfully utilized in a variety of animal models to treat auto-immune illnesses for a long time. Immune system responses will either be less active or more active depending on how the immune system is operating abnormally. Immune system hypoactivity reduces the body's capacity to fight off various invading pathogens, whereas immune system hyperactivity causes the body to attack and kill its own tissues and cells. For maximal patient compliance, we will concentrate on a variety of antibody therapies in this study to treat Type 1 diabetes (an autoimmune condition). T-cells are responsible for the auto-immune condition known as T1D, which causes irregularities in the function of β-cells in the pancreas. As a result, for the treatment and prevention of T1D, immunotherapies that selectively restore continuous beta cellspecific self-tolerance are needed. Utilizing monoclonal antibodies is one way to specifically target immune cell populations responsible for the auto-immune-driven disease (mAb). Numerous mAbs have demonstrated clinical safety and varied degrees of success in modulating autoimmunity, including T1D. A targeted cell population is exhausted by mAb treatments, regardless of antigenic specificity. One drawback of this treatment is the loss of obtained protective immunity. Immune effector cell function is regulated by nondepleting monoclonal antibodies (mAb). The antigenfocused new drug delivery system is made possible by the adaptability of mAbs. For the treatment of T1D and T-cell-mediated autoimmunity, different existing and potential mAb therapy methods are described in this article.

A Study on Fatigue among Drivers with Machine Learning by Using CNN and SVM

A major contributing factor to traffic accidents is driver fatigue brought on by long stretches of driving, lack of sleep, boredom, or any combination of these. Therefore, it has been suggested that fatigue detecting systems be used to warn or alert drivers. But how early driver fatigue is detected frequently affects how well the system works. Conventional techniques aim to identify driver fatigue in real time; however, in numerous crucial situations, like the takeover transition phase in fully automated driving, this detection may come too late. Therefore, the objective of this work is to predict the driver's transition from nonfatigue to fatigue during highly automated driving using physiological indications. First, we used the ground truth for driver fatigue, which is PERCLOS, or the percentage of time the eyelids are closed. Subsequently, we selected the physiological features that were most important for anticipating driver weariness in advance. Finally, we used these crucial physiological characteristics to create prediction models that, by applying a method called nonlinear autoregressive exogenous network, could forecast the exhaustion transition at least 13.8 seconds in advance. The recommended method's potential was demonstrated by the accuracy of tiredness transition prediction for highly automated driving (F1 measure = 97.3 percent and a score of 99.1 percent for two types of models, respectively).

Carbon emissions accounting and estimation of carbon reduction potential in the operation phase of residential areas based on digital twin

The carbon emissions of the construction industry have raised a lot of concern as it contributes about one third of global carbon emissions. Among them, the operation of residential areas is an important source of carbon emissions. However, due to the complexity of carbon emission sources, it is difficult to account carbon emissions in the operation phase of residential areas. The assessment of the carbon reduction potential for the transition to low carbon residential areas is also inadequate. In this study, carbon emission sources and carbon reduction methods in residential areas have been sorted out, while the carbon emission accounting methods and the estimation methods for carbon reduction potential have been proposed. Moreover, this article first proposes a theoretical framework based on digital twin (DT) for carbon emissions accounting and estimation of carbon reduction potential in the operation phase of residential areas. In this framework, building information modelling (BIM) and remote sensing (RS) image processing technology are integrated, and specific application processes are proposed to improve the intelligence level of carbon emission accounting and carbon reduction potential assessment. Additionally, the feasibility of this method is verified using a campus residential area as a case study. The case study indicates that the method proposed in this article has great potential for application. It also shows that the carbon sinks and new energy sources are the effective method to achieve low or even zero carbon residential areas. In this case, >60% of carbon emissions can be offset through the comprehensive use of various carbon reduction measures.

Ultrasonic powder consolidation of metallic glass/Al-6061 composites

Traditional powder consolidation methods for fabricating metallic matrix composites often require high temperatures, high pressures, and substantial energy consumption. Therefore, developing new processing technologies that can manufacture composites rapidly, efficiently, and economically is crucial. This study introduces ultrasonic powder consolidation process as a novel strategy for fabricating and tuning metallic glass (MG) and aluminum alloy composites. By optimizing the mass ratios of Zr55Cu30Ni5Al10 (at.%) MG to Al-6061 powders, a diverse range of composites with tailored compressive strength and plasticity was achieved. Mechanical testing showed that increasing the aluminum content improved plasticity while maintaining significant strength. Notably, the composite with a 5:5 mass ratio exhibited the best balance of mechanical properties. Morphological characterizations demonstrated excellent densification and uniformity in the composites, with no visible defects and relative densities ranging from approximately 92 %–99 %. Detailed microstructural analysis revealed the formation of a well-bonded interface with a diffusion layer, confirming the metallurgical bonding was facilitated by ultrasonic vibration. Furthermore, the ultrasonic consolidation process enabled the successful fabrication of complex shapes, such as star and gear components, demonstrating the method's potential for advanced manufacturing. These results show that the ultrasonic powder consolidation process is a viable and efficient approach for producing high-quality MG/Al-6061 composites with enhanced mechanical performance and application versatility.

Comprehensive measurement of three-dimensional thermal conductivity tensor using a beam-offset square-pulsed source (BO-SPS) approach

Accurately measuring the three-dimensional thermal conductivity tensor is essential for understanding and engineering the thermal behavior of anisotropic materials. Existing methods often struggle to isolate individual tensor elements, leading to large measurement uncertainties and time-consuming iterative fitting procedures. In this study, we introduce the Beam-Offset Square-Pulsed Source (BO-SPS) method for comprehensive measurements of three-dimensional anisotropic thermal conductivity tensors. This method uses square-pulsed heating and precise temperature rise measurements to achieve high signal-to-noise ratios, even with large beam offsets and low modulation frequencies, enabling the isolation of thermal conductivity tensor elements. We demonstrate and validate the BO-SPS method by measuring X-cut and AT-cut quartz samples. For X-cut quartz, with a known relationship between in-plane and cross-plane thermal conductivities, we can determine the full thermal conductivity tensor and heat capacity simultaneously. For AT-cut quartz, assuming a known heat capacity, we can determine the entire anisotropic thermal conductivity tensor, even with finite off-diagonal elements. Our results yield consistent principal thermal conductivity values for both quartz types, demonstrating the method's reliability and accuracy. This research highlights the BO-SPS method's potential to advance the understanding of thermal behavior in complex materials.

Untargeted and targeted metabolomics analysis of CO poisoning and mechanical asphyxia postmortem interval biomarkers in rat and human plasma by GC[sbnd]MS

Accurate and objective estimation of the postmortem interval (PMI) is crucial in forensic practice. This study aimed to infer PMI through equations based on the relationship between PMI and metabolomics biomarkers.Rats were subjected to models representing various temperatures and causes of death, with blood collected at different intervals. Untargeted gas chromatographymass spectrometry metabolomics detection methods were developed, and candidate biomarkers were chosen as co-differentially expressed metabolites in four models. A targeted method was then developed for quantitatively determining candidate biomarkers. Animal tests and human cadaver samples with clearly documented causes of death and time were used to verify the reliability of the regression equation.Results: Unique differential metabolites for CO poisoning deaths included 2,3-butanediol, hypoxanthine, and dehydrated hexanol, while those for mechanical asphyxia deaths comprised propylamine, 1,3-propylene glycol, phosphoric acid, and sorbitol. Pyruvate, glycerol and isoleucine were identified as candidate biomarkers. Human case results demonstrated the method's potential (error rate < 20 %). The findings of this study may offer reference points for estimating PMI and causes of death in forensic practice.

Confronting the Lippmann–Schwinger equation and the [formula omitted] method for coupled-wave separable potentials

We study a family of separable potentials with and without added contact interactions by solving the associated Lippmann–Schwinger equation with two coupled partial waves. The matching of the resulting amplitude matrix with the effective-range expansion is studied in detail. When a counterterm is included in the potential we also carefully discuss its renormalization. Next, we use the matrix N/D method and study whether the amplitude matrices from the potentials considered admit an N/D representation in matrix form. As a novel result we show that it is typically not possible to find such matrix representation for the coupled partial-wave case. However, a separate N/D representation for each coupled partial wave — a valid option known in the literature — is explicitly implemented and numerically solved in cases where the matrix N/D method is unavailable.

Synthesis of 3-Methyleneisoindolin-1-ones via Rhodium(III)-Catalyzed C-H/N-H Activation and Annulation of N-Methoxybenzamides with Potassium Vinyltrifluoroborate.

Isoindolinones are vital heterocyclic compounds in medicinal chemistry, notable for their diverse bioactivities. Significant attention has been devoted to their preparation; however, existing methods are unsuitable for constructing unsubstituted 3-methyleneisoindolin-1-ones. Herein, we present a rhodium(III)-catalyzed method for synthesizing unsubstituted 3-methyleneisoindolin-1-ones via C-H/N-H activation and annulation of N-methoxybenzamides with potassium (ethenyl)trifluoroborate. This approach offers mild reaction conditions, high regioselectivity, and efficient yields. Interestingly, sterically demanding or heterocyclic N-methoxyaromaticamides resulted in the formation of 2-vinyl(hetero)aromatic amides instead of 3-methyleneisoindolin-1-ones. Mechanistic insights suggest a rhodacycle intermediate pathway, highlighting the method's potential for developing new bioactive isoindolinone derivatives.

Regio- and Enantioselective N-Heterocyclic Carbene-Catalyzed Annulation of Aminoindoles Initiated by Friedel-Crafts Alkylation.

Chiral indoles annulated on the benzene ring are unique and significant in natural and medicinal compounds. However, accessing these enantioenriched molecules has often been overlooked. The present study introduces an organocatalytic protocol to access these compounds efficiently, demonstrated by substrate scope, functional group tolerance, and using only 1 mol % of a chiral conjugated acid catalyst. Additionally, the study explores regioselectivity, gram-scale reactions, and follow-up transformations, underscoring the method's potential.

A spectral approach for the dynamic Bradley–Terry model

SummaryThe dynamic ranking, due to its increasing importance in many applications, is becoming crucial, especially with the collection of voluminous time‐dependent data. One such application is sports statistics, where dynamic ranking aids in forecasting the performance of competitive teams, drawing on historical and current data. Despite its usefulness, predicting and inferring rankings pose challenges in environments necessitating time‐dependent modelling. This paper introduces a spectral ranker called Kernel Rank Centrality, designed to rank items based on pairwise comparisons over time. The ranker operates via kernel smoothing in the Bradley–Terry model, utilising a Markov chain model. Unlike the maximum likelihood approach, the spectral ranker is nonparametric, demands fewer model assumptions and computations and allows for real‐time ranking. We establish the asymptotic distribution of the ranker by applying an innovative group inverse technique, resulting in a uniform and precise entrywise expansion. This result allows us to devise a new inferential method for predictive inference, previously unavailable in existing approaches. Our numerical examples showcase the ranker's utility in predictive accuracy and constructing an uncertainty measure for prediction, leveraging data from the National Basketball Association (NBA). The results underscore our method's potential compared with the gold standard in sports, the Arpad Elo rating system.

Aggregated Pattern Classification Method for improving neural disorder stage detection.

Neurological disorders pose a significant health challenge, and their early detection is critical for effective treatment planning and prognosis. Traditional classification of neural disorders based on causes, symptoms, developmental stage, severity, and nervous system effects has limitations. Leveraging artificial intelligence (AI) and machine learning (ML) for pattern recognition provides a potent solution to address these challenges. Therefore, this study focuses on proposing an innovative approach-the Aggregated Pattern Classification Method (APCM)-for precise identification of neural disorder stages. The APCM was introduced to address prevalent issues in neural disorder detection, such as overfitting, robustness, and interoperability. This method utilizes aggregative patterns and classification learning functions to mitigate these challenges and enhance overall recognition accuracy, even in imbalanced data. The analysis involves neural images using observations from healthy individuals as a reference. Action response patterns from diverse inputs are mapped to identify similar features, establishing the disorder ratio. The stages are correlated based on available responses and associated neural data, with a preference for classification learning. This classification necessitates image and labeled data to prevent additional flaws in pattern recognition. Recognition and classification occur through multiple iterations, incorporating similar and diverse neural features. The learning process is finely tuned for minute classifications using labeled and unlabeled input data. The proposed APCM demonstrates notable achievements, with high pattern recognition (15.03%) and controlled classification errors (CEs) (10.61% less). The method effectively addresses overfitting, robustness, and interoperability issues, showcasing its potential as a powerful tool for detecting neural disorders at different stages. The ability to handle imbalanced data contributes to the overall success of the algorithm. The APCM emerges as a promising and effective approach for identifying precise neural disorder stages. By leveraging AI and ML, the method successfully resolves key challenges in pattern recognition. The high pattern recognition and reduced CEs underscore the method's potential for clinical applications. However, it is essential to acknowledge the reliance on high-quality neural image data, which may limit the generalizability of the approach. The proposed method allows future research to refine further and enhance its interpretability, providing valuable insights into neural disorder progression and underlying biological mechanisms.

Resourceful exploitation of sedimentary clay: Designing a dual-chambered borosilicate glass reactor system for the efficient treatment of malachite green and phenol through SCRT adsorption and 5%Fe@SRCT catalysis via CWPO, with evaluation of seed germination and fish survival.

This study presents an innovative double-walled borosilicate glass reactor system for the efficient treatment of liquid and gaseous wastewater. This reactor system allows precise temperature control, continuous pH monitoring, and controlled dosing of reagents to optimize reaction conditions. Detailed characterization was carried out by X-ray diffraction (XRD), Fourier transforms infrared spectroscopy (FTIR), BET (specific surface area) analysis, point of zero charge (PZC), and scanning electron microscopy (SEM) for the SCR, SCRT, and 5%Fe@SCRT materials. For Malachite Green adsorption, SRCT demonstrated a maximum adsorption capacity of 39.78 ±0.5mg/g using the Langmuir isotherm model and followed pseudo-second-order kinetics. Optimum conditions for adsorption were found to be: an initial concentration of 50 ppm, an adsorbent dosage of 1g/l, a pH of 8.5, and a temperature of 50°C. For the catalytic oxidation of phenol, 5%Fe@SRCT achieved a remarkable removal rate of 99.9±0.1% under optimum conditions (50 ppm phenol, 1g/l catalyst dosage, pH3.5, H2O2 concentration 8.7mM, and temperature 70°C). Intermediates identified during the reaction included hydroquinone, benzoquinone, catechol, and resorcinol, with degradation occurring over a 60-minute reaction period. The 5%Fe@SCRT material showed excellent reusability in the removal of phenol by catalytic oxidation, with no significant loss of efficiency over three cycles, while the SRCT underwent three cycles of regeneration for the adsorption of Malachite Green. Scavenger tests confirmed the involvement of hydroxyl radicals in the catalytic oxidation process. In addition, fish survival tests after catalytic oxidation of phenol by 5%Fe@SRCT showed no impact on fish, underlining the environmental safety of this process. In addition, germination tests after decolorization of MG by SRCT demonstrated a good effect with no negative impact, reinforcing the ecological value of this innovative technology. These results highlight the innovative use of SCRT and 5%Fe@SCRT as versatile materials for environmental remediation, exploiting their effective adsorption capacities and efficient catalytic oxidation performance within the proposed double-walled borosilicate glass reactor system. PRACTITIONER POINTS: The study demonstrates the effectiveness of an innovative reactor system employing SRCT adsorbent and Fe@SRCT catalyst for efficient removal of malachite green and phenol from wastewater. Environmental impact assessment, including seed germination and fish survival evaluation, validates the method's eco-friendly potential. Implementation of this approach could significantly contribute to sustainable water treatment practices.