Three-step Process Research Articles

Biosynthesis of green CuO@C nanocomposite using Combretum indicum flower extract for organic dye removal: adsorption performance, modeling, and recyclability studies.

Water contamination becomes one of the most high-priority environmental concerns, calling for the efficient treatment techniques. Bionanocomposites can be robust adsorbents, but the synthesis requires toxic chemicals or energy consuming and cause the secondary pollution. Green nanocomposites can be biogenically synthesized using the plant extract to end up with a critically safe strategy. Herein, we used the flower extract of Combretum indicum plant as a bio-based reductant and carbonaceous source for the green CuO@C nanocomposite. This green nanoadsorbent obtained a specific surface area of 17.33 m2/g, good crystallinity, and functional group-containing surface, i.e., -OH and -CONH-. We also conducted the optimization of parameters, i.e., concentration, CuO@C dose, pH, time, and temperature, and reached removal efficiencies towards malachite green (MG, 83.23%), Congo red (CR, 84.60%), brilliant blue (BB, 71.39%), and methylene blue (MB, 23.67%). The maximum adsorption capacities were found as ordered, MG (46.387 mg/g) > MB (23.154 mg/g) > BB (22.8 mg/g) > CR dye (11.063 mg/g). Through the intra-particle diffusion kinetic model, MG and BB adsorption endured a three-step process, while CR and MB adsorption was a two-step process. The recyclability of the green CuO@C nanocomposite was three cycles with 67.54% for the final cycle of BB removal. Moreover, the nanoadsorbent displayed a high stability, checked by X-ray diffraction, FT-IR analysis, EDX spectra, and SEM images. It is recommended that the green CuO@C nanocomposite biosynthesized using the Combretum indicum flower extract can be a good alternative for the dye treatment from wastewater.

Investigation of optimal operating conditions in dimethyl ether carbonylation to methyl acetate production process.

Ethanol production methods are expanding due to the importance of ethanol as a fuel or additive to fuels. One of these methods is converting methanol to ethanol in a three-step process. All of these steps need to deeply study and investigate to develop the process. In this research, the carbonylation of dimethyl ether to produce methyl acetate, which is the intermediate reaction of the three-step process of methanol to ethanol, has been simulated and optimized. The parameters of temperature, pressure, residence time, and feed ratio have been investigated as effective operational parameters of the process. It has been shown that the temperature and pressure of the process are more effective in the ranges of 220-280 °C and 30-50 bar, respectively. The simulation results showed a maximum point in dimethyl ether conversion in the feed ratio of 0.4-0.6, i.e., in temperature of 260 °C, residence time of 5 h, pressure of 45 bar, DME/CO/Ar = 30/67/3, and DME conversion about 22%. Also, it has been shown that increasing the residence time increases the effect of each of the above parameters. Optimization of the dimethyl ether carbonylation process has demonstrated that the combination of different ranges of the above parameters achieves the desired conversion, i.e., in pressure of 48.23 bar, temperature of 259.06 °C, residence time of 3.68 h, and dimethyl ether/feed of 0.461 vol%; conversion of dimethyl ether will be equal to 85.50%.

A novel three-step deep learning approach for the classification of breast cancer histopathological images

Breast cancer is a widespread and significant health concern among women globally. Accurately categorizing breast cancer is essential for effective treatment, ultimately improving survival rates. Moreover, deep learning (DL) has emerged as a widely adopted approach for precise medical image classification in recent years, showing promise in this domain. However, despite the availability of DL models proposed in the literature for automated classification of breast cancer histopathology images, achieving high accuracy remains challenging. A minor modification to pre-trained models and simple training strategies can further enhance model accuracy. Based on the approach, this paper proposed an anti-aliased filter in a pre-trained ResNet-34 and a novel three-step training process to improve BC histopathology image classification accuracy. The training involves systematically unfreezing layers and imposing additional constraints on the rate of change of learnable parameters. In addition, four-fold on-the-fly data augmentation enhances model generalization. The Ada-Hessian optimizer adjusts learning rates based on first and second-order gradients to improve convergence speed. The training process utilizes a large batch size to minimize the training loss associated with batch normalization layers. Even with the limited GPU size, the gradient accumulation technique achieves a large batch size. Collectively, these strategies minimize training time while maintaining or improving the accuracy of BC histopathology image classification models. In the experimental implementation, the proposed architecture achieves superior results compared to recent existing models, with an accuracy of 98.64%, recall (98.98%), precision (99.35%), F1-Score (99.17%), and MCC (97.36%) for binary classification. Similarly, the model achieves an accuracy of 95.01%, recall (95.01%), precision (94.95%), F1-Score (94.94%), and MCC (93.42%) for the eight-class category of BC images.

Locating tectonic tremors with uncertainty estimates: time- and amplitude-difference optimization, wave propagation-based quality control and Bayesian inversion

SUMMARY The accurate location of tectonic tremors helps improve understanding of their underlying physical processes. However, current location methods often do not statistically evaluate uncertainties to a satisfactory degree and do not account for potential biases due to subsurface structures not included in the model. To address these issues, we propose a novel three-step process for locating tectonic tremors. First, the measured time- and amplitude differences between station pairs are optimized to obtain station-specific relative time and amplitude measurements with uncertainty estimates. Secondly, the time– and amplitude–distance relationships in the optimized data are used to roughly estimate the propagation speed (i.e. shear wave velocity) and attenuation strength. Linear regression is applied to each event, and the resulting velocity and attenuation strength are used for quality control. Finally, the tremor location problem is formulated within a Bayesian framework where the model parameters include the source locations, local site delay/amplification factors, shear wave velocity and attenuation strength. The Markov chain Monte Carlo algorithm is used to sample the posterior probability and is augmented by a parallel tempering scheme for an efficient global search. We tested the proposed method on ocean-bottom data indicating an intense episode of tectonic tremors in Kumano-nada within the Nankai Trough subduction zone. The results show that the range of the 95 per cent confidence interval is typically <7 km horizontally and <10 km vertically. A series of experiments with different inversion settings reveals that adopting amplitude data and site correction factors help reduce random error and systematic bias, respectively. Probabilistic sampling allows us to spatially map the probability of a tremor occurring at a given location. The probability map is used to identify lineaments of tremor sources, which provides insights into structural factors that favour tremor activity.

Teaching Method In Writing Descriptive Text (A Descriptive Study at the Seventh Grade Students of Thammislam Foundation School Academic Year 2023)


 
 
 
 This study investigates the instructional approach English teachers employ in teaching descriptive writing to seventh-grade students at Thammislam Foundation School during the Academic Year 2023. What challenges does the English teacher encounter when instructing seventh-grade students at Thammislam Foundation School during the Academic Year 2023 in the area of writing descriptive texts? The research objectives are categorized into three distinct areas: writing, descriptive text, and the instructional methods employed by English teachers. The present study employed a descriptive qualitative research design. The researcher assumed the role of a non-participant observer. The researcher conducted three observations in a seventh-grade classroom and interviewed with an English teacher to gather information about the instructional methods employed in teaching descriptive writing. The observed population comprises 28 students enrolled in the seventh grade at Thammislam Foundation School during the academic year of 2023. The data collection instruments employed in this study encompassed observation, interviews, and the examination of relevant study documents, including modules, student worksheets, and curriculum materials. The researcher employed a three-step process to analyze the data. The three main components of the research process include data reduction, data display, and conclusion verification. Researchers employ triangulation as a means to enhance the trustworthiness of the data. The research findings indicate that the activities conducted by the teacher during the initial and subsequent meetings were consistent with the principles and framework of task-based language teaching. Implementing task-based language teaching in a classroom typically involves three distinct stages: the pre-task stage, the task cycle, and the language focus stage. The challenges encountered by the student included a limited vocabulary and a need for more proficiency in selecting appropriate verbs for constructing sentences. The instructor assigned exercises to enhance the students' vocabulary and proficiency in selecting the appropriate verb for each sentence. The models used for the tasks were evaluated based on their ability to match the given criteria accurately. In addition, the instructor employed a systematic approach to facilitate comprehension among the students, particularly in the context of composing descriptive texts.
 
 
 

Indirect method for preparing dual crosslinked eutectogels with high strength, stretchability, conductivity and rapid self-recovery capability as flexible and freeze-resistant strain sensors

The growing demand for stretchable conductor materials in flexible electronic technologies has led to increased focus on developing conductive gels. However, existing hydrogels have poor temperature resistance, and ionogels may have potential toxicity, limiting their use in bio-related applications. This work focuses on the development of the emerging eutectogels with both mechanical and electrical properties, which have traditionally been challenging to achieve. We present an indirect method to prepare dual crosslinked eutectogels that possess high strength, stretchability, conductivity and rapid self-recovery capability. The approach involves using water molecules as a co-solvent to form a deep eutectic solvent through a three-step process, followed by ionically cross-linking poly(acrylamide-co-acrylic acid) with Fe3+. The resulting eutectogels have a balanced mechanical strength with a tensile strength up to 6.17 MPa, large stretchability up to 1067%, a modulus of 1.25 MPa, and excellent toughness of 20.95 MJ/m3. In addition, the eutectogels exhibit high ionic conductivity of 10.29 mS/cm, rapid self-recovery and low temperature tolerance capability (up to −50 °C). To our knowledge, the eutectogels with balanced tensile strength, toughness, conductivity, and low-temperature tolerance can hardly be achieved. Moreover, the conductive eutectogel also exhibit high sensitivity with a gauge factor of 9.47 at strain of 700% with fast response time, making them promising candidates for soft human-motion sensors that can accurately detect human motions with high sensitivity and good durability over a wide temperature range. The findings provide new insights into developing eutectogels with both mechanical and electrical properties, opening opportunities in flexible electronics, soft robotics, and energy storage devices.

Limitation of the migration of plasticizers from medical devices through treatment with low-pressure cold plasma, polydopamine coating, and annealing

Poly(vinyl chloride) (PVC) is widely used in the manufacture of medical devices. The plasticizers added to PVC are potentially toxic for humans, likely to migrate, and thus unintentionally administered to patients. The objective of the present study was to reduce the migration of plasticizer (1,2-cyclohexanedicarboxylic acid, diisononylester (DINCH) or trioctyltrimellitate (TOTM)) from PVC by implementing a three-step surface treatment process: (i) pretreatment with low-pressure argon cold plasma, (ii) polydopamine coating, and (iii) post-treatment with cold plasma exposure or thermal treatment at 140 °C.Samples were then characterized in terms of the water contact angle (WCA) and the aspect in scanning electron microscopy. Plasticizer migration (n = 5) was measured using an HPLC technique with ultraviolet detection and found to depend on the treatment and the plasticizer.Plasticized PVC was hydrophobic, with a measured mean ± standard deviation WCA of 96.7 ± 3.6° for PVC-DINCH and 110.2 ± 5.8° for PVC-TOTM. Plasma post-treatment and thermal post-treatment were respectively associated with a mean decrease in migration of 38.3 ± 1.9% for DINCH and 61.5 ± 4.4% for TOTM.Our results are promising with regard to limiting the migration of plasticizers into the patient’s blood and thus enabling the development of safer medical devices.

Risk Analysis and Research for Construction Projects

As the population grows and the demand for public infrastructure increases, more and more construction projects are being carried out. In order to avoid the failure of construction projects due to their high level of complexity, risk anticipation and risk response can be made through the study of project risks, which can greatly improve the success of construction projects. The investment and time spent on a construction project are huge and if the project fails or is delayed, there will be serious consequences. This is why construction project risk management is so important for construction projects. Therefore, this paper analyses the risks of construction projects. It finds that construction project risks can be classified as internal and external risks and suggests improvements to the existing three-step project risk management process Identify-Assess-Respond, adding a project risk review. This paper enriches the study of risk classification and management processes for construction projects and proposes risk management process measures that have implications for future construction projects to deal with risks more efficiently.

Development and validation of the intellectual property services scale in China

Existing studies have emphasized the crucial role of intellectual property services (IPS) in the advancement of science and technology, but there was still a lack of a valid scale for assessing IPS. Accordingly, this study aimed to develop a reliable instrument to fill this gap. A three-step process of scale development was described: item generation through grounded theory research (n = 14), scale development through exploratory factor analysis (n = 219) and scale assessment through confirmatory factor analysis (n = 191). By analyzing and coding the interview data drawn from IPS agencies in China, the study verified that IPS could be represented as a three-dimensional construct consisting of vocational skills services (VSS), commercial operation services (COS), and value-added business services (VBS). The subsequent exploratory factor analysis and confirmatory factor analysis were conducted to test and modify the IPS scale with 11 items was shown to have acceptable internal consistency reliability, as well as convergent and discriminant validity. The IPS scale could serve as an operational tool for IPS practitioners to measure and identify the problems of service quality and service mode. Additionally, it held significant theoretical value and practical implications for the advancement of intellectual property services industry in China.

A Review of the Harvesting Techniques of Microalgae

Algae are an important group of photosynthetic autotrophs and are commonly found in different types of water bodies, including paddy fields. The algal group possesses distinctive characteristics and ranges from prokaryotic cyanobacteria to eukaryotic algae. Within these, microalgae are unicellular microorganisms widely distributed in saltwater as well as freshwater environments. Microalgae species have been utilized in different fields, especially animal and human nutrition, medicine, bioremediation, and bio-fertilizers. Recently, numerous studies have reported the importance of microalgae in the production of biofuel. Further, microalgae have great carbon dioxide fixation efficiency during growth, so farmable land is not required for cultivating microalgae. Microalgae biomass production is a three-step process: cultivation, harvesting, and processing. Of these, the harvesting process is considered challenging due to its high cost, and it directly affects the processing step. In addition, several factors influence the harvesting process, including the size of microalgae cells (<30 µm), cultural conditions of microalgae, electronegative property of cell membrane, growth rate, etc. The harvesting of microalgae is an elaborate process that involves different chemical or mechanical approaches. A number of harvesting techniques have been utilized to recover algal biomass, such as membrane filtration, chemical and bio-flocculation, flotation centrifugation, sedimentation, and coagulation. In this context, this review aims to discuss various types of techniques used for harvesting microalgae. This review could be useful for selecting appropriate harvesting technology for enhancing the yield of microalgae biomass.

Preparation of dehydroabietyl polyethylene glycol aldehyde modified hydroxyethyl chitosans and their physicochemical properties

A series of polymeric surfactants based on monodehydroabietyl polyoxyethylene(5) ether (DHA(EO)5H) and hydroxyethyl chitosan (HEC) were prepared through a three-step process. First, DHA(EO)5H was oxidized using activated MnO2 and transformed into dehydroabietyl polyethylene glycol(5) aldehyde (DHA(EO)4CH2CHO). Then, the DHA(EO)4CH2CHO was reacted with HEC and converted into Schiff-base. Finally, the polymeric surfactant, DHA(EO)4CH2CHO modified HEC (DHA(EO)4CH2CHO-m-HEC), was obtained by reducing the Schiff-base with sodium borohydride. The grafting degree (DG) of DHA(EO)4CH2CHO substitution onto HEC for DHA(EO)4CH2CHO-m-HECs was determined using elemental analysis (EA), and the surface activities and foam performance of DHA(EO)4CH2CHO-m-HECs in aqueous solution were investigated respectively. The emulsifying capacities of DHA(EO)4CH2CHO-m-HECs were evaluated according to the stability times of emulsion composed of water and liquid paraffin. The experimental results showed the DG could have significant influence on the critical micelle concentration (cmc) of DHA(EO)4CH2CHO-m-HECs and their surface tensions at cmc (γcmc), but nearly no effect on their minimum surface tensions (γmin) in aqueous solution. Among the synthetic polymeric surfactants in this investigation, DHA(EO)4CH2CHO-m-HEC with DG of 56.95% exhibited the best emulsification and foam properties.

Global Stillbirth Policy Review - Outcomes And Implications Ahead of the 2030 Sustainable Development Goal Agenda.

Globally, data on stillbirth is limited. A call to action has been issued to governments to address the data gap by strengthening national policies and strategies to drive urgent action on stillbirth reduction. This study aims to understand the policy environment for stillbirths to advance stillbirth recording and reporting in data systems. A systematic three-step process (survey tool examination, identifying relevant study questions, and reviewing country responses to the survey and national documents) was taken to review country responses to the global 2018-2019 World Health Organization (WHO) Reproductive, Maternal, Neonatal, Child and Adolescent Health (RMNCAH) Policy Survey. Policy Survey responses were reviewed to identify if and how stillbirths were included in national documents. This paper uses descriptive analyses to identify and describe the relationship between multiple variables. Responses from 155 countries to the survey were analysed, and over 800 national policy documents submitted by countries in English reviewed. Fewer than one-fifth of countries have an established stillbirth rate (SBR) target, with higher percentages reported for under-5 (71.0%) and neonatal mortality (68.5%). Two-thirds (65.8%) of countries reported a national maternal death review panel. Less than half (43.9%) of countries have a national policy that requires stillbirths to be reviewed. Two-thirds of countries have a national policy requiring review of neonatal deaths. WHO websites and national health statistics reports are the common data sources for stillbirth estimates. Countries that are signatories to global initiatives on stillbirth reduction have established national targets. Globally, nearly all countries (94.8%) have a national policy that requires every death to be registered. However, 45.5% of reviewed national policy documents made mention of registering stillbirths. Only 5 countries had national policy documents recommending training of health workers in filling out death certificates using the International Classification of Diseases (ICD)-10 for stillbirths. The current policy environment in countries is not supportive for identifying stillbirths and recording causes of death. This is likely to contribute to slow progress in stillbirth reduction. The paper proposes policy recommendations to make every baby count.

Breathable Fabrics with Robust Superhydrophobicity via In Situ Formation of Hierarchical Surface Morphologies.

Superhydrophobic fabrics have recently attracted extensive interest not only in the fields of water-repellent clothing but also for the emerging functional fabrics due to their intrinsic flexibility and excellent stability. In this work, we proposed a simple, cost-effective, and environmentally friendly method to fabricate superhydrophobic fabrics with a broad application scope for textiles of different apertures. The flexible, breathable, and superhydrophobic fabric was realized via a three-step process, including polydimethylsiloxane (PDMS) encapsulation, in situ microcilia array formation, and silica nanoparticle decoration. With an adhesive PDMS layer and additive NdFeB particles, the hierarchical structures can tightly attach to the fabric substrate to provide robustness and durability. Specifically, the optimization of microcilia architecture was achieved via tuning the composite mass ratios so that suitable morphologies can be produced for robust nonwetting behavior. The superhydrophobic fabrics possess a contact angle and sliding angle of ∼155 and ∼3°, respectively, with excellent durability against 650 cycles' periodic mechanical abrasion, 130 cycles' tape-peeling test, washing evaluation, and chemical corrosions. Furthermore, the superhydrophobic fabric shows outstanding breathability and flexibility to be adaptive to surfaces with curvature or irregular shapes. The presented superhydrophobic strategy was considered to be feasible for multiple fabric substrates, revealing the broad application potential for fields of healthcare production, outdoor goods, catering industry, etc.

Design and development of an eHealth intervention to support self-management in people with musculoskeletal disorders - 'eHealth: It's TIME': a study protocol.

Background:Musculoskeletal disorders (MSDs) are a leading cause of global morbidity, with the burden expected to increase in the near future. Self-management, with the support of healthcare professionals, is recommended for many MSDs. However, frequent clinical contact is not feasible. Previous research has highlighted the need for a co-designed eHealth-mediated self-management follow-up support intervention which integrates remote monitoring and behavioural change. Thus, the current study aims to develop and design a user-centred, eHealth-mediated self-management support prototype for people with MSDs. Methods:A three-step, iterative system development cycle will be utilised to develop and design the "eHealth: It's TIME prototype". The three-step process will include creating website features and content using two sequential focus groups with people with MSDs (n = 6 - 8); heuristic testing using the 10 heuristic principles of Nielsen (n = 5); and usability testing through in-person 60-minute interviews with people with MSDs (n = 3 - 5) and musculoskeletal physiotherapists (n = 3 - 5). Conclusion:The eHealth: It's TIME prototype will be a systematically developed, follow-up self-management support intervention guided by behavioural change theory and the preferences of end users.

The process of developing professional capacity for teachers

The primary aim of Vietnam's new educational program is to enhance students' knowledge and skills through practical learning experiences, enabling them to tackle academic and real-world challenges. However, teachers in Dong Thap province face limitations in their professional competencies. Thus, this study aims to address these issues by examining different models for developing teachers' professional competencies. 1,116 participants participated in this study (258 managers and 868 teachers from high schools and preschools) who completed two questionnaires assessing their professional competence and the perceived importance of professional development. The findings reveal that teachers lack subject knowledge, interdisciplinary understanding and skills in teaching design and assessment. Both school managers and teachers recognize the necessity of professional competence development. A three-step process for enhancing teachers' professional competence is proposed for implementing professional development activities based on these findings.

Unpacking Occupational Health Data in the Service Sector: From Bayesian Networking and Spatial Clustering to Policy-Making

The health status of the service sector workforce is a significant unknown in the field of medical geography. While spatial epidemiology has made progress in predicting the relationship between human health and the environment, there are still important challenges that remain unsolved. The main issue lies in the inability to statistically determine and visually represent all spatial concepts, as there is a need to cover a wide range of service activities while also considering the impact of numerous traditional medical variables and emerging risk factors, such as those related to socioeconomic and bioclimatic factors. This study aims to address the needs of health professionals by defining, prioritizing, and visualizing multiple occupational health risk factors that contribute to the well-being of workers. To achieve this, a methodological approach based on the synergy of Bayesian machine learning and geostatistics is proposed. Extensive data from occupational health surveillance tests were collected in Spain, along with socioeconomic and bioclimatic covariates, to assess potential social and climate impacts on health. This integrated approach enabled the identification of relevant patterns related to risk factors. A three-step geostatistical modeling process, including variography, ordinary kriging, and G clustering, was used to generate national distribution maps for various factors such as annual mean temperature, annual rainfall, spine health, limb health, cholesterol, age, and sleep quality. These maps considered four target activities—administration, finances, education, and hospitality. Remarkably, bioclimatic variables were found to contribute approximately 9% to the overall health status of workers.

Leveraging on few-shot learning for tire pattern classification in forensics

This paper presents a novel approach for tire-pattern classification, aimed at conducting forensic analysis on tire marks discovered at crime scenes. The classification model proposed in this study accounts for the intricate and dynamic nature of tire prints found in real-world scenarios, including accident sites. To address this complexity, the classifier model was developed to harness the meta-learning capabilities of few-shot learning algorithms (learning-to-learn). The model is meticulously designed and optimized to effectively classify both tire patterns exhibited on wheels and tire-indentation marks visible on surfaces due to friction. This is achieved by employing a semantic segmentation model to extract the tire pattern marks within the image. These marks are subsequently used as a mask channel, combined with the original image, and fed into the classifier to perform classification. Overall, The proposed model follows a three-step process: (i) the Bilateral Segmentation Network is employed to derive the semantic segmentation of the tire pattern within a given image. (ii) utilizing the semantic image in conjunction with the original image, the model learns and clusters groups to generate vectors that define the relative position of the image in the test set. (iii) the model performs predictions based on these learned features.Empirical verification demonstrates usage of semantic model to extract the tire patterns before performing classification increases the overall accuracy of classification by ∼4%.

Novel diamides inspired by protein kinase inhibitorsas anti-Trypanosoma cruzi agents: in vitro and in vivo evaluations.

Background: Chagas disease is a life-threatening illness caused by Trypanosoma cruzi. The involvement of serine-/arginine-rich protein kinase in the T. cruzi life cycle is significant. Aims: Tosynthesize, characterizeand evaluate the trypanocidal activity of diamides inspired by kinase inhibitor, SRPIN340. Material & Methods: Synthesis using a three-step process and characterization by infrared, nuclear magnetic resonanceand high-resolution mass spectrometry were conducted. Theselectivity index was obtained by the ratio ofCC50/IC50 in two in vitro models. The most active compound, 3j, was evaluated using in vitro cytokine assays and assessing in vivo trypanocidal activity. Results: 3j activity in themacrophage J774 lineage showed an anti-inflammatory profile, and mice showed significantly reduced parasitemia and morbidity at low compound dosages. Conclusion: Novel diamide is active against T. cruzi in vitro and in vivo.

Sulfonic acid-type hydrothermal carbon microspheres as stable, efficient catalysts for transesterification reactions

With the aim of preparing a sulfonic acid-type solid acid catalyst having a high density of acid sites and excellent stability, this work synthesized sulfonic acid bowl-shaped hydrothermal carbon microspheres (SA-OBHCs), using amine groups as the primary sulfonic acid bonding sites. This material was synthesized via a three-step functionalization process comprising the oxidation, amidation and sulfonation of BHCs. The extent to which chlorosulfonic acid reacted with amine groups in diethylenetriamine molecules on microsphere surfaces was also investigated using density functional theory calculations. These calculations showed that the substitution reaction of primary amines proceeded less readily than that of secondary amines but provided a more stable product. The surface acid density of the SA-OBHCs was as high as 11.2 mmol·g−1 and the –SO3H groups on the SA-OBHCs were found to be thermally stable up to 200 °C. These SA-OBHCs were subsequently used to catalyze the synthesis of fatty acid methyl esters (FAMEs) from a waste frying oil model compound and methanol. Reactions performed using a methanol/oil molar ratio of 20:1 at 140 °C for 3 h gave a FAMEs yield of 93.4 %. After four reuses, an SA-OBHCs specimen provided a FAMEs yield of 77.5 %, demonstrating the significant stability of this material.

Ultra-sensitive determination of Ochratoxin A in coffee and tea samples using a novel semi-automated in-syringe based coagulant-assisted fast mycotoxin extraction (FaMEx) technique coupled with UHPLC-MS/MS.

In this study, we demonstrated a novel semi-automated in-syringe-based coagulant-assisted liquid-liquid microextraction (IS-CGA-LLME) as fast mycotoxin extraction (FaMEx) technique coupled with ultra-high-performance liquid chromatography connected with a tandem-mass spectrometer (UHPLC-MS/MS) for the quantification of mycotoxin (Ochratoxin A, OT-A) in coffee and tea samples. IS-CGA-LLME is a three-step extraction process that includes extraction of OT-A from sample matrix using low-volume solvent extraction, then the extractant was cleaned-up using a coagulation process, and finally, the decolorized/matrix removed sample solution was processed for LLME for target analyte's pre-concentration. The final extractant was analyzed using UHPLC-MS/MS for OT-A quantification. Under the optimized experimental conditions, highly sensitive detection and quantification limits were obtained at 0.001 and 0.003ngg-1 for OT-A with excellent extraction recovery (93-111%) and precision <10%. These results proved that the developed method is a simple, highly sensitive, semi-automated, low-matrix effect and efficient procedure for the determination of mycotoxins in food samples.