Improved Extraction Efficiency Research Articles

Enhancing Indian Onion (Allium Cepa L. var. aggregatum) Extraction by Microwave Hydrodiffusion and Gravity (MHG) Method: Parametric Study and Optimization using FCCD Design

The aim of this study is to extract essential oil from Indian onion bulbs (Allium cepa L. var. aggregatum) using the microwave hydrodiffusion-gravity (MHG) method. The operating parameters considered include drying pre-treatment, material size (1-3 cm), microwave power (300-600 W), and extraction time (15-75 minutes). Optimal extraction conditions were identified through a two-stage process: initially screening data using a full factorial design, followed by process optimization using a face-centred central composite design (FCCD). The oil yield increased with extraction time up to a certain point, after which it plateaued, indicating no further yield improvements. The size of the material had an impact on how well microwave energy transferred; smaller particles provided larger surface areas, which improved extraction efficiency. Furthermore, the reduction in moisture content due to drying directly affected essential oil production, leading to higher yields. Sudden temperature increases could negatively impact oil quality, making temperature control during extraction critical. The microwave power level also played a significant role in the extraction process. The highest oil yield, 2.78%, was obtained with an extraction time of 75 minutes, a microwave power of 600 W, and a material size of 1 cm. Additionally, the extracted onion oil contained cyclohexanol, 5-methyl-2-(1-methylethenyl), and Z-citral.

Ultra-high pressure assisted extraction of water-soluble polysaccharides from Moringa oleifera seed husks and their antioxidant activities

ABSTRACT The process conditions and antioxidant activity of ultra-high pressure assisted extraction of polysaccharides from Moringa oleifera seed husks were studied. Taking the yield of polysaccharides as the index, the effects of degree of crushing, extraction pressure, holding pressure time and material-to-liquid ratio on the yield of polysaccharides from Moringa oleifera seed husks were investigated. The optimal extraction conditions were determined by single-factor test and orthogonal test: a holding time of 10 min, a material-to-liquid ratio of 1:25, an extraction pressure of 350 MPa, and a polysaccharide yield of 0.264%. Its antioxidant activity can be determined by measuring the total antioxidant capacity and their ability to scavenge DPPH and ·OH radicals. The results showed that the IC50 of Moringa oleifera seed husk polysaccharides on DPPH and hydroxyl radicals were 0.0662 g/L and 0.0844 g/L, respectively. The total antioxidant capacity was 0.1826 mm/g (expressed as the equivalent concentration of ferrous sulfate). Compared with hot water extraction methods, ultra-high pressure assisted extraction methods can shorten extraction time, improve extraction efficiency. The water-soluble polysaccharides extracted from Moringa oleifera seed husks by ultra-high pressure assisted extraction technology also have higher antioxidant activity than that by hot water extraction method at the same concentration.

Enhanced Extraction of Tanshinones from Salvia miltiorrhiza Using Natural-Surfactant-Based Cloud Point Extraction

Salvia miltiorrhiza (SM) contains the tanshinones, a compound with various pharmacological effects, and has been extensively studied as a pharmaceutical material. However, conventional methods for extracting tanshinones face challenges such as environmental hazards and high cost. In this study, we aimed to effectively extract tanshinones from SM using cloud point extraction (CPE) with lecithin, a natural surfactant. By optimizing various extraction conditions including the solid-to-liquid ratio, lecithin concentration, NaCl concentration, pH, and equilibrium temperature, the optimal extraction efficiency was achieved using 20 mL of solvent per 1 g of sample, 3% lecithin (w/v), 2% NaCl (w/v), pH 6, and room temperature (25 ± 2 °C). The CPE method, which minimizes the use of organic solvent and is eco-friendly, demonstrated improvements in extraction efficiency, with a 4.55% increase for dihydrotanshinone I, 8.32% for cryptotanshinone, 15.77% for tanshinone I, and 6.81% for tanshinone IIA compared to the conventional water extraction method. These results suggest that CPE is a promising, environmentally friendly, and efficient approach for extracting hydrophobic components from pharmacologically active materials such as SM, with potential applications across various fields of natural product extraction.

Supercritical carbon dioxide chelation extraction of heavy metal ions from drilling fluid waste: Experiment and simulation

This study evaluates the application of supercritical carbon dioxide chelation extraction technology for treating heavy metal ions (Zn2+ and Cr3+) in drilling fluid waste. Through a combination of experimental and molecular dynamics simulation methods, the influence of extraction parameters (temperature, pressure, duration, and chelating agents) on the extraction efficiency are investigated. Findings show that increased duration and pressure significantly improve extraction efficiency, while temperature has a complex effect, initially increasing efficiency but plateauing and slightly decreasing at higher temperatures. The optimum extraction condition with pressure of 220 bar, temperature 348.15 K and an extraction duration of 70 min using ethylene diamine tetraacetic acid (EDTA) as the chelating agent has been determined. Importantly, molecular simulation analysis revealed that citric acid outperforms EDTA in terms of Zn2+ and Cr3+ aggregation by forming larger aggregates with greater numbers of molecules while reducing overall aggregate count. Furthermore, optimization of extraction pressure in the EDTA system shows potential benefits. These results suggest that supercritical carbon dioxide chelation extraction technology has strong potential for environmentally friendly and reliable waste management in the drilling industry. This study represents a significant step forward in developing sustainable solutions for heavy metal removal from drilling fluid waste.

Developments and Applications of Molecularly Imprinted Polymer-Based In-Tube Solid Phase Microextraction Technique for Efficient Sample Preparation.

Despite advancements in the sensitivity and performance of analytical instruments, sample preparation remains a bottleneck in the analytical process. Currently, solid-phase extraction is more widely used than traditional organic solvent extraction due to its ease of use and lower solvent requirements. Moreover, various microextraction techniques such as micro solid-phase extraction, dispersive micro solid-phase extraction, solid-phase microextraction, stir bar sorptive extraction, liquid-phase microextraction, and magnetic bead extraction have been developed to minimize sample size, reduce solvent usage, and enable automation. Among these, in-tube solid-phase microextraction (IT-SPME) using capillaries as extraction devices has gained attention as an advanced "green extraction technique" that combines miniaturization, on-line automation, and reduced solvent consumption. Capillary tubes in IT-SPME are categorized into configurations: inner-wall-coated, particle-packed, fiber-packed, and rod monolith, operating either in a draw/eject system or a flow-through system. Additionally, the developments of novel adsorbents such as monoliths, ionic liquids, restricted-access materials, molecularly imprinted polymers (MIPs), graphene, carbon nanotubes, inorganic nanoparticles, and organometallic frameworks have improved extraction efficiency and selectivity. MIPs, in particular, are stable, custom-made polymers with molecular recognition capabilities formed during synthesis, making them exceptional "smart adsorbents" for selective sample preparation. The MIP fabrication process involves three main stages: pre-arrangement for recognition capability, polymerization, and template removal. After forming the template-monomer complex, polymerization creates a polymer network where the template molecules are anchored, and the final step involves removing the template to produce an MIP with cavities complementary to the template molecules. This review is the first paper to focus on advanced MIP-based IT-SPME, which integrates the selectivity of MIPs into efficient IT-SPME, and summarizes its recent developments and applications.

SPME arrow-based extraction for enhanced targeted and untargeted urinary volatilomics

BackgroundVolatile organic compounds (VOCs) present in human urine are promising biomarkers for various health conditions and environmental exposures. However, their reliable detection is challenging due to the complexity of urinary matrices and the low concentrations of VOCs. Moreover, untargeted approaches present considerable challenges in terms of data interpretation, increasing the complexity of method development. Here we address these challenges by developing a new method that combines solid-phase microextraction (SPME) Arrow with gas chromatography-high resolution mass spectrometry (GC-HRMS), using a design of experiments (DOE) approach for targeted and untargeted compounds. This methodology, specifically tailored for SPME Arrow, represents a significant advancement in untargeted urinary analysis. ResultsThe method was developed based on targeted and untargeted outcomes, were ranking results focus on the highest response area of 11 spiked target VOCs representative of urinary volatilomics, and on identifying the maximum untargeted number of VOCs. The method was developed focusing on the highest response area of 11 spiked target VOCs representative of urinary volatilomics and identifying the maximum number of VOCs. A univariate method determined the optimal coating type, urine volume, and salt addition. Subsequently, a central composite design (CCD) DOE was used to determine ideal temperature, extraction, and incubation times. The best method obtained has an extraction time of 60 min at a temperature of 53 °C, with an SPME Arrow CAR/PDMS using 2 mL of urine, with 0.25 % w/v of NaCl and a pH of 2. Compared to conventional SPME fibers, the SPME Arrow showed improved extraction efficiency, detecting more VOCs. Finally, the enhanced method was successfully applied to urine samples from children exposed and non-exposed to tobacco smoke, identifying specific VOCs, like p-cymene and p-isopropenyl toluene related to tobacco exposure. SignificanceBy integrating both targeted and untargeted approaches, the developed method comprehensively captures the complexity of urinary metabolomics. This dual strategy ensures the precise identification of known compounds and the discovery of novel biomarkers, thereby providing a more complete metabolic profile. Such an approach is crucial for advancing in non-invasive diagnostics and environmental health studies, as it offers deeper insights into the intricate relationships between metabolic processes and various health conditions.

Synergistic effects of Paenibacillus mucilaginosus and Penicillium pimiteouiense on the extraction of humic substances from lignite

Based on the common step of alkaline extraction-acid precipitation, lignite treatment techniques have been developed to improve extraction efficiency and yield of humic acid (HA). This study explored the combination of bacterium Paenibacillus mucilaginosus (P.m) LT1906 and fungus Penicillium pimiteouiense (P.p) LL2205 strains for pre- and post-treatment of lignite collected from eastern Inner Mongol to enhance HA and fulvic acid (FA) yield and plant growth-promoting activity. The lignite was subjected to P.m followed by HNO3 treatment, HNO3 followed by P.p treatment, and a combination of P.m, HNO3 and P.p treatments, then performed by alkaline extraction-acid precipitation technique. The results indicated that P.m pretreatment increased HA yield by 12.47 %, while P.p posttreatment significantly enhanced FA yield by 13.37 %. Combined bacterial and fungal treatments notably improved the total yields of HA and FA by 19.12 %. Structural analysis showed that P.m pretreatment caused the deformation on the lignite surface and the complete lignite oxidation and leaching reactions, thereby increasing the production of HA, yet had no impact on the elemental composition or chemical structure of HA. P.p posttreatment introduced oxygencontaining functional groups and reduced aromatic components through oxidative depolymerization of lignite, leading to increased FA production and HA seed germination-promoting activity.

Recovery of bioactive compounds from oil palm waste using green extraction techniques and its applications

SummaryOil palm (Elaeis guineensis) is a major oil crop that significantly contributes to the Indonesia's and Malaysia's economic growth. However, oil palm processing for oil extraction generates substantial waste, such as palm kernel cake, palm‐pressed fibre, oil palm empty fruit bunches and palm oil mill effluent, which pose noticeable environmental challenges. This review explores green extraction techniques for palm waste processing, which include microwave‐assisted extraction, deep eutectic solvents, ultrasound‐assisted extraction, supercritical fluid extraction, enzyme‐assisted extraction and subcritical water extraction. The potential for industry scaling, mechanisms, applications and challenges of each technique used for oil palm waste are discussed. Microwave‐assisted extraction, ultrasound‐assisted extraction and supercritical fluid extraction are particularly promising for large‐scale production, while deep eutectic solvents paired with ultrasound‐assisted extraction show the potential to improve extraction efficiency. This review also reveals that green extraction techniques are sustainable alternative to conventional techniques by effectively recovering valuable compounds from waste with reduced environmental impact. Additionally, the diverse applications of bioactive compounds (vitamin E, phytosterols, phenolics, polyphenols and flavonoids) extracted from oil palm waste in food, pharmaceutical, cosmetic and other industries are also presented. Valuable components such as cellulose, dietary fibres and bioactive peptides derived from the waste can be applied in food coatings, high‐protein products and drug delivery systems. Biosorbents, biofuels and biodegradable products derived from the waste would also provide environmental benefits. Future research should focus on optimising the green extraction techniques for industrial use to promote responsible production and circular waste management to foster health and environmental safety.

Optimization of Polyphenol Extraction Conditions from Rhizomes of Curcuma zedoaria with Antioxidant, Anti-Inflammatory and Anti-Diabetic Activities In Vitro

The recovery of polyphenol compounds is seen as an arduous task because polyphenol compounds are available as free aglycones, as sugar or ester conjugates, or as polymers with several monomeric components. The response surface method (RSM) as a tool to optimize the factors that affect extraction efficacy as well as to obtain maximum recovery of the compounds of interest. This study optimizes ultrasound-assisted polyphenol extraction from Curcuma zedoaria rhizomes using response surface methodology (RSM). Three variables were considered: Ethanol concentration (70 - 90 %, v/v), temperature (60 - 80 °C), and ultrasound time (15 - 25 min). Data were analyzed by ANOVA, yielding an R2 of 0.9993, a significant interaction effect (p < 0.0001), and an insignificant lack-of-fit test (p = 0.6684). Optimal conditions for maximum polyphenol content (TPC = 31.05 ± 0.53 mg GAE/g powder) were 80.02 % ethanol, 68 °C, 20.47 min ultrasound time, and 1/10 (w/v) raw material/solvent ratio. Experimental values matched RSM predictions, confirming successful extraction optimization from Curcuma zedoaria rhizomes. The polyphenol-rich optimum extract from Curcuma zedoaria rhizomes has been studied for its antioxidant, anti-inflammatory and anti-inflammatory properties in vitro. The results were found, the optimum extract of Curcuma zedoaria rhizomes could perform effective neutralization activities of free radicals performed in DPPH test (IC50 = 5.89 ± 0.23 µg/mL), NO· (IC50 = 5.89 ± 0.23 µg/mL) and ABTS·+ (IC50 = 8.28 ± 0.12 µg/mL). Besides, this optimum extract had the ability to protect red blood cell membranes and inhibit protein denaturation due to heat with IC50 times values ​​such as 29.06 ± 0.35 and 30.24 ± 0.32 µg/mL. In addition, it also significantly inhibited α-amylase, α-glucosidase enzyme activities with IC50 values ​​of 5.89 ± 0.23 and 9.62 ± 0.11 μg/mL, respectively. This investigation showed that the polyphenol-rich optimum extract from Curcuma zedoaria rhizomes was a promising antioxidant, anti-inflammatory and anti-diabetic agent. HIGHLIGHTS Using ultrasound and the Box-Behnken design to extract polyphenols from Curcuma zedoaria rhizomes can speed up and improve extraction efficiency. The goal of this study is to maximize the extraction parameters for polyphenols from the rhizomes of Curcuma zedoaria. There is strong agreement between the experimental data and the model that predicts the polyphenol content in the Box-Behnken design. In vitro biological activities such as anti-inflammatory, antidiabetic and antioxidant properties are significantly correlated with polyphenol concentration. The findings of our investigation show a noteworthy enhancement in the bioavailability of polyphenol compounds through increased extraction efficiency and bioactivity. GRAPHICAL ABSTRACT

A porous magnetic hydrogel-metal organic framework composite adsorbent for solvent-assisted magnetic solid-phase extraction and risk assessment of polycyclic aromatic hydrocarbons in food

A porous magnetic composite adsorbent was developed for the solvent-assisted magnetic solid-phase extraction of polycyclic aromatic hydrocarbons (PAHs). The porous magnetic composite adsorbent was created by incorporating a metal-organic framework (MOF), magnetite nanoparticles, and calcium carbonate into alginate beads. The extraction of the analytes was enabled through hydrophobic and π-π interactions between MOF-5, PAHs, and the organic solvent in the micropores. The fabricated adsorbent was characterized and optimized to improve extraction efficiency. Under the optimum conditions, good linearity was achieved with a detection limit in the range of 0.01–0.10 µg L−1. The developed method was successfully applied to extract PAHs from coffee, cocoa, and tea samples for analysis with high-performance liquid chromatography coupled with fluorescence detection (HPLC-FLD). Relative recoveries were obtained in the range of 70.3–96.1 %, with RSDs below 10 %. The developed adsorbent exhibited good stability and the extraction procedure was simple and rapid. The results obtained with the developed method were used to evaluate the incremental lifetime carcinogenic risk (ILCR) in adults from the intake of coffee, cocoa, and tea. Our calculations showed there was no cancer risk to consumers.

Pickering Emulsions in Catalytic Processes

AbstractPickering emulsions, which are emulsions stabilized by solid particles adsorbed at the interface between two immiscible liquids, provide a highly versatile platform for catalytic processes and offer distinct advantages over conventional systems. These emulsions combine the benefits of traditional biphasic catalysis with enhanced contact between reactive species due to their large interfacial area, which contribute to their high catalytic efficiency. Furthermore, Pickering emulsions offer significant advantages in catalytic processes, including improved extraction efficiency, a wider range of operational variables, the possibility of continuous operation, and the ease of recovery of the emulsifier and/or catalyst. Moreover, through strategic selection and design of solid particles, researchers can tailor interfacial properties to optimize catalytic performance, selectivity and stability. This comprehensive review discusses recent breakthroughs in Pickering emulsion research and their applications in catalysis, examining how Pickering emulsions have transformed catalytic methodologies. By discussing the latest developments, this review demonstrates the potential of Pickering emulsions as a catalyst platform and highlights their role in advancing sustainable and efficient catalytic processes.

Domain-Adaptive Framework for ACL Injury Diagnosis Utilizing Contrastive Learning Techniques

In sports medicine, anterior cruciate ligament (ACL) injuries are common and have a major effect on knee joint stability. For the sake of prognosis evaluation and treatment planning, an accurate clinical auxiliary diagnosis of ACL injuries is essential. Although existing deep learning techniques for ACL diagnosis work well on single datasets, research on cross-domain data transfer is still lacking. Building strong domain-adaptive diagnostic models requires addressing domain disparities in ACL magnetic resonance imaging (MRI) from different hospitals and making efficient use of multiple ACL datasets. This work uses the publicly available KneeMRI dataset from Croatian hospitals coupled with the publicly available MRnet dataset from Stanford University to investigate domain adaptation and transfer learning models. First, an optimized model efficiently screens training data in the source domain to find unusually misclassified occurrences. Subsequently, before being integrated into the contrastive learning module, a target domain feature extraction module processes features of target domain samples to improve extraction efficiency. By using contrastive learning between positive and negative sample pairs from source and target domains, this method makes domain adaptation easier and improves the efficacy of ACL auxiliary diagnostic models. Utilizing a spatially augmented ResNet-18 backbone network, the suggested approach produces notable enhancements in experimentation. To be more precise, the AUC for transfer learning improved by 3.5% from MRnet to KneeMRI and by 2.5% from KneeMRI to MRnet (from 0.845 to 0.870). This method shows how domain transfer can be used to improve diagnostic accuracy on a variety of datasets and effectively progresses the training of a strong ACL auxiliary diagnostic model.

Coal deformation characteristics during methane displacement by pulsed nitrogen injection: New method of using pulsed energy to improve coal permeability

The displacement of methane by injecting nitrogen is an effective method for improving extraction efficiency in low-permeability coal seams. By conducting the experiments on uniform/pulsed pressure nitrogen injection for methane displacement (UPNI-MD/PPNI-MD), the coal deformation characteristics are quantitatively characterized, and the coal deformation mechanism is discussed. The results show that the technology of methane displacement by PPNI-MD is more effective in enhancing the permeability of low-permeability coal seams. The pressure of nitrogen injection and the amount of methane/nitrogen adsorption are crucial factors affecting the coal deformation during the process of UPNI-MD/PPNI-MD. The erosive effect of methane adsorption on the mechanical properties of coal decreases, while the erosive effect of nitrogen adsorption on the mechanical properties of coal shows a “wave-like” variation in the case of PPNI-MD. The application of PPNI-MD technology can effectively decrease the coal expansion deformation while undergoing the methane displacement process. Nitrogen injection to displace methane breaks the system energy balance of coal, resulting in deformation. And the coal deformation during nitrogen injection for methane displacement depends on the energy carried by the injected nitrogen, the energy change during gas competitive adsorption and the heat exchange between nitrogen and coal. The effective stress of coal depends on the change in methane/nitrogen pressure. The mechanical properties of coal depend on the amount of methane/nitrogen adsorption and the pore pressure. High-energy nitrogen damage to coal during the process of PPNI-MD is manifested in coal deformation caused by methane/nitrogen pressure, coal deformation caused by methane/nitrogen adsorption, and damage to coal mechanical properties caused by methane/nitrogen adsorption.

Cross-domain fault diagnosis network based on attributes and features transfer with dual classifier under limited and unbalanced datasets

Abstract Deep learning (DL)-based methods have demonstrated significant success in fault diagnosis owing to their robust feature extraction and non-linear fitting capabilities. Meanwhile, their remarkable performance is accompanied by constant operating conditions and sufficient monitoring data. However, in real engineering environments, variable working conditions or limited and unbalanced data are common, which can widen the gap between fault diagnosis methods and real industrial applications. In this paper, we proposed a cross-domain fault diagnosis network based on a dual classifier (CFDNet) with input being limited and unbalanced data to learn attributes and features for unsupervised domain adaptation. We found that the diagnostic performance is commonly bounded by the underlying knowledge, especially feature extraction from original data. Therefore, we designed a new feature encoder with features and relationships, i.e. using a convolutional neural network and graph convolutional network, which improves extraction efficiency while retaining valuable information. Then, we discovered that enforced feature transfer can lead to negative transfer. To mitigate this, we present a feature and attribute transfer framework, which not only achieves features transfer but also enables attributes transfer. Furthermore, it was noted that limited and unbalanced datasets can introduce label bias and lead to biased model training. Hence, we designed dual classifiers to improve the probability of high-confidence final prediction by synthesizing diagnostic results. Comprehensive experiments conducted on three case studies demonstrate the effectiveness and superiority of our method for cross-domain fault diagnosis under limited and unbalanced datasets, which outperforms state-of-the-art methods in this study.

Comparative analysis of lead and cadmium extraction capacities of hydrophobic deep eutectic solvents

In this study, a total of eight hydrophobic deep eutectic solvents (HDESs) were prepared and evaluated for their efficacy in extracting lead and cadmium from aqueous solutions. The physical and thermal properties of these HDESs were characterized. Among the HDESs tested, the thymol:decanoic acid system, with a molar ratio of 1:1, exhibited the highest distribution ratios for lead and cadmium, with values of 0.79 and 0.55, respectively. The extraction performance of the thymol:decanoic acid system was further investigated by considering various factors such as contact time, pH, mass ratio of water to HDES, and HDES molar ratio. After optimization, the thymol:decanoic acid HDES demonstrated significantly improved extraction efficiency for lead (up to 93.49 %) and cadmium (up to 76.70 %) at initial concentrations of 1000 ppm and 100 ppm, respectively. The extraction mechanism was found to be primarily driven by the complexation and partitioning effects of thymol:decanoic acid with lead or cadmium, as confirmed by the noticeable changes in harmonic frequencies (730, 1337, and 1515 cm−1) observed in the IR spectra analysis before and after extraction. Additionally, the performance of the thymol:decanoic acid HDES was evaluated through solvent regeneration using a multi-stage extraction and reuse approach.

Composite flat-sheet membrane adsorbent of Li2TiO3-Ethylene-co-vinyl alcohol (LTO-EVAL) for lithium extraction

Composite bead adsorbents have been prevalent for the extraction of Li+ from brines. However, adsorbent beads generally demonstrate declining adsorption capacity and require extended equilibrium time when used repeatedly in absorption/desorption cycles. To overcome this challenge, a novel composite flat-sheet membrane adsorbent based on ethylene-co-vinyl alcohol (EVAL) and Li2TiO3 (LTO) is reported herein for highly efficient Li+ extraction from a geothermal brine. At an optimal LTO loading and membrane thickness, the resulting LTO-EVAL membrane was characterized by a large open macrovoid structure for fast ion transport, a fast adsorption rate that reached equilibrium in 0.5 h, and a high capacity of 96 % of that of the powder. The new membrane adsorbent showed adsorption behavior consistent with the Langmuir model and pseudo-second-order kinetic model. For a geothermal brine with a low lithium concentration, the LTO-EVAL membrane showed stable performance in 13 rapid adsorption/desorption cycles, with adsorption capacity at an average adsorption capacity of 7.3 mg/g and a 70 % adsorption efficiency. The average dissolution rate of Ti4+ was below 0.6 % in each cycle. The structure of the adsorbent material showed no discernable changes in chemical structure as confirmed by XRD and SEM results. With the improved extraction efficiency, the reusable membrane adsorbents would be economically viable in the long run and could potentially serve as the next generation of scalable Li+ adsorption material for brines. Further utilization of membrane adsorbent would lead to new design of adsorption modules and systems with reduced footprint. We need to further optimize the stability of the membrane adsorbents so that loss of Li2TiO3 remain at low level to reduce the lost for lithium extraction from the geothermal brine.

Limited Field Images Concrete Crack Identification Framework Using PCA and Optimized Deep Learning Model

Concrete crack identification methods based on machine learning can greatly improve extraction efficiency and precision. However, in many cases, model training requires a large amount of sample data, and insufficient data makes it difficult to effectively obtain model parameters. This study introduces a deep learning framework that integrates filters, principal component analysis, and attention mechanisms suitable for small sample sizes. Firstly, the histogram equalization method is used for the raw images, which can effectively enhance image contrast. Then, to acquire effective images of the crack, different methods are employed for crack detection, which are subsequently handled by principal component analysis (PCA) for optimal feature choice. Att-Unet and Att-Mask R-cnn segmentation models are used to design the detection for concrete cracks. To raise the learning ability of the segmentation models, an attention mechanism is applied to each feature layer of the decoder, and the loss function is evaluated using a combination of the Focal function and Cross Entropy. To verify the effectiveness of the proposed method, Deep Crack datasets and 76 sets of concrete crack data were collected for testing. Experimental results have shown that the method proposed can significantly reduce the model’s demand for data volume and improve training speed, which provides a new direction for small-sample crack extraction.

Isolation of stilbenoids from fresh knotwood of Scots pine using a high yield method

Abstract Scots pine knotwood contains valuable amount of stilbenoids such as pinosylvin (Ps) and pinosylvin monomethyl ether (PsMME). The stilbenoids have numerous biological activities. Traditional methods of extracting stilbenoids often yield low quantities and involve complex processes. In the present study, a simple and high-yield method was proposed for the extraction of stilbenoids from fresh Scots pine knotwood using ethanol taking a total of three days. In addition, the impact of drying process, extraction time, extraction solvents (acetone and ethanol) and storage time were evaluated on the yield of the stilbenoids. The increased yield and improved extraction efficiency make this method a promising advancement for promoting the utilization of stilbenoids for their valuable bioactive properties.

52‐4: Micro LED Display Light Extraction Efficiency Improvement by Secondary Optics on Substrate

Micro LED displays are widely acknowledged as forthcoming display innovations. However, due to the micro LED device size reductions, the light extraction efficiency of micro LED devices will be compromised. This paper extends the EQE equation and implement a LEE secondary optics (the secondary optics of light extraction efficiency) factor. The paper also presents few directions to enhance the normal angle light intensity for micro LED displays. Our sample preparations have confirmed that a white glue layer can bring about an improvement of over 100%, and a lens with a protection layer can also boost the normal angle light intensity by 45%. Considering the overall display performance including surface reflection, we simulated a condition that can improve the normal angle light intensity by over 70% in micro LED displays.

Advanced NADES-based extraction processes for the recovery of phenolic compounds from Hass avocado residues: A sustainable valorization strategy

Natural Deep Eutectic Solvents (NADES) are eco-friendly solvents with considerable potential for extracting bioactive compounds. In this study, we aimed to extract phenolic compounds from Hass avocado residues (epicarp and seed) using NADES in combination with ultrasound-assisted extraction (UAE) and pressurized liquid extraction (PLE) as advanced processes. Ten eutectic mixtures, consisting of choline chloride (ChCl) or betaine as hydrogen bond acceptors (HBA), as well as fructose (Fru), glucose, lactic acid (Lac), citric acid, or glycerol as hydrogen bond donors (HBD), were prepared through stirring and heating. The estimated values for total phenol content (TPC), proanthocyanidin content (PAC), monomeric anthocyanin content (MAC), and antioxidant capacity of the extracts demonstrated a noticeable influence of these solvents. The PLE process significantly enhanced the extraction of phenolic compounds from the epicarp, as evidenced by TPC (185 ± 9 mg gallic acid equivalent/g dry sample) and PAC (77.8 ± 6 mg proanthocyanidin B2/g dry sample) values. Phytochemical characterization conducted via HPLC-QTOF-MS/MS revealed the great extraction capacity of ChCl:Fru and ChCl:Lac, and the improvement of extraction efficiency through PLE. The results suggest that employing these innovative solvents along with advanced extraction processes, especially under high pressures, provides a sustainable approach to recover extracts with high content of phenolic compounds from Hass avocado agro-industrial waste, thus enabling their valorization.