Extraction Mechanism Research Articles

Investigation into the chemical mechanism of praseodymium extraction for rare earth elements through high-temperature electrochemical extraction

Investigation into the chemical mechanism of praseodymium extraction for rare earth elements through high-temperature electrochemical extraction

Reactive extraction of methanesulfonic acid from wastewater using trioctylamine

Wastewater containing methanesulfonic acid (MSA) mainly originates from the production process of metal detergent, which also contributes significantly to environmental pollution. This study investigates the extraction of MSA from wastewater using trioctylamine (TOA). A thorough investigation was carried out into the factors that affect extraction efficiency, such as the type of extractant, phase ratio (O/A), temperature, stirring speed, and extraction time. An extraction efficiency of 96.1% was achieved using TOA at 25 °C, 1400 r·min− 1, and an extraction time of 30 min. Various techniques including FT-IR, XPS, and high-resolution ESI-MS were employed to investigate the extraction mechanism. The results of different techniques revealed that the complexation between TOA and MSA occurred through ionic and hydrogen bonding interactions. Moreover, TOA was successfully regenerated through back-extraction with sodium hydroxide. The proposed extraction system is advantageous for eco-friendly engineering applications.

Combined ANFIS and numerical methods to reveal the mass transfer mechanism of ultrasound-enhanced extraction of proteins from millet

Millet protein, as a promising plant-based protein substitute source, is an excellent basis for essential amino acids compared to commonly consumed staple grains. Compared with the traditional extraction process, ultrasound has been used to enhance the extraction efficiency of various plant-based proteins. To reveal the mechanism of ultrasound-enhanced extraction of proteins, adaptive neuro-fuzzy inference system (ANFIS) algorithm and numerical simulation based on Fick’s law were applied to illustrate the mass transfer behavior of millet proteins under different ultrasonic conditions including solid–liquid ratios (S/L ratios), pH and acoustic energy density levels (AED). The results showed that AED dominated the changes in effective diffusion coefficient (De), showing a positive correlation relationship (p < 0.05). Specifically, when the AED was 47.07 W/cm2, the De value increased by 95 % compared to that of 23.47 W/cm2. Meanwhile, the ANFIS model successfully predicted protein yields across all investigated parameters, achieving a coefficient of determination (R2) greater than 0.97. This model also elucidated the interactions among four critical factors, among which pH and S/L ratios were the main factors affecting protein yield. Concerning the ultrasonic cavitation bubble dynamics, the bubble collapse efficiency enhanced with an increase in AED, and therefore high AED ultrasound can significantly enhance the cavitation effect. Additionally, the results of the yields and physical properties of millet protein also indicated that in contrast with the traditional extraction methods, the ultrasound impactfully improved extraction yield (by 165 %), and combined with pH condition, it decreased the protein particle size (from 813.55 nm to 299.30 nm) without altering the molecular weight distribution. This study offers a novel perspective on the mechanism underlying ultrasound-enhanced protein extraction, drawing upon principles of ultrasonics and extraction processes. The insights gained can serve as a foundation for the food industry to upscale the extraction process, potentially enhancing efficiency and yield.

Synergistic extraction of zinc from spent acid using P507-P204: A novel approach for efficient separation

A novel system for the synergistic extraction of zinc from spent acid using a mixture of 2-ethylhexyl phosphonic acid mono-2-ethylhexyl ester (P507) and Di-2-ethylhexyl phosphoric acid (P204) as extractant is proposed. The optimum extraction parameters of P507-P204 are investigated, and the results exhibit that 84.80% of zinc is extracted at a solution pH control of 1, the V(P507):V(P204) of 4:1, the P507-P204 concentration of 30% (v/v), and the phase ratio (O/A) of 2:1. Furthermore, the zinc can be effectively stripped from its extracted complex by sulfuric acid. The synergistic extraction mechanism of P507-P204 is identified by slope analysis, FT-IR, mass spectrum, and 31P-NMR. In addition, the mechanism of synergistic extraction of zinc by P507-P204 is cation exchange and releases two hydrogen ions during zinc extraction. In a word, the establishment of this synergistic system realizes the effective separation of zinc from spent acid and has potential industrial application value.

Deep feature synthesis approach using selective graph attention for replay attack voice spoofing detection

&lt;span lang="EN-US"&gt;As voice-based authentication becomes increasingly integrated into security frameworks, establishing effective defenses against voice spoofing, particularly replay attacks, is more crucial than ever. This paper presents a novel comprehensive framework for replay attack detection that leverages the integration of advanced spectral-temporal feature extraction and graph-based feature processing mechanisms. The proposed system presents the design of a waveform encoder and a novel temporal residual unit for spectral and temporal feature extraction in synchronous. Further, an approach of selective attention graph followed by multi-scale feature synthesis is employed to retain precise and spoof indicative feature vectors at the classification layer. The proposed method addresses the significant challenge of distinguishing genuine speech from replayed recordings. The validation of the proposed model is done on the ASVSpoof2019 dataset to demonstrate the efficacy of the proposed approach. The proposed system outperforms existing methods, achieving a lower equal error rate (EER) of 0.015 and a reduced tandem detection cost function (t-DCF) of 0.503. The comparative outcome exhibits the robustness of the method in identifying replay attacks.&lt;/span&gt;

Selective recovery of lithium from aqueous solution with hydrophobic deep eutectic solvent based on quantum chemical calculations and experimental investigation

Developing green and efficient extractants for selectively recovering lithium from salt lake brine is beneficial for alleviating the increasingly growing demand for lithium resources. This study developed a novel hydrophobic deep eutectic solvent (HDES) for selective recovery of Li+ from simulated salt lake brine solutions. Under the optimal experimental parameters, the single extraction efficiency of Li+ was 70.18%, β (Li+/ Na+) and β (Li+/Mg2+) were 22.32 and 947.58, respectively. Cycle experiments verified the high stability and good cycle performance of HDES. The conditions affecting the extraction efficiency of Li⁺ were optimized through experiments, and the high stability and good cyclic performance of HDES were verified. The extraction ability and mechanism of HDES for different metal ions were studied based on FTIR spectra combined with density functional theory. The calculation results showed that the order of extraction capacity of HDES for metal ions was: Li+>Na+>Mg2+. This calculation results were identical to the experimental results. This research will help develop HDES for the selective recovery of lithium from aqueous solutions containing high concentrations of Mg2+, helping to address the urgent global demand for lithium resources.

Advanced drone-based weed detection using feature-enriched deep learning approach

This research addresses the pressing challenge of weed identification in agriculture, crucial for ensuring food security in anticipation of a global population exceeding 9.7 billion by 2050. Utilizing drone imagery, we collected a dataset and proposed a customized model to achieve optimal performance. Our proposed model uses strategically modified backbone, neck, and head components, leveraging elements such as Ghost Convolution, BottleNeckCSP, and ECA (Efficient Channel Attention) layers. These modifications enhance the model’s capability to discern intricate patterns in drone imagery, ultimately leading to improved precision in weed detection. We introduce a purposefully crafted dataset to complement the model’s training, and our experiments demonstrate superior performance compared to the baseline models. Our model achieves a precision of 72.5%, recall of 68.0%, and mAP@0.5 of 73.9, showcasing the effectiveness of our approach in enhancing detection accuracy. Leveraging a unique blend of feature extraction mechanisms, our model achieves remarkable accuracy in real-time soybean detection, outperforming established models like RT-DETR (Real-Time DEtection TransfoRmer) and YOLOv10. A detailed ablation study and comparative analysis with different YOLO versions and the transformer-based RT-DETR showcase the effectiveness of the proposed enhancements. Our work signifies a significant step toward advancing the field of precision agriculture, offering a model that is not only adaptive but also robust in identifying and localizing weeds in soybean fields.

Mechanism of deep eutectic solvents based extraction of phenolic and flavonoid compounds from peony petals

Deep eutectic solvents (DES) have exhibited great potential in the extraction of active ingredients from flowers. In this study, ternary DESs were designed for the extraction of phenolic and flavonoid compounds from peony petals. When the molar ratio of ChCl:EG:U was 1:2:2, and the water content was 30 %, the maximum extraction yields of total phenolic content (TPC) and total flavonoid content (TFC) were achieved, reaching 109.30 mg GAE/g and 56.93 mg RE/g, respectively. Meanwhile, cellulose nanofibers were prepared. The relationship model with extraction yield as the target was established by multivariate analysis. The model indicated that the property parameters of the HBD had a significant impact on the extraction yields of TPC and TFC. At the molecular level, Density functional theory (DFT) was employed to explore the extraction mechanism. The analysis revealed that hydrogen bonding was the main interaction force. The maximum binding energy between DES and rutin was −89.03 kcal/mol. This study provides an efficient extraction solvent and insights into the mechanism of extracting the phenolic and flavonoid compounds from peony petals.

EA-CNN: Enhanced Attention-CNN with Explainable AI for Fruit and Vegetable Classification

The quality of vegetables and fruits are judged by their visual features. Misclassification of fruits and vegetables lead to a financial loss. To prevent the loss, superstores need to classify fruits and vegetables in terms of size, color and shape. To improve the accuracy of models for classifying fruits and vegetables, researchers have introduced various CNN architectures like VGG16, AlexNet, DenseNet-121 and different attention-based variants by compromising models’ complexity leading to high computational cost and overlooking feature’s contribution for interpretable predictions. Additionally, the major drawback in most of the existing works is the utilization of the limited dataset and number of classes for fruit and vegetable classification task instead of the benchmark dataset like Fruit-360 which comprises of 141 distinct fruit and vegetable classes. In this study, an explainable artificial intelligence (XAI) driven enhanced attention-CNN (EA-CNN) is proposed for accomplishing the fruit and vegetable classification task accurately and efficiently. The proposed EA-CNN model (a) detects the class of fruits and vegetable accurately through visual features by manipulating undiscover customized pooling technique and enhanced attention feature extraction mechanism, (b) classifies the fruits and vegetables efficiently through a simplified architecture, (c) provides interpretable predictions through XAI approach. By utilizing entire fruit-360 database, this research noteworthily enhances the model’s capability to classify wide range of fruits and vegetables, thus providing an effective and reliable solution for practical applications. The proposed study outclasses baseline models with regard to accuracy and computational cost on fruit-360 benchmark dataset. EA-CNN achieves better accuracy of 98.1% in smaller number of iterations as compared to benchmark models. Moreover, to further prove the efficiency, generalization ability, robustness, scalability and adaptability of this research, the architecture of proposed EA-CNN model is validated on another real-world dataset named as ‘Fruit Recognition’. The experimental outcomes display that the proposed EA-CNN model produces substantial performance on Fruit-Recognition dataset as well by attaining a generalized accuracy of 96%.

Synergistic Behavior and Kinetics of Yttrium Extraction from Nitric Acid Using a Binary System of Cyanex 572 and Cyanex 302

This study explores the extraction of Y(III) from nitric acid solutions using a kerosene-based blend of Cyanex 572 (H₂B₂) and Cyanex 302 (H₂A₂). Slope analysis suggests that the extracted species formed in the presence of this mixture is likely YH₄A₃B₄. Separation factors for Y(III) relative to other rare earth elements, specifically Nd(III) and La(III), demonstrate the effectiveness of the Cyanex 572 and Cyanex 302 mixture for selective separation. To gain deeper insights into the extraction mechanism, a kinetic study was conducted using a Lewis cell. The extraction kinetics of Y(III) from the nitric acid medium were analyzed by applying pseudo-first-order rate constants. Results indicate that the process is diffusion-controlled and involves an interfacial reaction. This conclusion is supported by investigating the effects of stirring speed, temperature, extractant concentration, and nitrate and hydrogen ion concentrations on the extraction rate. From the experimental data, a rate equation was derived, and the rate-determining step was identified using interfacial reaction model predictions. Additionally, an extraction model is proposed to elucidate the underlying mechanisms.

Lily extract ameliorate mouse pneumonia by modulating ciliary function

This study explores the biological effects and mechanisms of Lily extract on LPS-induced pneumonia mice. Research has found that after 48 h of treatment with low-dose and high-dose lily extract (100 mg/kg, 200 mg/kg), there was a significant reduction in inflammatory cell infiltration, mRNA expression levels of pro-inflammatory factors (IL-6, IL-1β, TNF-α), protein levels of inflammation-related proteins (IL-6, NF-κB P65) in the lung tissue. In addition, lily extract significantly improved the function of cilia assembly and movement. The expression levels of cilia-related genes (Ttc21a, Cfap45, etc.) and proteins were significantly upregulated. Among them, the high-dose treatment group had tightly arranged cilia and good growth conditions, which was significantly better than the LPS pneumonia model and low-dose treatment group. This found providing novel insights into the pathogenesis and progression of pneumonia. This has important implications for the prevention and treatment of pneumonia, as well as the development of natural agents.

A semi-automatic cardiovascular annotation and quantification toolbox utilizing prior knowledge-guided feature learning

Annotation and quantification of cardiovascular in intravascular ultrasound (IVUS) images are of great significance in assisting the clinical diagnosis and treatment of coronary heart disease. However, it is time-consuming and expensive for cardiologists to manually annotate cardiovascular and calculate parameters in IVUS images using different software during the diagnosis. In this paper, we propose an IVUS analysis toolbox (SaCAQ) for semi-automatic cardiovascular annotation and quantification via prior knowledge-guided feature learning. The SaCAQ consists of two main modules: (1) a semi-automatic cardiovascular annotation module and (2) a cardiovascular quantification module. The semi-automatic cardiovascular annotation module can accurately segment the vascular wall by a IVUS images segmentation model (ISM-IVUS). The ISM-IVUS first extracts vascular morphological features and pixel intensity change features for the complex image segmentation by the expert prior knowledge feature extraction mechanism. Secondly, vascular wall is accurately segmented by the multi-scale feature extraction of attention U-Net. Moreover, the segmentation result checking mechanism is applied to verify the automatic segmentation result, which enhances the reliability of SaCAQ. Plaque regions of interest to doctors are detected by the rule-based plaque detection method, which can assist in the quantification task. Finally, image measurement methods and medically recognized calculation formulas are used in the cardiovascular quantification module to obtain clinical parameters, which significantly improves the confidence of the results. The SaCAQ is verified on our own dataset containing 5242 IVUS slice images and 383 sets of parameters. The experiments demonstrate that the SaCAQ achieves high accuracy of vascular wall segmentation: mIoU of 86.16%, Dice coefficient of 93.43%, and Hausdorff distance of 0.2171 mm. Moreover, ISM-IVUS has good segmentation speed and robustness. The calculated parameters show good correlation and p-values. All of these results indicate that the proposed SaCAQ provides an efficient, accurate, and reliable toolbox for clinical diagnosis of cardiovascular disease. The SaCAQ and user manual can be publicly available from https://github.com/zHwiz/SaCAQ.

Correct and Complete Type Checking and Certified Erasure for Coq, in Coq

Coq is built around a well-delimited kernel that performs type checking for definitions in a variant of the Calculus of Inductive Constructions ( CIC ). Although the metatheory of CIC is very stable and reliable, the correctness of its implementation in Coq is less clear. Indeed, implementing an efficient type checker for CIC is a rather complex task, and many parts of the code rely on implicit invariants which can easily be broken by further evolution of the code. Therefore, on average, one critical bug has been found every year in Coq . This paper presents the first implementation of a type checker for the kernel of Coq (without the module system, template polymorphism and η -conversion), which is proven sound and complete in Coq with respect to its formal specification. Note that because of Gödel’s second incompleteness theorem, there is no hope to prove completely the soundness of the specification of Coq inside Coq (in particular strong normalization), but it is possible to prove the correctness and completeness of the implementation assuming soundness of the specification, thus moving from a trusted code base (TCB) to a trusted theory base (TTB) paradigm. Our work is based on the MetaCoq project which provides meta-programming facilities to work with terms and declarations at the level of the kernel. We verify a relatively efficient type checker based on the specification of the typing relation of the Polymorphic, Cumulative Calculus of Inductive Constructions ( PCUIC ) at the basis of Coq . It is worth mentioning that during the verification process, we have found a source of incompleteness in Coq ’s official type checker, which has then been fixed in Coq 8.14 thanks to our work. In addition to the kernel implementation, another essential feature of Coq is the so-called extraction mechanism: the production of executable code in functional languages from Coq definitions. We present a verified version of this subtle type and proof erasure step, therefore enabling the verified extraction of a safe type checker for Coq in the future.

Enhanced fluoride extraction from contaminated soil combining chelator and surfactant: Insights into adsorptive controlment of soil surface charge

Biodegradable chelators and surfactants are promising alternatives to conventional washing agents for remediating soil contaminated with toxic elements, owing to their excellent extractability and environmental compatibility. Most previous studies have primarily aimed at maximizing removal efficiency. However, understanding their underlying extraction mechanism is essential to expand the application potential of chelator- or surfactant-assisted washing systems. This study evaluated the effectiveness of chelators and surfactants in remediating fluoride (F)-contaminated soil and explored their associated extraction mechanisms. Our findings highlight a biodegradable chelator, HIDS (3-hydroxy-2,2′-imino disuccinic acid) as uniquely effective in F extraction with minimal F-bearing minerals dissolution (Ca, Fe, and Al). Chelator recovery rates and zeta potential measurements in post-washed solutions suggests that HIDS adsorbs onto soil surfaces, displacing the originally adsorbed F and enhancing the negative surface charge to inhibit F re-adsorption. Additionally, applying an anionic surfactant to enhance F extraction from soil showed promising results. Notably, a binary blend of HIDS and in-lab designed anionic surfactant, SDT (sodium N-dodecanoyl-taurinate), achieved the highest F removal rate (132 mg kg−1) under optimized washing conditions (HIDS: 10 mmol L−1, SDT: 10 mmol L−1, solution pH: 3, and washing time: 1 h), enhancing F extraction by 22% compared to HIDS-only washing (108 mg kg−1; washing time: 3 h). The FT–IR and zeta potential measurements suggested that SDT adsorbed onto the soil surface. The action of the HIDS–SDT blend towards F extraction involves the complexation and acid dissolution of F-bearing soil minerals, followed by F replacement through chelator and surfactant adsorption. This process mitigated F back-adsorption and enhanced F extraction by generating a negatively charged soil surface.

Dual-task vision transformer for rapid and accurate intracerebral hemorrhage CT image classification

Intracerebral hemorrhage (ICH) is a severe and sudden medical condition caused by the rupture of blood vessels in the brain, leading to permanent damage to brain tissue and often resulting in functional disabilities or death in patients. Diagnosis and analysis of ICH typically rely on brain CT imaging. Given the urgency of ICH conditions, early treatment is crucial, necessitating rapid analysis of CT images to formulate tailored treatment plans. However, the complexity of ICH CT images and the frequent scarcity of specialist radiologists pose significant challenges. Therefore, we collect a dataset from the real world for ICH and normal classification and three types of ICH image classification based on the hemorrhage location, i.e., Deep, Subcortical, and Lobar. In addition, we propose a neural network structure, dual-task vision transformer (DTViT), for the automated classification and diagnosis of ICH images. The DTViT deploys the encoder from the Vision Transformer (ViT), employing attention mechanisms for feature extraction from CT images. The proposed DTViT framework also incorporates two multilayer perception (MLP)-based decoders to simultaneously identify the presence of ICH and classify the three types of hemorrhage locations. Experimental results demonstrate that DTViT performs well on the real-world test dataset.

Curcuma longa rhizome extract: a potential antibiofilm agent against antibiotic-resistant foodborne pathogens

The traditional medicinal value of Curcuma longa (turmeric) and its potential relevance in modern healthcare suggests that traditional remedies and natural products can provide valuable solutions to contemporary challenges, such as combating biofilms and antibiotic-resistant pathogens, potentially offering new strategies for addressing health and safety issues in the fields of food and medicine. This study assessed the antibiofilm and antibacterial characterization of Curcuma longa rhizome extract against antibiotic-resistant foodborne pathogens. Gas Chromatography-Mass Spectrometry (GC-MS) and Fourier-transform infrared (FTIR) analysis were determined to check for the compounds, functional groups, and constituents of the plant extract. In-vitro antibiofilm and antibacterial bioassay of the extract were determined using standard bacteriological procedures. Potential mechanisms of the plant extract were also studied using standard biological methods. The important chemical constituents from the GC-MS extract of C. longa are arturmerone, cinnamyl angelate, tumerone, γ-atlantone, atlantone, α-atlantone, γ-atlantone and curlone. The FTIR analysis of the extract comprises alkyl halides, bromoalkanes, alkanes, ethylene molecules, arenes, amines, alcohols, sulfones, carboxylic acids and their derivatives, aromatic compounds, and phenols. The MIC of C. longa crude extract ranges from ethanol extract (0.03125 − 0.5 mg/mL) and acetone extract (0.0625 − 0.5 mg/mL). The MBC range is as follows: ethanol extract (0.125 − 1 mg/mL), acetone extract (0.125 − 1 mg/mL). The time-kill kinetics showed significant cell reduction with time. The bacterial isolates’ nucleic acids and protein leakage were consistent with increased extract concentration and time. There was a reduction in the biofilm cell on the shrimp surface and EPS with increased concentration and time. C. longa exerted significant anti-biofilm activity by removing existing biofilms, disrupting cell connections, and decreasing cells in biofilms. These findings can aid food protection from microbial contamination and prevent biofilms-related infections.

Behind the clicks: Can Amazon allocate user attention as it pleases?

We investigate Amazon's ability to direct user clicks to more visually prominent search results, even as quality declines with the increasing prevalence of sponsored advertising products. We analyze product results from over 2,000 Amazon Marketplace search queries to estimate how a top three most clicked product's features (price and quality) and screen placement influence a user's clicks. Our econometric results show that the position of a product search result (“position bias”), adjusted for its relative prominence on the screen, strongly shapes whether a user clicks on it. Within the top five search results, where typically four are advertisements, users exhibit decreased sensitivity to a product's relevance or pricing. This allows Amazon's sponsored ads to leverage product prominence as a mechanism for rent extraction from users and producers. Regulatory frameworks might limit platforms from exploiting consumers' satisficing behaviour online, including via moderating excessive advertising in algorithmic search results.

Anti-Inflammatory Effects of Honeysuckle Leaf Against Lipopolysaccharide-Induced Neuroinflammation on BV2 Microglia.

Neurodegenerative disorders have emerged as a major global public health concern, and the burden is predicted to increase over time. Modulating neuroinflammation and microglial activity is considered a promising target for improving neurodegenerative disorders. The leaf of honeysuckle (LH), which has anti-inflammatory properties, has long been collected, regardless of the season, and used for medicinal purposes. However, research on its effects on neuroinflammation is scarce. In this study, we determined the neuroprotective effects of LH water extract by inhibiting microglial activation induced by lipopolysaccharide (LPS). The production or secretion of pro-inflammatory mediators was examined in LPS-exposed BV2 cells to ascertain the efficacy of LH water extract in improving neuroinflammation. In addition, the phosphorylation of mitogen-activated protein kinases (MAPKs) and the degradation of inhibitory κBα (IκBα) were analyzed to elucidate the regulatory mechanisms of LH water extract. Ultra-performance liquid chromatography (UPLC) analysis was conducted to identify the active component of the LH. LH water extract suppressed the formation of inducible nitric oxide synthase (iNOS), nitric oxide (NO), and pro-inflammatory cytokines, including interleukin (IL)-1β and tumor necrosis factor (TNF)-α, in LPS-activated BV2 cells. LH impeded the activation of c-Jun N-terminal kinase (JNK). Moreover, chlorogenic acid was found in LH. The above findings suggest that LH water extract could improve neuroinflammation.

Bioluminescent Whole-Cell Bioreporter Bacterial Panel for Sustainable Screening and Discovery of Bioactive Compounds Derived from Mushrooms.

This study presents a rapid and comprehensive method for screening mushroom extracts for the putative discovery of bioactive molecules, including those exhibiting antimicrobial activity. This approach utilizes a panel of bioluminescent bacteria, whose light production is a sensitive indicator of various cellular effects triggered by the extracts, including disruption of bacterial communication (quorum sensing), protein and DNA damage, fatty acid metabolism alterations, and oxidative stress induction. The bioassay's strength is its ability to efficiently analyze a large number of extracts simultaneously while also assessing several different mechanisms of toxicity, significantly reducing screening time. All samples analyzed exhibited more than one cellular effect, as indicated by the reporter bacteria. Four samples (C. cornucopioides, F. fomentarius, I. obliquus, and M. giganteus) displayed the highest number (six) of possible mechanisms of antibacterial activity. Additionally, combining extraction and purification protocols with a bioluminescent bacterial panel enables simultaneous improvement of the desired antimicrobial properties of the extracts. The presented approach offers a valuable tool for uncovering the diverse antimicrobial mechanisms of mushroom extracts.

Exploring the Role of Herbal Compounds in Skin Aging: ASystematic Review of Topical Approaches.

Recently, dermatology has increasingly focused on understanding skin aging and exploring novel therapeutic approaches. Despite progress in cosmetic and pharmaceutical research, a significant gap remains in comprehensively understanding the effects and mechanisms of herbal extracts on skin aging. While many studies have examined the bioactivities of herbal compounds in preclinical models, comprehensive human trials have been scarce over the past decade. This review aims to address this gap by synthesizing human trials from the past decade, focusing on the therapeutic effects of herbal extracts on skin aging. The objective is to unravel the mechanisms contributing to skin aging and assess the therapeutic potential of herbal compounds. Following the PRISMA 2020 guideline, a systematic review was performed across OvidMEDLINE, Cochrane Central Register of Controlled Trials, and Embase via Ovid. A meticulous search strategy identified relevant clinical trials. The review highlights the essential role of herbal compounds in skin aging, particularly their antioxidant activity in suppressing the aging process. Analysis of 51 clinical trials offers valuable insights into their diverse effects on skin aging parameters. Herbal compounds are promising alternatives to synthetic products for treating skin aging. Their demonstrated efficacy in mitigating wrinkles, enhancing elasticity, maintaining hydration, and controlling pigmentation underscores their potential in developing antiaging therapeutics. However, further studies are needed to identify specific compounds responsible for these effects and understand their mechanisms. Future directions include conducting large-scale trials, exploring synergies with other ingredients, and optimizing delivery systems for sustainable, effective antiaging therapies.