Extraction Process Research Articles

Optimizing the microwave-assisted extraction and ultrasonic-assisted processes to acquire flavonoid and phenolic compounds from watermelon rinds using natural deep eutectic solvents

ABSTRACTS This study extracted flavonoids and phenolics from watermelon fruit by microwave-assisted extraction (MAE) and ultrasound-assisted extraction (UAE) combined with a natural deep eutectic solvent (NADES). The effectiveness of NADES combined with UAE and MAE was evaluated for extracting these compounds from watermelon rinds. The study chose a 2:1 molar ratio of hydrogen bond donors (HBDs) to hydrogen bond acceptors (HBAs). Among the tested combinations, 1,2-propanediol-lactic acid showed the highest solubility for flavonoids and phenolics within the extraction system. Experiments with a single factor were used to examine the effects of different parameters on total phenolic content (TPC) and total flavonoid content (TFC). The Box-Behnken design (BBD) model optimized the NADES-based UAE and MAE processes based on TPC and TFC responses. The results of the experiment and the predicted values from the BBD agreed well. Optimal conditions for recovering phenolics and flavonoids from watermelon rinds with NADES-based UAE were determined as 1/60 g/ml for SLR, 300 W, 20% water content, and 15 min, while NADES-based MAE: 1/50 g/ml for SLR, 400 W, and 3 min. This study presents an efficient approach for extracting biologically active compounds from watermelon.

Enhancing hotel efficiency and environmental health with biosensors and big data analytics

The hospitality industry embraces digital technologies to enhance efficiency, guest satisfaction, and environmental sustainability. Integrating biosensors and big data analytics allows hotels to monitor operational processes while maintaining high ecological health standards. However, the impact on environmental health is not fully understood, leading to suboptimal decisions and missed opportunities. This research proposes a novel Tabu Search Drove-Extended Multi-Layer Perceptron (TSD-EMLP) to evaluate the effectiveness of digital operations in hotels through the use of biosensors and big data for predicting hotel environmental conditions. Initially, smart biosensors are deployed in key areas and heating, ventilation, and air conditioning denoted as HVAC systems in the hotel, then the water quality data, noise levels, lighting quality, waste management data, operational data, financial and operational effectiveness data are collected and transmitted to the Internet of Things (IoT) cloud for further process. Interquartile Range (IQR) utilizes the IoT cloud data to remove sensor errors and anomalous events to frame outlier data for the Wavelet Packet Transform (WPT) feature extraction process to decompose sensor data into detailed frequency bands, allowing for precise analysis of complex environmental signals, TSD-EMLP model predicts the environmental health in hotels using the decomposed data. The results demonstrate reducing energy consumption, ventilation systems, indoor environment control, and guest satisfaction, improving air quality, and adjusting environmental settings based on real-time environmental conditions through TSD-EMLP optimized settings. TSD-EMLP classification model achieved high accuracy in predicting hotel environmental health, with a low improvement in guest satisfaction metrics.

SMLS-YOLO: an extremely lightweight pathological myopia instance segmentation method

Pathological myopia is a major cause of blindness among people under 50 years old and can result in severe vision loss in extreme cases. Currently, its detection primarily relies on manual methods, which are slow and heavily dependent on the expertise of physicians, making them impractical for large-scale screening. To tackle these challenges, we propose SMLS-YOLO, an instance segmentation method based on YOLOv8n-seg. Designed for efficiency in large-scale screenings, SMLS-YOLO employs an extremely lightweight model. First, StarNet is introduced as the backbone of SMLS-YOLO to extract image features. Subsequently, the StarBlock from StarNet is utilized to enhance the C2f, resulting in the creation of the C2f-Star feature extraction module. Furthermore, shared convolution and scale reduction strategies are employed to optimize the segmentation head for a more lightweight design. Lastly, the model incorporates the Multi-Head Self-Attention (MHSA) mechanism following the backbone to further refine the feature extraction process. Experimental results on the pathological myopia dataset demonstrate that SMLS-YOLO outperforms the baseline YOLOv8n-seg by reducing model parameters by 46.9%, increasing Box mAP@0.5 by 2.4%, and enhancing Mask mAP@0.5 by 4%. Furthermore, when compared to other advanced instance segmentation and semantic segmentation algorithms, SMLS-YOLO also maintains a leading position, suggesting that SMLS-YOLO has promising applications in the segmentation of pathological myopia images.

Lithium recovery from an alumina electrolysis slag leaching solution with strong acidity: Separation of lithium-ion and production of battery grade lithium carbonate

Lithium (Li) with excellent physicochemical properties is widely used in modern industries. However, the shortage and depletion of Li resources has hindered the sustainable economic development. Li recovery from waste resource was an effective approach, in particular, alumina electrolysis slag is exemplified as Li riched solid waste. In this work, separation of Li from alumina electrolysis slag leaching solution (AESLS) via solvent extraction using bis(2-ethylhexyl) hydrogen phosphate (P204) system was explored. Effects of saponification degree, phase ratio, and reaction time on metal ions impurities and Li extraction were investigated. Operational parameters including extraction, scrubbing, saponification and precipitation were optimized. Results showed that the extraction efficiencies of Mg2+, Ca2+, Al3+, Fe3+ and Li+ were 100 %, 100 %, 99.99 %, 100 % and 20.71 %, respectively, under the optimal single extraction conditions. The loss of Li after scrubbing of loaded organic phase using hydrochloric acid was as low as 1.59 %. The P204 extraction process was proved to be a thermodynamic process with spontaneous entropy increase, and the extraction order of metal ions was Al3+ > Fe3+ > Ca2+ > Mg2+ > Li+ according to the extraction driving force (ΔC); The solvent-extracted ligand mechanism was elucidated by infrared spectroscopy and nuclear magnetic resonance. Additionally, battery-grade lithium carbonate was produced. Our study paves a new way for recovery of Li in AESLS.

Study on the Spontaneous Combustion Law of Coal Body around a Borehole Induced by Pre-extraction of Coalbed Methane.

To address the challenges associated with the high gas content, high pressure, and low permeability coefficient in deep coal seams, strategies such as infilling boreholes and increasing the negative pressure of extraction are commonly implemented to alleviate issues related to coalbed methane extraction. However, long-term mining pressure can lead to the development of cracks in the coal seam near the borehole, thereby creating air leakage channels, which could potentially impact the oxygen supply during the extraction process. This leads to secondary disasters such as the spontaneous combustion of coal and gas explosions, considerably impacting the life and health of underground workers. To solve this issue, a thermal-fluid-solid coupling model for the working surface was constructed based on numerical simulation software, taking into account the multimechanism coupling effect of coal seam gas. The laws of coal oxidation and spontaneous combustion induced by coalbed methane extraction around boreholes were studied. The variation laws of the oxygen concentration, coal temperature, and oxidation heating zone around the borehole under different extraction conditions were simulated and analyzed. The findings demonstrate that the negative extraction pressure enables the gas to penetrate the fracture zone of the borehole, leading to an increase in the oxygen consumption rate and coal temperature around the borehole with an increase in negative extraction pressure. The coal gas leakage surrounding the borehole reduces as the sealing depth increases, and both the heating rate of coal and oxygen volume fraction show a downward trend. The fitting relationship between the negative pressure of drainage, depth of sealing, and temperature change in the coal body surrounding the boreholes was identified. It was determined that the negative pressure of 13 kPa for borehole drainage and a sealing depth >18 m are the optimal extraction parameters. The range of the oxidation zone and the position of the boundary line under this parameter were predicted, and the position function of the dangerous area of oxidation heating was defined. The research results have remarkable implications for the coordinated prevention and control of gas and coal spontaneous combustion in coalbed methane predrainage boreholes, as well as for efficient prevention and control of CO in on-site gas extraction boreholes, thus ensuring efficient and safe gas extraction.

Salar de Atacama Lithium and Potassium Productive Process

The average lithium content in the Earth’s crust is estimated at about 0.007%. Despite this, lithium is considered abundant and widely distributed, with significant extraction from various sources. Notably, the brines in the Salar de Atacama are highlighted for their high lithium concentration ~1800 mg/L. Lithium is currently recovered from these brines through a solar evaporation process. The brine is transferred through a series of ponds, increasing the lithium concentration from 0.2% to 6% over 18 months, while decanting other minerals like potassium, magnesium, and boron. This method is the most efficient and cost-effective globally due to the Salar de Atacama’s high lithium concentration of approximately 1800 ppm and the region’s intense solar radiation, which facilitates evaporation at no economic cost. This manuscript describes in detail the lithium and potassium extraction processes used in the Salar de Atacama.

TAMC: Textual Alignment and Masked Consistency for Open-Vocabulary 3D Scene Understanding

Three-dimensional (3D) Scene Understanding achieves environmental perception by extracting and analyzing point cloud data with wide applications including virtual reality, robotics, etc. Previous methods align the 2D image feature from a pre-trained CLIP model and the 3D point cloud feature for the open vocabulary scene understanding ability. We believe that existing methods have the following two deficiencies: (1) the 3D feature extraction process ignores the challenges of real scenarios, i.e., point cloud data are very sparse and even incomplete; (2) the training stage lacks direct text supervision, leading to inconsistency with the inference stage. To address the first issue, we employ a Masked Consistency training policy. Specifically, during the alignment of 3D and 2D features, we mask some 3D features to force the model to understand the entire scene using only partial 3D features. For the second issue, we generate pseudo-text labels and align them with the 3D features during the training process. In particular, we first generate a description for each 2D image belonging to the same 3D scene and then use a summarization model to fuse these descriptions into a single description of the scene. Subsequently, we align 2D-3D features and 3D-text features simultaneously during training. Massive experiments demonstrate the effectiveness of our method, outperforming state-of-the-art approaches.

Force Measurement Technology of Vision‐Based Tactile Sensor

Marker‐type vision‐based tactile sensors (VTS) realize force sensing by calibrating marker vector information. The tactile visualization can provide high‐precision and multimodal force information to promote robotic dexterous manipulation development. Considering VTS's contribution to force measurement, this article reviews the advanced force measurement technologies of VTSs. First, the working principle of marker‐type VTSs is introduced, including single‐layer markers, double‐layer markers, color coding, and optical flow. Then, the relationship between the marker type and the category of force measurement is discussed in detail. On this basis, the process of marker feature extraction is summarized, including image processing and marker‐matching technologies. According to the learning approach, force measurement methods are classified into physical and deep learning models. Further, branches of each method are analyzed in terms of input types. Combined with measuring range and precision, the correlation of sensor design, materials, and recognition methods to force measurement performance is further discussed. Finally, the difficulties and challenges are analyzed, and future developments are proposed. This review aims to deepen understanding of the research progress and applications and provide a reference for the research community to promote technology generations in related fields.

Continental-scale insights into the sugarbeet diffusion juice microbiomes

ABSTRACT Bacterial contamination of raw diffusion juice poses unique challenges during the sugar extraction process. This study profiled bacterial communities by using full-length 16S rRNA amplicon sequencing and quantified the carbohydrate concentrations in raw diffusion juice samples received from sugar factory regions across the USA and Canada. Juice samples were collected at four time points during the 2021 and 2022 processing campaigns. Firmicutes was the dominant phylum from the raw diffusion juice samples collected during both campaigns and comprised 85.5% of total bacterial abundance. Lactic acid bacteria such as Leuconostoc and Lactobacillus were among the core genera which also dominated the bacterial community in raw diffusion juice. Positive correlations in the abundance of functionally and taxonomically related bacterial communities were identified. During the 2021 campaign, 44 bacterial genera were differentially abundant in raw diffusion juice extracted from sugarbeet roots in Periods 1 to 4. This number declined sixfold during the 2022 campaign to three genera. The concentration of raffinose in raw diffusion juice positively correlated to the relative abundance of Leuconostoc . Furthermore, an in vitro assay was performed to assess the growth dynamics of Leuconostoc mesenteroides in sucrose or raffinose-rich medium and observed the rapid consumption of both carbohydrates by this bacterium. This finding is important for deciphering microbial growth dynamics in raw diffusion juice that can be useful in minimizing sugar loss during the factory processing. IMPORTANCE Findings additionally provide baseline information that can be used to develop mitigation strategies that reduce losses due to microbial contamination of sucrose processing streams.

Carbon dioxide as a sustainable reagent in circular hydrometallurgy.

This review highlights the use of CO2 as a reagent in hydrometallurgy, with emphasis on the new concept of circular hydrometallurgy. It is shown how waste CO2 can be utilised in hydrometallurgical operations for pH control or regeneration of acids for leaching. Metal-rich raffinate solutions generated after removal of the valuable metals can serve as feedstocks for mineral carbonation, providing alternative avenues for CO2 sequestration. Furthermore, CO2 can also be used as a renewable feedstock for the production of chemical reagents that can find applications in hydrometallurgy as lixiviant, as precipitation reagent or for pH control. Mineral carbonation can be combined with chemical reactions involving metal complexation reagents, as well as with solvent extraction processes for the concurrent precipitation of metal carbonates and acid regeneration. An outlook for future research in the area is also presented.

Priority radiomic parameters for computed tomography of head and neck malignancies: A systematic review

BACKGROUND: Radiomics is the newest and most promising direction in modern radiographic diagnostics. The number of head and neck cancer studies employing radiomics is increasing annually. A systematic review of recent publications (2021–2023) on computed tomography (CT) of head and neck malignancies was performed. AIM: To present systematized data on parameters for radiomic analysis for head and neck malignancies identified by CT data. MATERIALS AND METHODS: The literature search was carried out in PubMed. The basic characteristics of the selected articles were extracted, and their quality was assessed using RQS 2.0 and the modified QUADAS-CAD questionnaire. The reproducibility level of radiomic parameters selected for predictive models in different studies was assessed. Eleven articles were selected for the review. In most cases, a high risk of systematic error associated with data imbalance in terms of demographic parameters and level of pathologies was noted. RESULTS: The range of RQS 2.0 scores for the included articles varied from 19.44% to 50.00% of the maximum possible score. The decreasing research quality was mainly caused by the lack of external result validation (73% of the analyzed articles) and data accessibility and transparency (82%). Inter-study reproducibility of radiomic parameters was low owing to the wide variety of techniques used for image acquisition, image post-processing, extraction, and statistical processing of radiomic parameters. CONCLUSION: A set of stable radiomic parameters must be successfully introduced into clinical practice. The standardization of radiomics method and creation of an open radiomics database are necessary for this purpose.

Cyber-attack Detection and Mitigation Process under Big Data Consideration: Improved Recursive Feature Elimination-based Feature Selection

Due to the rapid growth of network technology, huge volume and distinct data sent via networks is expanding constantly. The situation shows how complex and dense cyber attacks and hazards are developing. Due to the rapid advancement in network density, cyber security specialists find it difficult to monitor all network activity. Due to frequent and sophisticated cyber attacks, it is becoming more challenging to detect and identify abnormalities in network events. The use of deep learning provides a variety of tools and strategies for automated cyber-attack detection as well as quick attack-type prediction as well as evaluation. This work introduces a novel cyber-attack detection and mitigation process under the following phases including preprocessing, feature extraction via the Map Reduce framework that handles the big data, feature selection, attack detection and mitigation. The Improved Normalisation process is achieved on the preprocessing phase. The work is examined from a big data perspective; hence Map Reduce framework is utilised for this. As a result, the framework will manage the feature extraction process, where features including statistical features, raw features, improved correlation-based features, and info gain-based features will be extracted. Following feature extraction, the Improved Recursive Feature Elimination procedure is processed that selects the relevant features. The hybrid detection model, which combines Recurrent Neural Networks (RNN) Deep and Belief Networks (DBN) is used to detect the attacks. Once an attack has been detected, the attacker must be mitigated. To accomplish this, an improved BAIT-based mitigation procedure is used. The two datasets used in this work are, namely, Intrusion Detection Systems (IDS) 2018 Intrusion CSVs (CSE-CIC-IDS2018) and UNSW_NB15. Finally, the suggested model and the alternative methods are contrasted using a variety of measures such as accuracy, sensitivity, specificity, precision, FDR, FNR and FPR.

Analyzing Foam Injection Dynamics in Porous Media: A Combined Computational and Experimental Approach

The study of foam injection dynamics into porous media holds numerous applications, with its primary use being the optimization of the oil extraction process. Additionally, it finds utility in various other domains, including soil decontamination techniques. This study focuses on evaluating foam injection through low-cost experiments and computational models. The model integrates a classic two-phase immiscible fluid flow in a porous media framework, augmented by a mobility reduction factor to simulate foam’s impact on gas phase mobility. Simple experiments were conducted to assess water displacement using both gas and gas-foam injection methods. Results revealed that while gas injection tends to form preferential paths rapidly, leading to gas disruption, foam injection generates a more compact front, enhancing sweep efficiency by mitigating preferential path formation. The computational model successfully reproduced the experimental findings qualitatively. This research introduces a cost-effective experiment suitable for didactic purposes, illustrating complex concepts and underscoring the complementary nature of experimental and computational methodologies in advancing engineering techniques.

Methodological advances in formulation and assay of herbal resources-based topical drug delivery systems.

Medicinal plants have been utilized for centuries as a source of healing compounds, which consist of thousands of known bioactive molecules with therapeutic potentials. This article aims to explore and emphasize the significance of medicinal plants and bioactive compounds in the development of topical pharmaceutical formulations. The journey from the extraction of phytochemicals to the development of topical pharmaceutical formulations is described with the aid of scientific evidence selected from PubMed, Google Scholar, ScienceDirect, and Web of Science. Articles published in English during 2018-2023 period were considered and selected randomly. The review discusses the extraction process of medicinal plants, solvent selection, and green synthesis of metal nanoparticles. Subsequently, various biological activities of plant extracts are elaborated especially focusing on antimicrobial, antioxidant, anti-inflammatory, and sun protection activities, along with the corresponding invitro assays commonly employed for the evaluation. The article presents the process of compound isolation through bioactivity-guided fractionation and also the toxicity evaluation of isolated fractions. Finally, the formulation of medicinal plant extracts into topical pharmaceuticals is addressed, emphasizing the stability evaluation procedures necessary for ensuring product quality and efficacy.

DeepTool: A deep learning framework for tool wear onset detection and remaining useful life prediction

Milling tool availability and its useful life estimation is essential for optimisation, reliability and cost reduction in milling operations. This work presents DeepTool, a deep learning-based system that predicts the service life of the tool and detects the onset of its wear. DeepTool showcases a comprehensive feature extraction process, and a self-collected dataset of sensor data from milling tests carried out under different cutting settings to extract relevant information from the sensor signals. The main contributions of this study are:•Self-Collected Dataset: Makes use of an extensive, self-collected dataset to record precise sensor signals during milling.•Advanced Predictive Modeling: Employs hybrid autoencoder-LSTM and encoder-decoder LSTM models to estimate tool wear onset and predict its remaining useful life with over 95 % R2 accuracy score.•Comprehensive Feature Extraction: Employs an efficient feature extraction technique from the gathered sensor data, emphasising both time-domain and frequency-domain aspects associated with tool wear.

Sintering Mechanism and Leaching Kinetics of Low-Grade Mixed Lithium Ore and Limestone

With the rapid development of new energy fields and the current shortage of lithium supply, an efficient, clean, and stable lithium resource extraction process is urgently necessary. In this paper, various advanced detection methods were utilized to conduct a mineralogical analysis of the raw ore and systematically study the occurrence state of lithium; the limestone sintering process was strengthened and optimized, elucidating the sintering mechanism and analyzing the leaching process kinetics. Under an ingredient ratio of 1:3, a sample particle size of 300 mesh, a sintering temperature of 1100 °C, a sintering time of 3 h, a liquid–solid ratio of 2:1, a leaching temperature of 95 °C, and a leaching time of 1 h, the leaching rate of Li reached 90.04%. The highly active Ca–O combined with Si–O on the surface of β–spodumene to CaSiO4, and Al–O was isolated and combined with Li to LiAlO2, which was beneficial for the leaching process. The leaching process was controlled by both surface chemical reactions and diffusion processes, and Ea was 27.18 kJ/mol. These studies provide theoretical guidance for the subsequent re-optimization of the process.

Forming performance and environmental impact of bamboo fiber reinforced polypropylene composites based on injection molding process for automobiles

AbstractTo explore the potential application of plant fiber reinforced composites for automotive component applications, this study prepared bamboo fiber (BF)/nano‐talc/polypropylene (PP) composites based on the injection molding process, comprehensively evaluated the effect of reinforcement materials on the forming properties of composites, including thermal performance, mechanical properties, water absorption, etc. Furthermore, taking a certain automotive injection molded interior part as the object, a life‐cycle assessment from production to the gate was conducted based on the real energy and material consumption during the composite preparation process. The results indicate that adding BF and talc powder increased the thermal stability, density, hardness, viscosity, and crystallinity of the composites while reducing the water contact angle on the surface. Surface‐modified BF and PP showed good compatibility. Talc powder exhibited good dispersibility in PP, and the synergistic effect of BF and talc powder effectively enhanced the composite performance. The tensile, flexural, and impact strength of the composites were improved by 40.64%, 51.48%, and 66.51%, respectively, compared with pure PP. The modulus of the composite increased nearly 2 times compared with pure PP. Additionally, the composite demonstrated good friction and wear properties. The environmental impact of the BF composite manufacturing process was significantly higher than that of pure PP. The substantial consumption of electricity, chemicals, and water resources in the extraction and modification processes of BF were the main factors. The findings of this study contribute to achieving green, high‐performance BF composite manufacturing and the expansion of its applications.Highlights Composites have a higher environmental impact compared to PP. BF and talc can synergistically enhance the performance of composites. BF shows good compatibility with PP. Composites exhibit good friction and wear characteristics.

One step from oxides to sustainable bulk alloys

Metallurgical production traditionally involves three steps: extracting metals from ores, mixing them into alloys by liquid processing and thermomechanical processing to achieve the desired microstructures1,2. This sequential approach, practised since the Bronze Age, reaches its limit today because of the urgent demand for a sustainable economy2–5: almost 10% of all greenhouse gas emissions are because of the use of fossil reductants and high-temperature metallurgical processing. Here we present a H2-based redox synthesis and compaction approach that reforms traditional alloy-making by merging metal extraction, alloying and thermomechanical processing into one single solid-state operation. We propose a thermodynamically informed guideline and a general kinetic conception to dissolve the classical boundaries between extractive and physical metallurgy, unlocking tremendous sustainable bulk alloy design opportunities. We exemplify this approach for the case of Fe–Ni invar bulk alloys6,7, one of the most appealing ferrous materials but the dirtiest to produce: invar shows uniquely low thermal expansion6,8,9, enabling key applications spanning from precision instruments to cryogenic components10–13. Yet, it is notoriously eco-unfriendly, with Ni causing more than 10 times higher CO2 emission than Fe per kilogram production2,14, qualifying this alloy class as a perfect demonstrator case. Our sustainable method turns oxides directly into green alloys in bulk forms, with application-worthy properties, all obtained at temperatures far below the bulk melting point, while maintaining a zero CO2 footprint.

Segmentation Synergy with a Dual U-Net and Federated Learning with CNNRF Models for Enhanced Brain Tumor Analysis

Background: Brain tumours represent a diagnostic challenge, especially in the imaging area, where the differentiation of normal and pathologic tissues should be precise. The use of up-to-date machine learning techniques would be of great help in terms of brain tumor identification accuracy from MRI data. Objective: This research paper aims to check the efficiency of a federated learning method that joins two classifiers, such as convolutional neural networks (CNNs) and random forests (R.F.F.), with dual U-Net segmentation for federated learning. This procedure benefits the image identification task on preprocessed MRI scan pictures that have already been categorized. Methods: In addition to using a variety of datasets, federated learning was utilized to train the CNN-RF model while taking data privacy into account. The processed MRI images with Median, Gaussian, and Wiener filters are used to filter out the noise level and make the feature extraction process easy and efficient. The surgical part used a dual U-Net layout, and the performance assessment was based on precision, recall, F1-score, and accuracy. Results: The model achieved excellent classification performance on local datasets as CRPs were high, from 91.28% to 95.52% for macro, micro, and weighted averages. Throughout the process of federated averaging, the collective model outperformed by reaching 97% accuracy compared to those of 99%, which were subjected to different clients. The correctness of how data is used helps the federated averaging method convert individual model insights into a consistent global model while keeping all personal data private. Conclusion: The combined structure of the federated learning framework, CNN-RF hybrid model, and dual U-Net segmentation is a robust and privacypreserving approach for identifying MRI images from brain tumors. The results of the present study exhibited that the technique is promising in improving the quality of brain tumor categorization and provides a pathway for practical utilization in clinical settings.

Psychosocial Impacts of Idiopathic Clubfoot on Parents and Children: A Scoping Review Protocol.

Idiopathic clubfoot is a complex pediatric foot deformity. The Ponseti technique is widely regarded as the standard for correcting deformities, and treatment compliance is essential for preventing relapse. Examining psychosocial effects on parents and/or children during clubfoot treatment provides valuable insights for improving compliance. This scoping review will map the existing literature on the psychological and social effects experienced by parents and/or children with idiopathic clubfoot. It also aims to examine the assessment tools used to measure these impacts and identify factors influencing treatment compliance. This review will adhere to the Joanna Briggs Institute (JBI) guidelines for scoping reviews. The search will include databases such as Scopus, Web of Science, EBSCOhost, MEDLINE, and PsycINFO and focus on studies published in the last 10 years. This review will include quantitative, qualitative, and mixed-method studies that investigate the psychological and social effects experienced by parents or affected children of any age with idiopathic clubfoot. Reporting will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) extension guidelines. The screening and data extraction process will involve two independent reviewers. The analysis will be descriptive and qualitative. The findings will be presented in tables and a narrative summary. This review may guide health practitioners in developing evidence-based interventions to improve treatment adherence.