Potential Method Research Articles

Band Gap Engineering of Binary and Cobalt-Doped PbS Thin Films Integrated by SILAR Method for Optoelectronic Potentials.

Thin films of PbS, both undoped and cobalt-doped (Co-PbS), were produced on glass substrates using the straightforward and controllable approach of sequential ionic layer adsorption and reaction (SILAR). The reactive substances employed to deposit the PbS thin films were lead nitrate (Pb(NO3)2), cobalt nitrate (Co(NO₃)₂(H₂O)₆), sodium thiosulphate (Na2S2O3) and H2O for different dipping concentrations of lead and cobalt. The films were adherent to the substrate and were compact, and crack-free with a shiny silver color. The films were structurally characterized by XRD and were found well crystallized according to the face-centered cubic formation. The films were optically characterized by the UV-Vis. spectrophotometer and absorption were found stronger in the UV and UV-Vis. region and then diminishes. Band gaps were determined to be between 1.8 eV and 2.2 eV, making them significant materials with the option of band gap engineering according to the desire by manipulating the compositions.

Enhancing Data Security through Digital Image Steganography: An Implementation of the Two Least Significant Bits (2LSB) Method

The rapid advancement of digital communication technologies has heightened the demand for robust data security methods, particularly for safeguarding sensitive information against cyber threats. Steganography, the art of concealing data within digital media, offers an effective means of securing communication by masking the very existence of hidden messages. This study evaluates the Two Least Significant Bits (2LSB) method as an enhancement to the conventional Least Significant Bit (LSB) technique, focusing on improving storage capacity and resistance to detection. The research employs a quantitative methodology, conducting experiments to analyze the impact of data embedding on image quality using metrics such as Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). Data embedding was tested on images of varying sizes and resolutions, with results compared before and after embedding. The study also evaluates the system’s tolerance to compression and robustness against steganalysis attacks, such as histogram analysis and noise detection. The results demonstrate that the 2LSB method significantly enhances storage capacity while maintaining high image quality. For instance, PSNR values remained above 40 dB across all tested images, indicating minimal visual distortion. The method also showed resilience to lossless compression formats such as PNG, with minimal impact on data integrity. However, lossy compression formats like JPEG posed challenges in maintaining the embedded data. The MSE values before and after embedding were minor, ensuring that the hidden data remained undetectable under standard analysis. This research contributes to developing more efficient and secure steganography techniques, providing practical applications in protecting sensitive data during transmission. The findings underline the 2LSB method's potential as a viable solution for digital security, balancing enhanced storage capacity with robust resistance to detection. Future work should further explore integrating advanced encryption with 2LSB to strengthen data protection in diverse digital environments

Threshold Condition of Energy Splitting for Some Classes of Symmetric Double Well Potential

The double-well potential (DWP) is a prevalent mathematical model for systems with 2 force centers, widely applied in quantum physics. Often expressed as a function with multiple parameters, the DWP can exhibit either single-well or double-well behavior depending on these parameters. Within the tunneling regime, optical properties are primarily characterized by the energy difference resulting from energy splitting (ES). Thus, formulating boundary conditions for the DWP and quantifying ES are critical but understudied areas. This research explores the threshold conditions for the existence of DWPs and ES in 2 common classes of symmetric DWPs: The extensively studied Razavy potentials and the more recently introduced Dong potentials. Utilizing quasi-exact solutions for Razavy potentials and the filter method for Dong potentials, we analyze the dependence of ES on DWP parameters. Our findings align well with existing numerical data, eigenfunction analysis and energy difference approaches. This innovative methodology allows for the examination of threshold conditions for higher ES, and provides an opportunity to control ES in DWPs by adjusting structural parameters. HIGHLIGHTS For symmetric-hyperbolicus double well potentials there are some threshold condition,.i.e.: (i) threshold for existence DWP, (ii) threshold for existence DWP with first-level energy splitting rE12 characterized by ε2 = Vmax, and (iii) Threshold for existence DWP with higher-level energy splitting rEij characterized by εj = Vmax. The method has been applied in the Razavy and Dong class potentials. The eigen-energies are determined through quasi-exact solutions for Razavy potentials and numerically using the filter method for Dong potentials. We find the the critical condition as a function od structural parameters. Our findings are in good agreement with the eigenfunction and energy difference approaches. GRAPHICAL ABSTRACT

Acute responses to a potentiation warm-up protocol on sprint and change of direction in female football players: a randomized controlled study

ObjectivesTo evaluate the acute effect of exposure to a potentiation warm-up protocol compared to a usual warm-up program.DesignRandomized parallel control trial.SettingSynthetic grass in the club’s facilities (Portugal).ParticipantsSeventeen female football players (age: 23.9 ± 3.9 years), were randomly allocated to a control (n = 8) and an experimental group (n = 9). To allocate the players, a table was computer-generated by a research team member with no involvement in the trial.InterventionThe control group performed their usual warm-up program, while the experimental group performed a potentiation warm-up protocol with jumps combined with sprints with change of direction.Main outcomes measuresThe players were tested pre- and post-intervention for a 40-m linear sprint and pre-planned change of direction using the T-test.Statistical analysisAn intention-to-treat analysis was performed, with all the participants originally randomized being involved. The normal distribution was verified by the Shapiro-Wilk test. The assumption of sphericity was analyzed. Effect sizes were calculated using partial eta squared.ResultsNo significant pre-post differences in the T-test and in the 40-m sprint were detected for any group. However, in the T-test, large effect sizes in time increments were observed within the experimental (0.27 s; p > 0.05, ηp2 = 0.176) and control groups (0.06 s; p > 0.05, ηp2 = 0.176). Also, in the 40-m sprint, large effect sizes in time increments were observed within the experimental (0.05 s; p > 0.05, ηp2 = 0.251) and control groups (0.09 s; p > 0.05, ηp2 = 0.251).ConclusionsThe performance-enhancing ability of the potentiation method performed at warm-up was not verified when applied to female football players. Thus, the potentiation methods may not improve sprint and COD ability for this population. However, the lack of statistical significance may have been due to reduced statistical power, as three of four effects suggest acute performance impairment after a supposed potentiation-oriented warm-up. Nevertheless, the presence of a statistical type 2 error cannot be ruled out.Registration number (retrospectively registered)NCT06555185; Project URL: https://clinicaltrials.gov/study/NCT06555185.

Novel bias-reduced coherence measure for EEG-based speech tracking in listeners with hearing impairment.

In the literature, auditory attention is explored through neural speech tracking, primarily entailing modeling and analyzing electroencephalography (EEG) responses to natural speech via linear filtering. Our study takes a novel approach, introducing an enhanced coherence estimation technique to assess the strength of neural speech tracking. This enables effective discrimination between attended and ignored speech. To mitigate the impact of colored noise in EEG, we address two biases-overall coherence-level bias and spectral peak-shifting bias. In a listening study involving 32 participants with hearing impairment, tasked with attending to competing talkers in background noise, our coherence-based method effectively discerns EEG representations of attended and ignored speech. We comprehensively analyze frequency bands, individual frequencies, and EEG channels. Frequency bands of importance are shown to be delta, theta and alpha, and the important EEG channels are the central. Lastly, we showcase coherence differences across different noise reduction settings implemented in hearing aids (HAs), underscoring our method's potential to objectively assess auditory attention and enhance HA efficacy.

A novel method for assessment rooftop PV potential based on remote sensing images

The assessment of rooftop photovoltaic (PV) potential is highly significant for energy policy formulation. With the rapid development of computer vision (CV) and remote sensing imagery, utilizing CV to extract rooftop information is an ideal approach. However, deep learning requires a large amount of accurately annotated data, and annotating remote sensing images is a labor-intensive task. This limitation hinders the application of deep learning in rooftop PV potential assessment. To address this issue, this paper proposes a semi-supervised learning (SSL)-based segmentation model to extract rooftop information from remote sensing images. Subsequently, a rooftop classification method is proposed to categorize rooftops into several classes and estimate their rooftop PV available area ratios. Finally, the total available rooftop PV area in urban areas is evaluated, and the potential rooftop PV installed capacity and power generation are calculated. This method is applied in the Longhu District of Shantou City, Guangdong Province. The evaluation results show that the total rooftop area in Longhu District is 17.2 km2, with a rooftop PV available area of 12.7 km2. It is estimated that the rooftop PV installed capacity in Longhu District is 1849.4 MW, with an annual power generation of 2219.3 GWh.

Assessing vertical seed dispersal through seed oxygen isotope ratios of Japanese larch (Larix kaempferi)

AbstractVertical seed dispersal, involving seed movement to higher or lower elevations, plays a vital role in plant adaptation to rising temperatures. However, its assessment has been limited owing to labor‐intensive processes. A recently developed method exploits the negative correlation between elevation and seed oxygen isotope ratios (18O/16O; hereafter δ18O) for efficient evaluation of vertical seed dispersal. Nevertheless, it remains unclear whether this method is suitable for conifers and whether plant size and age introduce noise into δ18O values, which would affect vertical seed dispersal estimations. In this study, we investigated the association between elevation and seed δ18O values of Japanese larch (Larix kaempferi) and explored the relationship among these oxygen isotope ratios, tree height, diameter at breast height (DBH), and age. Notably, we found a negative correlation between elevation and seed δ18O values of L. kaempferi but failed to detect any influence of tree height, DBH, and age on these oxygen isotope ratios. These findings suggest this method's potential for accurately estimating vertical seed dispersal across diverse plant lineages.

Rolling bearing fault diagnosis based on acoustic-vibration data fusion and mode decomposition combined with the crested porcupine optimization algorithm

Variational Mode Decomposition (VMD) is extensively utilized in the domain of rotating machinery fault diagnosis. Nevertheless, the reliance on empirical parameter tuning and the limitations inherent in traditional single-signal (either vibration signals or acoustic signals) fault diagnosis methods present substantial challenges. These include incomplete information and high sensitivity to noise. To address these shortcomings, a new method is introduced that integrates multi-modal sensor data and implements an adaptive VMD approach, enhanced through the Crested Porcupine Optimization Algorithm (CPO) to automatically optimize the key parameter. Then, uses optimized VMD to decompose the time series into intrinsic mode functions of different frequencies to capture the multi-scale characteristics of data. Finally, the decomposed acoustic-vibro signal components are fed into the CNN-BiGRU-AT network, which learns the spatio-temporal dependencies to enhance the accuracy of fault classification. The experimental results conducted across diverse high-noise environments demonstrate that this method’s superior noise robustness compared to single-modality sensor approaches， highlighting the method's potential for machinery fault detection in noisy conditions.

Direct hydroacoustics analyses of pipe orifice using lattice Boltzmann method

Acoustic waves in water pipes pose a structural threat but also offer a valuable tool for non-invasive inspection. Complex pipe geometries such as bends and surface liners create complex fluid boundaries, triggering interactions between fluid flow and acoustics. The lattice Boltzmann method (LBM) excels at capturing these interactions near complex boundaries, in contrast to the finite volume method, which can result in errors. However, the application of LBM in water pipes has been limited by stability problems. This study proposes a two-step (DM-TS) collision operator based on a direct method (LBM-HA) for stable LBM simulations in water pipes. LBM-HA enables direct hydroacoustic predictions for both acoustic and dynamic flow fields in a pipe orifice. A novel acoustic-dynamic complex boundary condition was formulated from the linearized Euler's equation to account for the physical effects of the bounded domain of the LBM-HA analysis. To distinguish between dynamic and acoustic components, wavenumber and frequency decomposition techniques were applied to the pressure data obtained within the pipe. In addition, mode decomposition of the acoustic pressure was conducted to identify the dominant acoustic modes. By comparing the LBM-HA results with experimental data, we highlighted the method's potential for direct hydroacoustic analysis considering the acoustic characteristics of the water pipe.

A spatio-temporal forecasting method of fracture distribution using dynamically exposed rock images in tunnel: Methodology and application

Forecasting the geometric characteristics of rock fractures in unexcavated tunnel sections is crucial for construction arrangements. This paper presents a method for predicting fracture distribution near the tunnel face from a spatiotemporal perspective. We Innovatively analogize the tunnel excavation mileage as the time series data. Using fractal geometry and geostatistics, we extract geometric features of peripheral rock fractures and establish a spatiotemporal dataset for dynamic prediction. The SCINet model, a spatial-aware recursive neural network, is employed for deterministic-stochastic dynamic prediction. Numerical simulations are conducted to validate the predictive accuracy of the model for various distributions, including trace length, dip angle, and density. Notably, the Mean Absolute Percentage Error (MAPE) for trace length prediction is remarkably low at 5.49 %, and the Kullback-Leibler (KL) divergence is as low as 4.81 %. The method is further applied to an underground oil storage cavern, China, revealing structural surface information. By incorporating continuously exposed fracture data, prediction accuracy is progressively improved, demonstrating the method's potential as a predictive tool for enhancing tunnel construction safety and efficiency.

Towards improving the characteristics of high-energy pyrazines and their N-oxides.

Based on the methods of quantum chemistry and atom-atom potentials, the molecular and crystal structure of a number of high-energy pyrazines was modeled: unsubstituted diazines, as well as fully nitrated 1,4-diazabenzenes, their oxides and polymorphs. The enthalpies of formation, densities of molecular crystals, and some performance characteristics of these compounds were determined. The parameters of decomposition of substances were estimated. It has been established that tetranitropyrazine-1,4-dioxide has maximum energy content and excellent performance characteristics, which determine the prospects for using this compound as a high-energy one in the considered series of compounds. In this work, DFT calculations were conducted through the software Gaussian 09 using B3LYP functional with basis set aug-cc-PVDZ and the Grimme dispersion correction D2. For crystal structure optimization, the atom-atom potential methods with PMC program (Packing of Molecules in Crystal) were used. Charges for molecular electrostatic potential were fitted by FitMEP and enthalpies of formation in gas phase were assessed by G3B3.

A Novel Carbon Dot-Bromothymol Blue System for Ratiometric Colorimetric-Fluorometric Sensing of Glutathione in Urine: A Smartphone-Compatible Approach.

This study presents a novel dual-modal approach for glutathione (GSH) detection using blue and yellow dual-emission carbon dots (BY-CDs) and bromothymol blue (BTB) at pH 8.0. The method employs both colorimetric and fluorometric detection modes, offering a new perspective on GSH quantification. BTB's blue coloration induces selective fluorescence quenching of the CDs' 610nm emission peak, with minimal effect on the 445nm peak. Upon GSH addition, the quinonoid structure (blue color) of BTB transforms to its benzenoid form (yellow color). This transformation triggers fluorescence restoration at 610nm and significant quenching at 445nm, enabling ratiometric fluorescence analysis (F610/F445). Concurrently, colorimetric detection is also ratiometric, based on measuring the ratio between the emerging yellow color peak (435nm) and the decreasing blue color peak (618nm) (A435/A618). The state-of-the-art aspect of this detection method lies in the strategic choice of dual-emission CDs and a dye with distinct absorption spectra that closely match the emission spectra of the CDs. This unique combination allows for dual detection with opposite responses in the two detection modes, enhancing selectivity and reliability. The probe was thoroughly characterized, and its detection mechanism was elucidated using various spectroscopic techniques. The method shows excellent linearity, a broad detection range, and low detection limits for both fluorometry (0.02 - 10.0μM, 5.88nM) and colorimetry (1.0 - 35.0μM, 301.25nM). Additionally, a smartphone-based platform was developed for colorimetric GSH determination, enhancing the method's potential for on-site analysis. The assay's practicality was validated through successful application to human urine samples, yielding excellent recovery values (97.33% to 99.13%).

Clothing design factors, aesthetic experience, and preference: Additional insights from neuromarketing in civil defense clothing

ABSTRACT This study analyzed the relationships among clothing design factors, aesthetic experience, and preference and suggested ways to design civil defense clothing with better work performance. We use behavioral and electroencephalogram (EEG) experiments to identify user-preferred colors and styles and incorporate them into an improved civil defense clothing design. Through the behavioral experiment, we identified the civil defense clothing design preferred by users and explored the aesthetic experience that influenced user preference. We used the neuroscientific method of event-related potentials to further explore the neural mechanisms of users’ preferred civil defense clothing designs. In the EEG experiment, the preferred civil defense clothing contains frontal late positive potential (LPP) components involved in aesthetic experience. These results indicate that the improved civil defense clothing design evoked aesthetic experiences from users. We expect that using advanced materials and design improves the user’s aesthetic experience and preferences. This research contributes to the development of civil defense clothing that not only enhance functionality but also improve user experience and work performance.

Flipped Classroom in the Czech Republic

The Flipped Classroom approach, advocated since the 1990s by scholars such as Alison King, Eric Mazur, Maureen J. Lage, Glenn J. Platt, and Michael Treglia, involves students independently learning theoretical content before class via e-learning resources like video recordings and self-directed programmes. This allows educators to use class time for discussions and scaffolding as students engage in problem-solving tasks individually and in groups. Notable milestones include the establishment of Khan Academy in 2004, MEF University's adoption of flipped teaching in 2011, the founding of the Flipped Learning Network in 2016, and the extensive use of e-learning during the COVID-19 pandemic. This paper examines implementing a flipped classroom project in environmental education in the Czech Republic. In 2018, three academics from different universities initiated the flipping of environmental education courses. The research, framed as combined action research, employed a questionnaire as the primary tool. Data were collected twice: in 2021, at the end of mandatory online teaching due to the COVID-19 pandemic, and in 2024, after three years of regular education. Responses from 288 students were analysed using standard statistical methods, while open-ended responses from 82 students and interviews with four university lecturers were qualitatively assessed. The findings underscore the benefits of electronic materials, particularly students’ appreciation for interactive elements, time flexibility, and the ability to review content multiple times. Students' willingness to watch digital lectures at home decreased post-pandemic, and the perceived efficacy of this method was higher among students during the pandemic. Although students find the flipped classroom method effective, they favour its combination with traditional teaching methods. Educators, cognisant of the method's potential, interpret these results as indicative of “virtual environment fatigue”, noting students’ reluctance to prepare in advance as a significant barrier. They advocate for the widespread implementation of the flipped classroom model across faculties to optimise instruction and eliminate the need for switching between traditional and flipped approaches. This study offers a practical example and insights for educators seeking to integrate the flipped classroom methodology with e-learning tools into their teaching practices.

Optimising ride-sharing efficiency: innovative shareability-focused pricing strategies

Ride-sharing is increasingly recognised as a potential solution to transportation issues such as traffic congestion and parking problems. The success of ride-sharing systems depends on the willingness of individuals to share rides, and effective pricing strategies are among the crucial factors. However, creating a fair pricing system for riders and providers remains challenging. This research introduces a new pricing scheme based on the concept of shareability. This concept considers all the possible sharing situations among trips. It is designed to incentivize riders and accommodate providers' criteria. This scheme calculates the trip fare for passengers, considering various ride-sharing scenarios derived from the shareability function, thus ensuring fairness and transparency for passengers. Using the macroscopic fundamental diagram, our model incorporates traffic dynamic conditions. We have applied the method to the real case of Lyon city in France to assess the effectiveness of our approach. Through a comprehensive analysis of various configurations of the proposed pricing scheme and benchmarking with the existing approaches, we demonstrate its superior performance in reducing ride rejections compared to traditional methods. This result showed the proposed method's potential for enhancing ride-sharing systems' effectiveness.

A polar diatomic molecule under a high-frequency laser field: classical analytical solution

AbstractDynamics of a polar diatomic molecule (represented as the oscillating rotator model) under external fields is the most fundamental problem and the testbed in molecular physics—analogously to the dynamics of hydrogenic atom (represented as the model of an electron in the Coulomb field) under external fields being the most fundamental problem and the testbed in atomic physics. We present the classical analytical study of the dynamical Stark effect in the oscillating rotator under a high-frequency laser field. We obtain the analytical results by using the method of effective potentials. We demonstrate that under the laser field, the rotation frequency increases, while the oscillation frequency decreases, and the amplitude of the oscillations increases. More significantly, the laser field causes the generation of the second harmonic in the oscillations of this system. This is an important, counterintuitive result in studying this fundamental physical system. We also drew attention to some flaws in the literature on this subject.

A Cross-Modal Mutual Knowledge Distillation Framework for Alzheimer's Disease Diagnosis: Addressing Incomplete Modalities.

This paper was motivated by the challenge of early AD diagnosis, particularly in scenarios when clinicians encounter varied availability of patient imaging data, such as MRI and PET scans, often constrained by cost or accessibility issues. We propose an incomplete multimodal learning framework that produces tailored models for patients with only MRI and patients with both MRI and PET. This approach improves the accuracy and effectiveness of early AD diagnosis, especially when imaging resources are limited, via bi-directional knowledge transfer. We introduced a teacher model that prioritizes extracting common information between different modalities, significantly enhancing the student model's learning process. This paper includes theoretical analysis, simulation study, and real-world case study to illustrate the method's promising potential in early AD detection. However, practitioners should be mindful of the complexities involved in model tuning. Future work will focus on improving model interpretability and expanding its application. This includes developing methods to discover the key brain regions for predictions, enhancing clinical trust, and extending the framework to incorporate a broader range of imaging modalities, demographic information, and clinical data. These advancements aim to provide a more comprehensive view of patient health and improve diagnostic accuracy across various neurodegenerative diseases.

Advancing IoT security: A novel intrusion detection system for evolving threats in industry 4.0 using optimized convolutional sparse Ficks law graph point trans-Net

With the rapid advancement of Industry 4.0, the integration of Internet of Things (IoT) strategies in industrial environments has increased exponentially. While this integration enhances productivity and efficiency, it also introduces significant security vulnerabilities. Previous research has employed several deep learning approaches for intrusion detection; however, these methods often suffer from insufficient accuracy, increased computational time, complexity, and higher error rates. To address these issues, this work proposes an innovative solution: "Advancing IoT Security: A Novel Intrusion Detection System (IDS) for Evolving Threats in Industry 4.0 using optimized Convolutional Sparse Fick's Law Graph Pointtrans-Net (CSFLGPtrans-Net)." The proposed system utilizes a comprehensive intrusion detection dataset composed of four different datasets: ToN-IoT, NSL-KDD, CSE‑CIC‑IDS2018, and IoT_bot. Initially, the input data undergoes a pre-processing stage that includes cleaning columns and rows, encoding features, and normalizing data. Following this, a hybrid optimization method, combining the Fire Hawk Optimizer with the Spider Wasp Optimizer, is applied for feature selection. This step is crucial for identifying the most significant features to enhance classification accuracy. The refined data is then classified using the CSFLGPtrans-Net model. To ensure secure data transfer, Fuzzy-based Elliptic Curve Cryptography (FECC) is employed. Experimental simulations conducted on the Python platform demonstrate that the proposed method outperforms existing approaches across various performance metrics, achieving a higher accuracy of 98% and a recall of 0.993. These results highlight the method's superior efficiency and potential for further advancement in securing Industry 4.0 environments.

Hydrogel Containing Propolis: Physical Characterization and Evaluation of Biological Activities for Potential Use in the Treatment of Skin Lesions.

Skin injury affects the integrity of the skin structure and induces the wound healing process, which is defined by a well-coordinated series of cellular and molecular reactions that aim to recover or replace the injured tissue. Hydrogels are a group of promising biomaterials that are able to incorporate active ingredients for use as dressings. This study aimed to synthesize hydrogels with and without propolis extract and evaluate their physical characteristics and biological activities in vitro for potential use as active dressings in the treatment of skin lesions. The antifungal [Candida albicans (C. albicans) and Candida tropicalis (C. tropicalis)] and antibacterial [Staphylococcus aureus (S. aureus), Pseudomonas aeruginosas (P. aeruginosas) and Escherichia coli (E. coli)] activity was assessed by the microdilution method in plates and antioxidant potential by the reduction of the phosphomolybdate complex. The hydrogels showed good water absorption capacity, high solubility, and high gel fraction, as well as good porosity, water retention, and vapor transmission rates. They revealed a totally amorphous structure. The extract and the hydrogels containing the propolis extract (1.0% and 2.5%) did not inhibit fungal growth. However, they showed antibacterial activity against strains of S. aureus and P. aeruginosas. Regarding the E. coli strain, only the extract inhibited its growth. It showed good antioxidant activity by the evaluation method used. Therefore, the hydrogels containing propolis extract can be a promising alternative with antibacterial and antioxidant action for use as dressings for the treatment of skin lesions.

Octylamine-oxalic acid (Oct-Oxa) deep eutectic solvent for the separation of Sudan II from food samples

This work presents a novel deep eutectic solvent (DES) formed of octylamine and oxalic acid (Oct-Oxa) that was effectively used to separate Sudan II dye from food and water samples. The prepared DES was characterized using Fourier transform infrared spectroscopy (FT-IR) and carbon-13 nuclear magnetic resonance (13C NMR). Key parameters were optimized, including a short ultrasonication time of 30 s and a very low DES volume of only 500 μL that could be separated within one minute of centrifugation. The remaining parameters were also tested: pH (7), DES molar ratio (1:1), and sample volume (40 mL). The method demonstrates excellent recovery and accuracy, with limits of detection (LOD) of 1.2 μg kg−1 and quantification (LOQ) of 4.1 μg kg−1. It facilitates rapid separation and sensitive detection of Sudan II dye using ultraviolet-visible (UV-VIS) spectrophotometry. The novelty of this approach lies in the use of a novel DES, the minimal volume of DES required, and the efficiency of the short ultrasonication time, highlighting the method's potential for sustainable, cost-effective extraction in environmental and food safety applications.