Extraction Efficiency Research Articles

Geomechanical Response Characteristics of Different Sedimentary Hydrodynamic Cycles—Exampled by Xujiahe Formation of Upper Triassic, Western Sichuan Basin

This study delves into the geomechanical responses of different sedimentary hydrodynamic cycles in deep tight sandstone formations. Employing core observation and thin section analysis, we quantitatively identified and characterized bedding planes, sedimentary microfacies, and tectonic fractures. Then, the intricate relationships between various architectural interfaces and geomechanical parameters were elucidated. Subsequently, utilizing finite element numerical simulation software, in situ stress and fracture parameters were derived. By identifying a fracture facies zone correlated with the sedimentary hydrodynamic cycle and production data, our findings unveil several key insights: (1) Geomechanical parameters (Young’s modulus, Poisson’s ratio, brittleness index) exhibited noteworthy variations within the T3x2−5 sand group, indicative of weak elasticity and robust plasticity. (2) The effective distance, influenced by diverse reservoir architecture interfaces, displayed variability, with each transition between peak-valley-peak or valley-peak-valley pinpointed as a distinct sedimentary hydrodynamic cycle. (3) In environments characterized by strong sedimentary hydrodynamics (between two level 3 architecture interfaces), fractures with larger strike angles and lower dip angles were observed to be more prevalent. (4) Three significant fracture faces—level I, level II, and level III—were discerned within the study area. Notably, reservoirs associated with level III exhibited characteristics suggestive of medium porosity and permeability, indicative of a gas layer. By thoroughly understanding the geomechanical response characteristics of formations such as the Xujiahe Formation, it is possible to guide the exploration and development of energy resources such as oil and natural gas. This helps to improve the efficiency and safety of resource extraction, promoting the sustainable utilization of energy.

Virtual instrumentation-based data collection and analysis for CNC machining process

Using Computer Numerical Control (CNC) machine tools in manufacturing is crucial for exact processing materials. The quality of the machined product heavily relies on the machine's condition. Therefore, monitoring and assessing the machine's status is essential to ensure product quality and enhance its lifespan. However, due to cost, space, and technical limitations, only a few physical variables, such as acceleration, force, displacement, acoustic radiation, temperature, and flow conditions, can be measured. Efficient transfer and extraction of these data are critical for monitoring machine status using statistical methods or valuable signatures. Traditional measurement instruments are complex and numerous, whereas measurement and analysis systems based on virtual instrumentation technology collect necessary data from sensors and data acquisition cards, meeting the needs of test analysis. Virtual instrumentation utilizes computer hardware resources, modularization hardware, and software systems for data analysis, communication, and operation interface, providing greater versatility, flexibility, compatibility, and repeatability. Previous research has shown successful attempts at developing LabVIEW-based systems for monitoring CNC milling machines and analyzing cutting parameters and forces. This project aims to collect and process data from the CNC machining process. The main objectives include writing a LabVIEW software program, recording data from CNC machine programs using the provided hardware (myRIO) and LabVIEW program, processing the data using MATLAB software, and analyzing and discussing the obtained results. The report consists of four parts, starting with an introduction to the project's background and literature review, followed by a description of the project steps, methods, experiments, and data processing. The processed data graphs will be presented and discussed, and recommendations for future research will be provided before concluding with a study summary.

Recent advances of sweat sampling, sensing, energy-harvesting and data-display toward flexible sweat electronics

Sweat contains diverse types of biomarkers that can mirror an individual’s health condition. The forefront research of sweat monitoring primarily focuses on sensing basic parameters, i.e., sweat rate and single electrolyte imbalances in controlled laboratory settings. However, recent works show the potential of sweat for the rich biomarkers in aspects of comprehensive health status display, timely safety alarming, and energy harvesting. The advances in wearable flexible electronics enable continuous, real-time, noninvasive detection of multiple sweat components, providing molecular-level insights into human physiology and psychology information; additionally, the efficient sweat extraction technologies of flexible electronics promote its application in energy harvesting, contributing to advancing a flexible sweat platform. This review comprehensively explores flexible sweat-based electronics, encompassing four key aspects: sweat sampling methods, sweat-based sensors, sweat-based energy harvesters, and sweat data display methods. Firstly, the traditional sweat-based platform is discussed in sweat sampling, sensing, and data analysis. Then, the development of wearable sweat sampling methods is discussed with a comparison of the traditional sweat collection methods. After that, the recent advances in sweat-based biosensors for monitoring diverse sweat analytes, such as the perspiration volume, glucose, lactate, and uric acid levels, are summarized. Subsequently, this review also highlights the recent progress and potential value of sweat-based energy harvesters in sweat-activated batteries and bio-fuel cells. Furthermore, multiple data display methods are proposed to achieve accurate feedback on health status, such as colorimetric techniques, light-emitting diodes, actuators, etc. Finally, this review concludes the main current challenges faced in practical applications of sweat-based bioelectronic systems and proposes a vision for the future evolution of this promising field.

Segmentation of ethnic clothing patterns with fusion of multiple attention mechanisms

AbstractTo address the challenges posed by diverse pattern-background elements, intricate details, and complex textures in the semantic segmentation of ethnic clothing patterns, this research introduces a novel semantic segmentation network model called MST-Unet (Mixed Swin Transformer U-net). The proposed model combines a U-shaped network structure with multiple attention mechanisms. The upper layers of the model employ classical convolutional operations, focusing on local relationships in the initial layers containing high-resolution details. In deeper layers, Swin Transformer modules are utilized, capable of efficient feature extraction with smaller spatial dimensions, maintaining performance while reducing computational burden. An attention gate mechanism is integrated into the decoder, contributing to enhanced performance in ethnic clothing pattern segmentation tasks by allowing the model to better capture crucial image features and achieve precise segmentation results. In visual comparisons of segmentation results, our proposed model demonstrates superior performance. The segmentation results exhibit more complete preservation of edge contours and fewer misclassifications in irrelevant regions within the images. In qualitative and quantitative experiments conducted on the ethnic clothing pattern dataset, our model achieves the highest Dice score for segmentation results in all four subclasses of ethnic clothing patterns. The average Dice score of our model reaches an impressive 89.80%, surpassing other algorithms in the same category. When compared to Deeplab_V3+, ResUnet, SwinUnet, and Unet networks, our model outperforms them by 7.72%, 5.09%, 5.05%, and 0.67%.

Effect of distributed Bragg reflectors on optoelectronic characteristics of GaN-based flip-chip light-emitting diodes

Abstract Distributed Bragg reflectors have been widely utilized in GaN-based flip-chip light-emitting diodes (FCLEDs) owing to their excellent reflection performance. Recently, wide reflected angle DBR (WRA-DBR) has been suggested to enhance the optical characteristics of GaN-based FCLEDs by incorporating multiple sub-DBRs with varying central wavelengths. However, the reflectivity of WRA-DBR decreases at large incident angle from 425 nm to 550 nm, which restricts further optical performance improvement of FCLEDs. Here, we demonstrate a quintuple-stack DBR comprised of five sub-DBRs. The quintuple-stack DBR possesses a high reflectivity (>97.5%) for incident angles below 50° within the blue and green light wavelength ranges. Compared to WRA-DBR, quintuple-stack DBR exhibits a higher reflectivity in wavelength range of 425 nm to 550 nm and thinner multilayer thicknesses. Furthermore, stronger electric field intensities exist in the top facet and sidewalls of FCLED with quintuple-stack DBR, revealing that quintuple-stack DBR is beneficial for enhancing the light extraction efficiency. As a result, the light output power of FCLED with quintuple-stack DBR is ~3% higher than that of FCLED with WRA-DBR at 750 mA.

Response Surface Methodology: An Optimal Design for Maximising the Efficiency of Microwave-Assisted Extraction of Total Phenolic Compounds from Coriandrum sativum Leaves

The optimization of total phenolic compounds (TPC) extraction yield and maximization of total antioxidant capacity (TAC) from coriander leaves were investigated using response surface methodology. The extraction of TPC was carried out using microwave-assisted extraction. A Box-Behnken design was used to study the effects of the three independent variables, solvent concentration (ethanol/water 20–80%), microwave power (100–500 watt) and irradiation time (30–150 s) on the response. A second-order polynomial model was used to predict the reaction. The regression analysis showed that about 99% of the variations could be explained by the models. The predicted values were 50.97 GAE/g dw and 5.75 mg GAE/g dw for TPC and TAC. The reaction surface analysis showed that the optimum extraction parameters that maximized the extraction of antioxidants yield were 52.62% ethanol, 452.12 watt and 150 s. Under optimal conditions, the experimental values for TPC and TAC were 49.63 ± 0.93 mg GAE/g dw and 5.55 ± 0.07 mg GAE/g dw, respectively. The experimental values are in agreement with the predicted values, indicating the suitability of the model used and the success of the response surface methodology in optimizing the extraction conditions.

Determination of 13 potential anti-obesity agents in hair by dispersive liquid-liquid microextraction-assisted UHPLC-MS/MS

As the adulteration of dietary supplements with synthetic drugs remains a prevalent issue, the inclusion of anti-obesity agents may pose health risks, potentially leading to central nervous system or cardiovascular diseases. However, surveillance studies on the use of anti-obesity agents by the Chinese population are limited. This study aims to establish an efficient and rapid hair pretreatment method using dispersive liquid-liquid microextraction (DLLME) combined with high-speed grinding and develop a sensitive and accurate analytical method employing ultra high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) for detecting 13 potential anti-obesity agents in hair samples. Herein, hair samples were washed sequentially with 0.1% sodium dodecyl sulfate (SDS), water and acetone, and then ground at high speed using 1 mL of an extraction solution (internal standard solution-n-butanol-1.2 mol/L Na2HPO4, pH10.0, 100:400:500, v/v/v for procaterol; internal standard solution-ethyl acetate-1.2 mol/L Na2HPO4, pH8.0, 100:300:600, v/v/v for other 12 anti-obesity agents) while simultaneously performing DLLME. The developed method successfully detected 13 anti-obesity agents within 11 min, including bambuterol, clenbuterol, ractopamine, clorprenaline, formoterol, salbutamol, terbutaline, procaterol, phentermine, bupropion, sibutramine, desmethyl sibutramine, and N,N-didesmethyl sibutramine, which improved the screening efficiency. The calibration curves exhibited good linearity of 0.025–5 ng/mg, achieving correlation coefficients of r ≥ 0.99. The lower limits of quantification (LLOQs) for the analytes were 0.025 ng/mg, demonstrating acceptable levels of accuracy and precision. Recovery rates ranged between 73.30% and 107.47% across the three concentrations of 0.075, 0.375, and 3.75 ng/mg. The validated method was successfully applied to 369 real cases and detected six analytes, including bambuterol, salbutamol, terbutaline, sibutramine, desmethyl sibutramine, and N,N-didesmethyl sibutramine. This method offers several advantages, including simple pretreatment, high extraction efficiency, rapid extraction, solvent economy, and pollution mitigation, making it highly suitable for large-scale surveillance of usage of added anti-obesity agents.

Optimizing Microwave-Assisted Extraction from Levisticum officinale WDJ Koch Roots Using Pareto Optimal Solutions

The current research trend is not only focused on advanced techniques to intensify the extraction of bioactive compounds from plants, but also on the optimization process. The objective of this work was the implementation of the multiple criteria analysis using navigation on Pareto sets to determine the optimal parameters for the microwave-assisted extraction of Levisticum officinale WDJ Koch roots in order to obtain the maximum efficiency of the antioxidant potential of the extracts. The optimized parameters were extraction time, microwave power, and plant biomass/solvent ratio, while the evaluation criteria were based on the total phenols, flavonoids, reducing sugars, and antioxidant capacity. It was shown that the process parameters analyzed, i.e., biomass/solvent ratio, process time, and microwave power, determined the extraction efficiency of total phenolic content (TPC). A different observation was made for the analysis of total flavonoid content (TFC) and total antioxidant potential (TAA). Compared to the assessment of TFC and TAA, a completely different trend was observed for the analysis of total reducing sugars (RSC). Sets of Pareto optimal, compromise, and preferred solutions were identified that will maximize the efficiency of the extraction of bioactive compounds from biomass. Due to the determined number of Pareto optimal solutions, an approach related to the introduction of preferences in the optimization procedure was applied. It was shown that for a satisfactory level of bioactive compounds, extraction should be carried out at a maximum microwave power of 750 W. Preferred solutions were obtained for root biomass to water ratios ranging from 0.0536 g/mL to 0.0679 g/mL. The preferred optimal time for microwave-assisted water extraction ranged from 64.2857 to 85.7143 s.

Design of adaptive hybrid MPPT controllers with universal input voltage DC–DC converter for RES’s

At present, conventional energy production is absent because of the more hazardous gases released into the environment, the high effect on human health, more cost required for maintenance, plus less usefulness for highly populated areas. So, the Renewable Energy Sources are more focused for the present automotive industry application. In this work, the Proton Exchange Membrane Fuel Stack is considered for analyzing the proposed DC–DC converter circuit. The advantages of this fuel stack are high energy density, fast functioning nature, more robustness, and more usefulness for the various water membrane conditions of the fuel stack. However, the disadvantages of the fuel stack are excessive current generation, plus more current conduction losses. So, the wide voltage supply single switch power converter is introduced in this work for optimizing the current production of the fuel stack network. The merits of this converter circuit are high stability, good reliability, low voltage appearing across the switches, plus a uniform power supply. Here, the converter switching pulses are obtained by proposing the Modified Continuous Step Change Adaptive Fuzzy Logic with Grey Wolf Optimization hybrid controller. This controller provides high maximum power extraction efficiency from the fuel stack which is equal to 99.421%. Also, this controller's Maximum Power Point Tracking time is 0.0285 s.

Identification Methods for Modal Parameters of Track Structures and Their Application Status and Prospects

Rail transit’s wheel–rail system periodically encounters defects such as wheel polygons, rail corrugation, and rail fastener failure, which are intricately linked to the modal parameters of track structures. Identifying these modal parameters is essential for refining wheel–rail dynamics models, understanding track defect mechanisms, and defect detection. This study reviews the current methodologies for identifying track structure modal parameters, emphasizing their significance in track engineering. It categorizes various identification techniques, examines their development, and highlights their application in updating track dynamics theoretical models. The relationship between track modal parameters and wheel–rail defects is discussed, alongside a summary of modal parameter-based defect remediation strategies globally. The paper also evaluates the current state of defect identification research utilizing track modal parameters. In the “prospects” section, three forward-looking research avenues are proposed. These approaches are poised to streamline and improve the efficiency of modal parameter extraction, marking potential breakthroughs in the field.

Complete gold extraction and recovery from double refractory gold ores by thiourea after bio-oxidation

Cyanidation, a conventional process to extract gold from gold ores, has been used for over 130 years in industrial mining because of the high efficiency and rate of formation of Au(CN)2− and the high recovery efficiency by adsorption of Au(CN)2− on activated carbon. However, carbonaceous refractory gold ores are not targeted because Au(CN)2− is easily adsorbed on carbonaceous matter in the ores, resulting in high recovery loss. In this study, the flotation concentrates of a carbonaceous refractory gold ores was subjected to biooxidation at 45 °C using a mixed culture containing iron-oxidizing and sulfur-oxidizing bacteria, followed by gold extraction using thiourea under strongly acidic conditions. The gold extraction efficiency reached ∼100% in 12 h without re-adsorption. Finally, the quantitative recovery of the Au(CS(NH2)2)2+ complex was confirmed by adsorption on strongly cationic exchange resin. Biooxidation reduced the amount of Fe-containing metal sulfides, which minimized the decomposition of thiourea, and the Au(CS(NH2)2)2+ complex had a low affinity toward carbonaceous matter, different to Au(CN)2−. Since the process described in this study does not require roasting to remove carbonaceous materials in pretreatment and does not use cyanide for gold extraction, it is environmentally friendly and should be considered for practical applications in carbonaceous gold ore-producing mines.

Determination of Azole Fungicide Residues in Fresh Juice by Magnetic Solid Phase Extraction Based on Fe3O4@ZnAl-LDH@MIL-53(Al) Sorbent in Combination with High-Performance Liquid Chromatograph.

In this work, a magnetic adsorption material based on metal-organic framework (Fe3O4@ZnAl-LDH@MIL-53(Al)) was synthesized and used as an adsorbent in the process of magnetic solid phase extraction. Then, a high-performance liquid chromatograph was used to quantitatively detect triazole fungicides in samples. In order to verify the successful preparation of the material, a series of characterization analyses were carried out. Besides, the key parameters that may affect the extraction efficiency have been optimized, and under optimal conditions the three triazole fungicides showed good linearity in the range of 10-1000μg/L (R2 ≥ 0.9796); Limit of detections were ranged from 0.013 to 0.030μg/mL. Finally, the established method was applied to the detection of triazole fungicides in four fresh juice samples. The results showed that the target analyte was not detected in all the test samples. By detecting the recoveries (73.3-104.3%) and coefficient variation (RSD ≤ 6.8%) of triazole fungicides in fortified samples, it proved that this established method meets the requirements of pesticide residue analysis and showed excellent application potential.

Ion pair sites for efficient electrochemical extraction of uranium in real nuclear wastewater

Electrochemical uranium extraction from nuclear wastewater represents an emerging strategy for recycling uranium resources. However, in nuclear fuel production which generates the majority of uranium-containing nuclear wastewater, fluoride ion (F−) co-exists with uranyl (UO22+), resulting in the complex species of UO2Fx and thus decreasing extraction efficiency. Herein, we construct Tiδ+-PO43− ion pair extraction sites in Ti(OH)PO4 for efficient electrochemical uranium extraction in wastewater from nuclear fuel production. These sites selectively bind with UO2Fx through the combined Ti-F and multiple O-U-O bonds. In the uranium extraction, the uranium species undergo a crystalline transition from U3O7 to K3UO2F5. In real nuclear wastewater, the uranium is electrochemically extracted with a high efficiency of 99.6% and finally purified as uranium oxide powder, corresponding to an extraction capacity of 6829 mg g−1 without saturation. This work paves an efficient way for electrochemical uranium recycling in real wastewater of nuclear production.

Nutritional and Medicinal Properties of Microbial Oil

Plant and animal oils and fats currently dominate the edible oil market, but a new sustainable alternative of lipids from single-celled organisms has become advantageous in human nutrition and pharmacy. Single-cell oils (SCOs) are lipids biosynthesized and accumulated in the lipid bodies of oleaginous species of bacteria, yeasts, molds, and algae. The review has investigated SCOs’ composition, with a detailed review of the described beneficial impact in medicine, cosmetics, pharmacy, and nutrition. Although microbial oil has been known for more than 100 years, it was not applied until the 21st century, when commercial SCO production for human use started and administrative regulations governing their use were completed. This article discusses the applications of SCOs, which can be easily found in microorganisms, in the pharmaceutical, cosmetic, and food industries. In addition, some aspects of 15- or 17-carbon-atom-long fatty acids were also pointed out. Furthermore, some challenges for heterotrophic single-cell oil synthesis and improvements in its extraction efficiency have also been concluded, which can further contribute to their broadened use in pharmacy, medicine, cosmetics, and food applications.

A Transformer and LSTM-Based Approach for Blind Well Lithology Prediction

Petrographic prediction is crucial in identifying target areas and understanding reservoir lithology in oil and gas exploration. Traditional logging methods often rely on manual interpretation and experiential judgment, which can introduce subjectivity and constraints due to data quality and geological variability. To enhance the precision and efficacy of lithology prediction, this study employed a Savitzky–Golay filter with a symmetric window for anomaly data processing, coupled with a residual temporal convolutional network (ResTCN) model tasked with completing missing logging data segments. A comparative analysis against the support vector regression and random forest regression model revealed that the ResTCN achieves the smallest MAE, at 0.030, and the highest coefficient of determination, at 0.716, which are indicative of its proximity to the ground truth. These methodologies significantly enhance the quality of the training data. Subsequently, a Transformer–long short-term memory (T-LS) model was applied to identify and classify the lithology of unexplored wells. The input layer of the Transformer model follows an embedding-like principle for data preprocessing, while the encoding block encompasses multi-head attention, Add & Norm, and feedforward components, integrating the multi-head attention mechanism. The output layer interfaces with the LSTM layer through dropout. A performance evaluation of the T-LS model against established rocky prediction techniques such as logistic regression, k-nearest neighbor, and random forest demonstrated its superior identification and classification capabilities. Specifically, the T-LS model achieved a precision of 0.88 and a recall of 0.89 across nine distinct lithology features. A Shapley analysis of the T-LS model underscored the utility of amalgamating multiple logging data sources for lithology classification predictions. This advancement partially addresses the challenges associated with imprecise predictions and limited generalization abilities inherent in traditional machine learning and deep learning models applied to lithology identification, and it also helps to optimize oil and gas exploration and development strategies and improve the efficiency of resource extraction.

A Deep Learning-based Algorithm for Intelligent Prediction of Adverse Geologic Bodies in Tunnels

Abstract Aiming at the issues of high subjectivity and low efficiency in the image analysis methods for overcast prediction of tunnel adverse geological bodies, a deep learning-based intelligent prediction algorithm, namely YOLO-SEI (YOLOv8 enhanced by Sim-EFFcinetNet and Interlaced Sparse Self-Attention), is proposed in this paper. Firstly, Sim-EfficientNet with good feature extraction performance and efficiency is proposed as the backbone of YOLOv8 by fusing the SimAM attention and the EfficientNet-v2 network, which improves the model's extraction capability for radar wave features of adverse geologic bodies. Then, a feature fusion module enhanced by Interlaced Sparse Self-Attention is designed to effectively make up for the deficiency of convolutional neural network that is difficult to fully extract the global information of radar images. The experimental results show that the mAP and F1 of YOLO-SEI are 84.87% and 82.28%, respectively, which are higher than other commonly used deep learning models. In addition, YOLO-SEI has smaller storage space (41MB) and faster image processing speed (41.24 f/s), which is suitble for the rapid measurement and prediction of adverse geologic bodies in tunnel excavation construction.

Gershgorin circle theorem-based feature extraction for biomedical signal analysis

Recently, graph theory has become a promising tool for biomedical signal analysis, wherein the signals are transformed into a graph network and represented as either adjacency or Laplacian matrices. However, as the size of the time series increases, the dimensions of transformed matrices also expand, leading to a significant rise in computational demand for analysis. Therefore, there is a critical need for efficient feature extraction methods demanding low computational time. This paper introduces a new feature extraction technique based on the Gershgorin Circle theorem applied to biomedical signals, termed Gershgorin Circle Feature Extraction (GCFE). The study makes use of two publicly available datasets: one including synthetic neural recordings, and the other consisting of EEG seizure data. In addition, the efficacy of GCFE is compared with two distinct visibility graphs and tested against seven other feature extraction methods. In the GCFE method, the features are extracted from a special modified weighted Laplacian matrix from the visibility graphs. This method was applied to classify three different types of neural spikes from one dataset, and to distinguish between seizure and non-seizure events in another. The application of GCFE resulted in superior performance when compared to seven other algorithms, achieving a positive average accuracy difference of 2.67% across all experimental datasets. This indicates that GCFE consistently outperformed the other methods in terms of accuracy. Furthermore, the GCFE method was more computationally-efficient than the other feature extraction techniques. The GCFE method can also be employed in real-time biomedical signal classification where the visibility graphs are utilized such as EKG signal classification.

On the origin for the enhanced light extraction efficiency of DUV LED by using inclined sidewalls

On the origin for the enhanced light extraction efficiency of DUV LED by using inclined sidewalls

European Climate Policy in the Context of the Problem of Methane Emissions from Coal Mines in Poland

This paper presents a thorough examination of methane capture from Polish coal mines, contextualized within the framework of the European Union’s (EU) climate policy objectives. Through a strategic analysis encompassing the interior of coal mines, the surrounding environment, and the macro environment, this study elucidates the complex dynamics involved in methane emissions and capture initiatives. The key findings include a declining trend in absolute methane emissions since 2008, despite fluctuations in coal extraction volumes, and a relatively stable level of methane capture exceeding 300 million m3/year since 2014. The analysis underscores the critical role of government support, both in terms of financial incentives and streamlined regulatory processes, to facilitate the integration of methane capture technologies into coal mining operations. Collaboration through partnerships and stakeholder engagement emerges as essential for overcoming resource competition and ensuring the long-term success of methane capture projects. This paper also highlights the economic and environmental opportunities presented by methane reserves, emphasizing the importance of investment in efficient extraction technologies. Despite these advancements, challenges persist, particularly regarding the low efficiency of current de-methanation technologies. Recommendations for modernization and technological innovation are proposed to enhance methane capture efficiency and utilization.

Early exploration modelling of natural hydrogen systems through the use of existing open source data

Natural hydrogen’s viability as a sustainable energy source will be enhanced through comprehensive geological modelling. This paper integrates existing open-source data to delve into the geological aspects of natural hydrogen exploration and proposes possible workflows. Geological and geophysical modelling entails characterising subsurface formations conducive to natural hydrogen generation and trapping. Utilising geological surveys, field observation, geophysical seismic and gravity data, alongside existing well logs, this analysis looks to identify regions with favourable geological conditions for the generation, migration and structures for accumulation of natural hydrogen. Furthermore, understanding the subsurface geology aids in the development of safe and efficient extraction techniques. By incorporating geological modelling into the evaluation of natural hydrogen, this paper provides a comprehensive overview of its potential as a sustainable energy solution. Leveraging existing open-source data alongside geological insights ensures a robust foundation for decision-making in exploration, production, storage, and utilisation strategies. This integrated approach empowers stakeholders to make informed choices in shaping a greener, more sustainable energy landscape.