cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.26972633.v1
title: Biomass chemical exergy estimation using novel intelligent tool: application to biorefinery sustainability

description: In response to the growing importance of sustainable energy and environmental considerations in biorefinery technologies, this study introduces a novel hybrid artificial intelligence approach for predicting biomass chemical exergy. The proposed least-squares support vector machine method based on an imperialist competitive algorithm (ICA-LSSVM) combines composition and energy analysis methods to model the chemical energy of biomass accurately. Extensive data were collected to train the model, resulting in a high-precision prediction with a coefficient of determination (R<sup>2</sup>) of 0.999 and a root mean square error (RMSE) of 30.3. The main point of this study is the development of this accurate model to determine the chemical energy, and also different forms of comparison are used to prove this issue. The sensitivity analysis highlights the direct impact of carbon (C%), hydrogen (H%), and oxygen (O%) percentages on biomass chemical exergy, while nitrogen (N%) and sulfur (S%) exhibit an inverse relationship. The model outperforms existing correlations in the literature, emphasizing its accuracy and practicality for biomass chemical exergy prediction, and contributing to both laboratory and industrial applications. The study emphasizes the model’s real-data reliance and suggests further exploration with an expanded dataset for enhanced industrial applicability. This article extensively delves into the modeling and prediction of biomass chemical exergy using the ICA-LSSVM approach.A new sensitivity analysis has been done on the input variables to show their impact on this target.The study emphasizes the model’s real-data reliance and suggests further exploration with an expanded dataset for enhanced industrial applicability. This article extensively delves into the modeling and prediction of biomass chemical exergy using the ICA-LSSVM approach. A new sensitivity analysis has been done on the input variables to show their impact on this target. The study emphasizes the model’s real-data reliance and suggests further exploration with an expanded dataset for enhanced industrial applicability.
DOI: 10.6084/m9.figshare.27926005
title: Kinetic characterization of CO<sub>2</sub> gasification for coal-char: A case study

description: Gasification of coal under CO<sub>2</sub> environment is an attractive option for mitigating CO<sub>2</sub> emissions. Kinetic characterization of coal-char is beneficial for understanding its performance during gasification processes in practical systems. India has 4<sup>th</sup> largest coal reserve in the world; however, very few studies have been conducted on Indian coal for assessing thermal as well as kinetic behavior during CO<sub>2</sub> gasification. Therefore, the present study aims to assess the reaction kinetics during CO<sub>2</sub> gasification of coal-char from Talcher, which has the largest coal reserve among Indian coalfields, using isothermal thermogravimetric analysis at 900–1050°C. The studied coals are of sub-bituminous to high volatile bituminous. It has moderately high ash yield (15–31%) and &gt; 68% of reactive macerals. Specific surface area of char gradually increases from 2.83 m<sup>2</sup>/g to 42.71 m<sup>2</sup>/g with increase in temperature. Furthermore, the char surface area shows a strong correlation with the reactivity index. The kinetic parameters are determined by using first-order and nth-order kinetic models, namely, volumetric, shrinking core, and random pore models. The activation energy (E<sub>a</sub>) shows a range of 118–194 kJ/mol and 162–213 kJ/mol for first order and nth-order models, respectively. The temperature significantly influences the reaction rate, suggesting a chemical reaction control zone for the studied temperature range. This study also highlights the morphological evolution of char pore structure with temperature.
DOI: 10.6084/m9.figshare.27926005.v1
title: Kinetic characterization of CO<sub>2</sub> gasification for coal-char: A case study

description: Gasification of coal under CO<sub>2</sub> environment is an attractive option for mitigating CO<sub>2</sub> emissions. Kinetic characterization of coal-char is beneficial for understanding its performance during gasification processes in practical systems. India has 4<sup>th</sup> largest coal reserve in the world; however, very few studies have been conducted on Indian coal for assessing thermal as well as kinetic behavior during CO<sub>2</sub> gasification. Therefore, the present study aims to assess the reaction kinetics during CO<sub>2</sub> gasification of coal-char from Talcher, which has the largest coal reserve among Indian coalfields, using isothermal thermogravimetric analysis at 900–1050°C. The studied coals are of sub-bituminous to high volatile bituminous. It has moderately high ash yield (15–31%) and &gt; 68% of reactive macerals. Specific surface area of char gradually increases from 2.83 m<sup>2</sup>/g to 42.71 m<sup>2</sup>/g with increase in temperature. Furthermore, the char surface area shows a strong correlation with the reactivity index. The kinetic parameters are determined by using first-order and nth-order kinetic models, namely, volumetric, shrinking core, and random pore models. The activation energy (E<sub>a</sub>) shows a range of 118–194 kJ/mol and 162–213 kJ/mol for first order and nth-order models, respectively. The temperature significantly influences the reaction rate, suggesting a chemical reaction control zone for the studied temperature range. This study also highlights the morphological evolution of char pore structure with temperature.
DOI: 10.6084/m9.figshare.27926090
title: Study on improving self-cleaning performance of hydrophobic coating surface through freeze-thaw cycles

description: This study employed a freeze-thaw cycle visualization platform alongside CCD imaging technology to conduct microscopic investigations into the freeze-thaw process under typical conditions. The observations identified three distinct stages in the freeze-thaw cycle: freezing, melting and disintegration, and melting and movement. Additionally, the experiment analyzed the particle size and composition of adhesive dust, as well as the effects of freezing temperature, freezing time, and thawing temperature on the contact angle between a hydrophobic surface and dust particles. Under a combination of influencing factors (freezing temperature of −8°C, freezing time of 60 minutes, melting temperature of 40°C, particle size of 800 mesh, and an impurity ratio of 1:1:1), the self-cleaning performance of the hydrophobic surface was significantly enhanced – the contact angle increased by 39.67% compared to standard surfaces. The maximum dust removal efficiency achieved was 37.52% in a single freeze-thaw cycle on hydrophobic surfaces. These findings present a novel approach to improving hydrophobic surface self-cleaning technology, with promising practical applications.
DOI: 10.6084/m9.figshare.27926090.v1
title: Study on improving self-cleaning performance of hydrophobic coating surface through freeze-thaw cycles

description: This study employed a freeze-thaw cycle visualization platform alongside CCD imaging technology to conduct microscopic investigations into the freeze-thaw process under typical conditions. The observations identified three distinct stages in the freeze-thaw cycle: freezing, melting and disintegration, and melting and movement. Additionally, the experiment analyzed the particle size and composition of adhesive dust, as well as the effects of freezing temperature, freezing time, and thawing temperature on the contact angle between a hydrophobic surface and dust particles. Under a combination of influencing factors (freezing temperature of −8°C, freezing time of 60 minutes, melting temperature of 40°C, particle size of 800 mesh, and an impurity ratio of 1:1:1), the self-cleaning performance of the hydrophobic surface was significantly enhanced – the contact angle increased by 39.67% compared to standard surfaces. The maximum dust removal efficiency achieved was 37.52% in a single freeze-thaw cycle on hydrophobic surfaces. These findings present a novel approach to improving hydrophobic surface self-cleaning technology, with promising practical applications.
DOI: 10.34770/7dbr-jd83
title: Data for "Initial Assessment of 3D-Printed Molybdenum Lattices as a Substrate for Liquid Metal Plasma-Facing Components"

description: This repository provides the data which is plotted in the figures of the
 article titled "Initial Assessment of 3D-Printed Molybdenum Lattices
 as a Substrate for Liquid Metal Plasma-Facing Components", submitted
 to the journal Fusion Science and Technology in 2025. The files include
 compression test measurements, x-ray photoelectron spectroscopy spectra,
 energy-dispersive x-ray spectroscopy measurements, and video of lithium
 wetting tests.
DOI: 10.6084/m9.figshare.26527666
title: A novel Adaptive Jellyfish Search algorithm (AJFS) for maximum power optimization of photovoltaic systems: performance evaluation and analysis

description: In solar photovoltaic (SPV) systems, optimizing output under various meteorological conditions relies on the Maximum Power Point (MPP) tracking controller. However, the presence of multiple peaks due to partial shading complicates this tracking process. While traditional and soft computing methods are commonly employed for MPP tracking, they face limitations such as the fixed step size in conventional methods and a lack of randomness in soft computing approaches once they reach a certain MPP. To address these challenges, a unique optimization technique known as the Adaptive Jellyfish Search (AJFS) method has been proposed. This method conducts both global and local searches simultaneously in a single step, enhancing the effectiveness of MPP tracking. To evaluate its performance and resilience, zero, weak, moderate, and strong shading patterns are employed in simulations, with comparisons made against traditional Jellyfish Search (JFS) and Particle Swarm Optimization (PSO) techniques. The newly suggested AJFS approach demonstrates significant improvements over traditional methods. It reduces convergence time by 49% and offers additional motivating features, including zero risk of failure, minimized oscillation in power parameters, and enhanced energy production by 1.5%. Moreover, it enables smooth tracking of the MPP, particularly in dynamically changing shading patterns. Overall, the AJFS method presents a promising solution for efficient MPP tracking in SPV systems, overcoming limitations of conventional approaches and demonstrating superior performance under various shading conditions. Its ability to simultaneously conduct global and local searches in a single step makes it well suited for optimizing energy production in real-world scenarios.
DOI: 10.6084/m9.figshare.26527666.v1
title: A novel Adaptive Jellyfish Search algorithm (AJFS) for maximum power optimization of photovoltaic systems: performance evaluation and analysis

description: In solar photovoltaic (SPV) systems, optimizing output under various meteorological conditions relies on the Maximum Power Point (MPP) tracking controller. However, the presence of multiple peaks due to partial shading complicates this tracking process. While traditional and soft computing methods are commonly employed for MPP tracking, they face limitations such as the fixed step size in conventional methods and a lack of randomness in soft computing approaches once they reach a certain MPP. To address these challenges, a unique optimization technique known as the Adaptive Jellyfish Search (AJFS) method has been proposed. This method conducts both global and local searches simultaneously in a single step, enhancing the effectiveness of MPP tracking. To evaluate its performance and resilience, zero, weak, moderate, and strong shading patterns are employed in simulations, with comparisons made against traditional Jellyfish Search (JFS) and Particle Swarm Optimization (PSO) techniques. The newly suggested AJFS approach demonstrates significant improvements over traditional methods. It reduces convergence time by 49% and offers additional motivating features, including zero risk of failure, minimized oscillation in power parameters, and enhanced energy production by 1.5%. Moreover, it enables smooth tracking of the MPP, particularly in dynamically changing shading patterns. Overall, the AJFS method presents a promising solution for efficient MPP tracking in SPV systems, overcoming limitations of conventional approaches and demonstrating superior performance under various shading conditions. Its ability to simultaneously conduct global and local searches in a single step makes it well suited for optimizing energy production in real-world scenarios.
DOI: 10.6084/m9.figshare.26268590
title: Power quality advancement of grid-tied photovoltaic system: using optimal phase locked loop based control strategy

description: This manuscript proposes an optimal phase locked loop (PLL) based control method for voltage source inverter (VSI) of grid-tied solar photovoltaic (PV) system. The proposed work in this manuscript is an extended version of the paper presented in IEEE ICEPE-2020 conference, in which a sliding discrete Fourier transform (SDFT) and moving average filter (MAF) are resorted for the extraction of fundamental grid frequency and phase angle. Through extensive simulations and hardware experiments, it is demonstrated that the proposed method achieves a phase angle tracking accuracy of ± 0.05 deg and a grid frequency tracking error of less than 0.01 Hz under varying grid conditions. The adaptive and preferable tracking of phase angle as well as grid frequency ensures the generation of reference grid currents properly, which enhances the novelty of proposed work. Additionally, the JAYA optimization is used to extract the proportional and integral (PI) gain of PLL’s phase detector, resulting in approximately 90% reduction in settling time and 60% improvement in steady-state error compared to conventional methods. To minimize the overshoot and undershoot from DC-link voltage during loading condition, the current gain of an outer DC-link voltage control loop was added with modified quadrature axis current gain, resulting in a 30% reduction in transient voltage deviations. Further, numerous operational VSI with proper control retains the power management capability to set the seal on consumers requirement and mitigates the power quality (PQ) issues. Finally, with the help of MATLAB/Simulink studies and dSPACE-1104-based real time hardware setup, the accomplishment of proposed work is well validated.
DOI: 10.6084/m9.figshare.27898502
title: Energy, exergy, exergoeconomic, and environmental (4E) analyses of a combined system comprising reformed methanol high-temperature proton exchange membrane fuel cells and absorption refrigeration cycle

description: 4E (energy, exergy, exergoeconomic, and environmental) analyses of a reformed methanol high-temperature proton exchange membrane fuel cell (RM HT-PEMFC) system identify inefficiencies, assess associated costs, and evaluate environmental impacts. However, such studies remain limited in the literature, particularly those exploring the impact of the operating parameters of the methanol steam reforming in the combined RM HT-PEMFC and single-effect absorption refrigeration cycle (ARC). This study addresses this gap by analyzing a tri-generation consisting of the RM HT-PEMFC and the single-effect ARC. Simulation results indicate a total exergy destruction of 21.65 kW, a total cost rate of 8.93 $/h, an exergoeconomic factor of 43.22%, and an exergy efficiency of 33.06% in the baseline case. Notably, the stack and burner account for the highest irreversibility, contributing 67.75% of total exergy destruction and 55.71% of the total cost rate. Parametric studies on four key variables – current density, stack temperature, reformer temperature, and steam-to-carbon ratio – reveal that higher system exergy efficiency is generally associated with lower carbon dioxide emissions. Uncertainty analysis shows that extending the HT-PEMFC’s lifespan to 40,000 h can reduce exergy cost per unit product by 16.23%, while decreasing the price of green methanol to 11.00 $/h can lower costs by 26.97%.
DOI: 10.6084/m9.figshare.27898502.v1
title: Energy, exergy, exergoeconomic, and environmental (4E) analyses of a combined system comprising reformed methanol high-temperature proton exchange membrane fuel cells and absorption refrigeration cycle

description: 4E (energy, exergy, exergoeconomic, and environmental) analyses of a reformed methanol high-temperature proton exchange membrane fuel cell (RM HT-PEMFC) system identify inefficiencies, assess associated costs, and evaluate environmental impacts. However, such studies remain limited in the literature, particularly those exploring the impact of the operating parameters of the methanol steam reforming in the combined RM HT-PEMFC and single-effect absorption refrigeration cycle (ARC). This study addresses this gap by analyzing a tri-generation consisting of the RM HT-PEMFC and the single-effect ARC. Simulation results indicate a total exergy destruction of 21.65 kW, a total cost rate of 8.93 $/h, an exergoeconomic factor of 43.22%, and an exergy efficiency of 33.06% in the baseline case. Notably, the stack and burner account for the highest irreversibility, contributing 67.75% of total exergy destruction and 55.71% of the total cost rate. Parametric studies on four key variables – current density, stack temperature, reformer temperature, and steam-to-carbon ratio – reveal that higher system exergy efficiency is generally associated with lower carbon dioxide emissions. Uncertainty analysis shows that extending the HT-PEMFC’s lifespan to 40,000 h can reduce exergy cost per unit product by 16.23%, while decreasing the price of green methanol to 11.00 $/h can lower costs by 26.97%.
DOI: 10.6084/m9.figshare.26268590.v1
title: Power quality advancement of grid-tied photovoltaic system: using optimal phase locked loop based control strategy

description: This manuscript proposes an optimal phase locked loop (PLL) based control method for voltage source inverter (VSI) of grid-tied solar photovoltaic (PV) system. The proposed work in this manuscript is an extended version of the paper presented in IEEE ICEPE-2020 conference, in which a sliding discrete Fourier transform (SDFT) and moving average filter (MAF) are resorted for the extraction of fundamental grid frequency and phase angle. Through extensive simulations and hardware experiments, it is demonstrated that the proposed method achieves a phase angle tracking accuracy of ± 0.05 deg and a grid frequency tracking error of less than 0.01 Hz under varying grid conditions. The adaptive and preferable tracking of phase angle as well as grid frequency ensures the generation of reference grid currents properly, which enhances the novelty of proposed work. Additionally, the JAYA optimization is used to extract the proportional and integral (PI) gain of PLL’s phase detector, resulting in approximately 90% reduction in settling time and 60% improvement in steady-state error compared to conventional methods. To minimize the overshoot and undershoot from DC-link voltage during loading condition, the current gain of an outer DC-link voltage control loop was added with modified quadrature axis current gain, resulting in a 30% reduction in transient voltage deviations. Further, numerous operational VSI with proper control retains the power management capability to set the seal on consumers requirement and mitigates the power quality (PQ) issues. Finally, with the help of MATLAB/Simulink studies and dSPACE-1104-based real time hardware setup, the accomplishment of proposed work is well validated.
DOI: 10.5281/zenodo.18601173
title: INFLAMMATION IS A PROTECTIVE REACTION OF THE BODY

description: This article discusses inflammation as an important protective-adaptive reaction of the body that develops in response to harmful factors (infection, mechanical damage, chemical and physical effects). The main stages of the inflammatory process, pathophysiological mechanisms, and the role of cellular and humoral factors are analyzed. Also, the positive and negative aspects of inflammation, acute and chronic forms, and their clinical significance are considered. The article serves as a theoretical basis for medical workers and students to better understand the inflammatory process and correctly assess it.
DOI: 10.5281/zenodo.18601172
title: INFLAMMATION IS A PROTECTIVE REACTION OF THE BODY

description: This article discusses inflammation as an important protective-adaptive reaction of the body that develops in response to harmful factors (infection, mechanical damage, chemical and physical effects). The main stages of the inflammatory process, pathophysiological mechanisms, and the role of cellular and humoral factors are analyzed. Also, the positive and negative aspects of inflammation, acute and chronic forms, and their clinical significance are considered. The article serves as a theoretical basis for medical workers and students to better understand the inflammatory process and correctly assess it.
DOI: 10.6084/m9.figshare.27620282
title: Assessment of the use of biochar from the slow pyrolysis of walnut and almond shells as an energy carrier

description: This study explores the energy applications and combustion kinetics of biochar produced from walnut and almond shells through pyrolysis. Using macro-thermogravimetric analysis at heating rates of 10, 15, and 20 K min<sup>−1</sup>, a multi-step mechanism involving two parallel reactions was used to model thermal decomposition. Bioenergy indices revealed that walnut shell-based biochar (WSB) produced at 773 K and 873 K displayed superior biofuel potential due to its high energy density and low ash content, while almond shell-based biochar (ASB) at 673 K demonstrated the best overall bioenergy performance. Among the tested heating rates, 15 K min<sup>−1</sup> provided optimal results for ignition, combustion, and burnout indices, with WSB showing the highest overall combustion performance. Kinetic analysis using the Coats-Redfern method demonstrated the highest R<sup>2</sup> values and minimized RMSE and SSE. The activation energy for the first pseudo-component ranged from 54.91 to 129.84 kJ mol<sup>−1</sup> for ASB and from 61.71 to 141.85 kJ mol<sup>−1</sup> for WSB. For the second pseudo-component, the activation energy ranged from 25.21 to 159.32 kJ mol<sup>−1</sup> for ASB and 25.63 to 96.25 kJ mol<sup>−1</sup> for WSB. These findings emphasize the potential of WSB and ASB biochar for bioenergy applications, with WSB exhibiting the best combustion characteristics.