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A new finite multi-layer model coupled with real gas law is successfully established using statistical physics theory and applied to theoretically characterize the docking process of vanillin key food odorant on human hOR8H1, chimpanzee cOR8H1, and horse hoOR8H1 olfactory receptors. To deeply comprehend and analyze the mechanism of adsorption involved in the sense of smell, stereographic, van der Waals, and energetic metrics are interpreted. Indeed, modeling findings reveal that the vanillin molecules are non-parallelly docked on the binding sites of the three mammalian olfactory receptors. The van der Waals parameters serve as valuable tools for assessing the stability of the formed complexes during adsorption. The molar adsorption energy values, ranging from 18.26 to 20.75 kJ/mol, suggest that the vanillin molecules are exothermically-physisorbed on hOR8H1, cOR8H1, and hoOR8H1. In addition, the energetic parameter may also be deployed to quantitatively characterize the interactions between the vanillin molecules and the three mammalian receptors.

The emergence of Digital Twins (DT) represents a significant technological innovation aimed at enhancing the design, construction, and operation of built assets. DT are virtual representations of physical objects, allowing an effective management across various stages within the construction and infrastructure sectors. While DT technology offers considerable potential, it remains in its infancy and faces challenges such as integration with other Industry 4.0 technologies and uncertainties about their actual uses and applications still exist. This paper seeks to define and explore the key characteristics of DT within the built environment, examine their relationship with Building Information Modeling (BIM), and evaluate their interactions with other Industry 4.0 technologies in the construction and maintenance of assets. Despite these potential benefits, several challenges prevent the widespread adoption of DT technology such as data security and ownership concerns, the lack of common data standards and interoperability, and difficulties in managing diverse source systems.

This paper presented the preparation, characterization, and adsorption properties of Brazil nut shell activated carbon for catechol removal from aqueous solutions. The equilibrium adsorption of catechol molecules on this activated was experimentally quantified at pH 6 and temperatures ranging from 25 to 55 °C, and at 25 °C and pH ranging from 6 to 10. These results were utilized to elucidate the role of surface functionalities through statistical physics calculations. All these experimental adsorption isotherms were fitted and interpreted via a monolayer model with one energy, which was chosen as the optimal model. Model physicochemical parameters, which may be categorized as stereographic parameters such as the maximum adsorbed quantity ( Q M ), the number of adsorbed catechol molecules per one Brazil nut shell activated carbon binding site ( n ), and the number of effectively occupied binding sites ( N M ) and energetic parameter such as the half saturation concentration ( C HS ), were analyzed. Microscopically speaking, these modeling results were employed to stereographically and energetically investigate the phenol derivative adsorption mechanism. The maximum catechol adsorbed quantities on this activated carbon ranged from 89.98 to 103.16 mg/g under the tested operating conditions. The adsorption of catechol molecules was found to be exothermic where the maximum adsorbed quantity augmented with solution temperature and the maximum adsorption efficiency was found 103.16 mg/g at 55 °C. In addition, it was found that the catechol molecules were adsorbed with nonparallel orientations on the activated carbon adsorbent since the numbers of catechol molecules per site were superior to 1 (1.10 <  n  < 1.86). Moreover, the calculated molar adsorption energies, which varied between 19.04 and 22.37 kJ/mol, showed exothermic ( ΔE  > 0) and physical ( ΔE  < 40 kJ/mol) adsorption process involving hydrogen bonds, π-π interactions, electron donor-acceptor interactions, and dispersion forces. Finally, the tested adsorbent exhibited unimodal pore size and site energy distributions with peaks centered at pore radius ranging from 2.26 to 2.68 nm, and at adsorption energy ranging from 20.01 to 23.78 kJ/mol, respectively. Macroscopically speaking, three thermodynamic potentials, including the adsorption entropy, internal energy of adsorption, and Gibbs free energy, suggested that the adsorption of catechol on Brazil nut shell activated carbon was a spontaneous and exothermic mechanism.

Conventional energy networks produce energy with less efficiency. Also, these source’s development costs and size are more. So, the world is focusing on renewable energy networks for energy production to the consumer. In this work, a proton exchange membrane fuel stack (PEMFS) technology is selected for energy feeding to the hydrogen vehicle. The merits of this stack are more abundant, faster fuel stack operational response, and more efficient for electrical automotive networks. However, the fuel stack’s energy production is nonlinear and its operational point varies concerning the fuel stack device operating temperature. The particle swarm optimized adaptive network‐based fuzzy inference system (PSO‐ANFIS) is proposed in this work to find the operational point of the fuel cell network. The features of this hybrid methodology are the low number of iteration values required, low convergence time, low‐level dependence on the fuel stack, and high compliance for the quick deviations of the fuel system temperature. The operating efficiency and tracking time of the proposed maximum power point tracking (MPPT) controller are 95.60% and 0.1089 s. Another issue of the fuel cell is high output current generation and less voltage production. This condition is happening in the fuel cell because of its chemical reaction dynamics, internal resistance of the cell, and electrochemical potential. Due to this excess current flow in the fuel cell, the direct fuel stack‐fed electrical networks face the issue of high power conduction losses. To reduce the power conduction losses of the system, a single‐switch power circuit is used to reduce fuel source current, thereby optimizing the excessive power losses of the system. The whole fuel stack energy production network is analyzed by selecting the MATLAB Window.

The present power generation corporations are working on Renewable Power Systems (RPS) for supplying electrical power to the automotive power industries. There are several categories of RPSs available in the atmosphere. Among all of the RPSs, the most general power network used for Electric Vehicles (EVs) is hydrogen fuel which is available in nature. The H 2 fuel is fed to the Proton Exchange Membrane Fuel Stack (PEMFS) for producing electricity for the EV stations. The advantages of this selected fuel system are more power conversion efficiency, environmentally friendly, low carbon emissions, more power density, less starting time, plus able to work at very low-temperature values. However, this fuel stack faces the issue of a nonlinear power density curve. Due to this nonlinear power supply from the fuel stack, the functioning point of the overall network changes from one position of the I–V curve to another position. So, the peak voltage extraction from the fuel stack is not possible. In this article, there are various metaheuristic optimization-based Maximum Power Point Tracking (MPPT) methodologies are studied along with the conventional methods for obtaining the Maximum Power Point (MPP) position of the PEMFS. From the simulative investigation, the Continuous Different Slope Value-based Cuckoo Search Method (CDSV with CSM) provides better efficiency with more output power. Also, for all the MPPT methods comprehensive analysis has been made by utilizing the simulation results.

A biocomposite composed of chitosan and lignin was synthesized for the removal of dyes and metals from aqueous solutions. The structural and surface properties of the adsorbent were characterized using FT-IR spectroscopy, SEM micrograph, X-ray diffraction, nitrogen adsorption-desorption isotherms, BJH pore size distribution, and zeta potential evolution. This study also presented a physicochemical investigation of the adsorption mechanism of reactive orange 16 (RO16) dye and hexavalent chromium (Cr(VI)) ions on chitosan-lignin biocomposite, using both experimental adsorption data and theoretical modeling based on statistical physics theory to elucidate the underlying interactions. An advanced statistical physics adsorption model, namely heterogeneous monolayer model with two functional groups (HMLM2FG), was employed to simulate the adsorption behavior, indicating that RO16 and Cr(VI) interacted with two distinct functional groups on the chitosan + 50% lignin surface. This model enabled detailed stereographic and energetic studies of the tested adsorption systems. Hence, stereographic analysis revealed that the studied adsorbent functional groups preferentially capture the attached species with n  > 1 at specific temperatures, suggesting a multi-ionic mechanism with significant aggregation. The total maximum adsorbed quantities of chitosan-lignin adsorbent, determined by the proposed model HMLM2FG, were found to be 59.43–79.76 mg/g for RO16 and 52.06–72.61 mg/g for Cr(VI). Chitosan + 50% lignin demonstrated then greater efficiency in removing RO16 and Cr(VI) from aqueous solutions, showing an exothermic adsorption process characterized by adsorption energies ranging from 4.88 to 16.97 kJ/mol. These energy values were consistent with physisorption mechanisms. Overall, this study combined experimental findings with a theoretical approach to offer a novel microscopic and macroscopic analysis of the adsorption behavior of two major industrial pollutants on chitosan-lignin biocomposite. Clinical trial number : Not applicable.