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Noncovalent Catalysis of Renewable Chitosan Biopolymer for the Conversion of CO(2) to Green Urea for Sustainable Agriculture.

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Noncovalent Catalysis of Renewable Chitosan Biopolymer for the Conversion of CO2 to Green Urea for Sustainable Agriculture - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice ACS Phys Chem Au . 2026 Apr 29;6(3):527–533. doi: 10.1021/acsphyschemau.6c00008 Noncovalent Catalysis of Renewable Chitosan Biopolymer for the Conversion of CO 2 to Green Urea for Sustainable Agriculture Abdul Hamid Malhan † , Rudharachari Parameshwari † , Krishnan Thirumoorthy †, ‡, * Author information Article notes Copyright and License information † Department of Chemistry, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu 632 014, India ‡ School of Computer Science and Engineering, Vellore Institute of Technology, Vellore. Tamil Nadu 632 014, India * Email: kthirumoorthy@gmail.com ; thirumoorthy.krishnan@vit.ac.in . Received 2026 Jan 20; Accepted 2026 Mar 17; Revised 2026 Mar 17; Collection date 2026 May 27. © 2026 The Authors. Published by American Chemical Society This article is licensed under CC-BY-NC-ND 4.0 PMC Copyright notice PMCID: PMC13220198  PMID: 42221932 Abstract The present study employs density functional theory to investigate the noncovalent catalysis by chitosan for the conversion of CO 2 and NH 3 to urea, addressing the pressing need for carbon capture and utilization by converting captured CO 2 from industrial sources into a renewable C1 feedstock, specifically urea, which is known for its high nitrogen content as a fertilizer. The conversion of CO 2 into urea is crucial to modern agriculture, providing a highly efficient and cost-effective means of supplying the nitrogen required to nourish the global population. Computational quantum chemical calculations elucidate the reaction mechanism, detailing all relevant intermediates and transition states. The findings reveal that the NH 2 and OH functional groups of chitosan play a critical role in the reaction by stabilizing key transition states through noncovalent interactions, thereby substantially lowering the overall activation energy barrier. The current study provides significant molecular-level insights into the mechanism underlying chitosan’s noncovalent catalytic properties, establishing a robust foundation for understanding its potential applications as an effective, environmentally sustainable approach to urea synthesis. This work marks a significant advancement in green catalysis, aligning with UN Sustainable Development Goals (SDG) 13 (Climate Action) by using CO 2 as a feedstock, SDG 9 (Industry, Innovation, and Infrastructure) by providing a sustainable industrial alternative, and SDG 12 (Responsible Consumption and Production) by demonstrating a circular-economy model valorizing biowaste. These findings pave the way for future experimental validation and the development of biopolymer catalysts, contributing to a more sustainable chemical industry. Keywords: chitosan, noncovalent catalysis, CO 2 valorization, urea, hydrogen bonding, density functional theory Introduction The rising atmospheric concentration of CO 2 , primarily attributable to anthropogenic activities, is recognized as the principal driver of global climate change. Consequently, every inhabited region is now experiencing the effects of climate change, including more frequent and intense heatwaves, prolonged droughts, destructive storms, and rising sea levels. These environmental shifts propagate into significant societal risks, threatening global food and water security, compromising human health, and accelerating biodiversity loss at an alarming rate. The industrial sector, while a critical engine of economic growth, constitutes a disproportionately large source of greenhouse gas emissions. In 2022, industrial activities were directly responsible for the 9.0 Gt of CO 2 emissions, representing a quarter of the global energy system’s total emissions. Within this sector, the iron and steel industry presents a particularly intractable challenge, contributing a staggering 7–11% of all global CO 2 emissions. In 2020 alone, direct emissions from steel production amounted to approximately 2.6 Gt, with an additional 1.0 Gt attributable to the sector’s electricity consumption. , This immense carbon footprint is not an incidental byproduct but is intrinsically linked to the core chemistry of steelmaking. The dominant production method, the blast furnace-basic oxygen furnace (BF-BOF) process, relies on carbothermic reduction of iron ore using metallurgical coal. This century-old technology is fundamentally carbon-intensive, accounting for over 90% of the industry’s emissions and releasing approximately 2.4–3.0 tonnes of CO 2 for every tonne of steel produced. While alternative methods, such as the electric arc furnace (EAF) process that uses recycled scrap, are more than 75% cleaner, the global supply of scrap is insufficient to meet the rising demand. This situation effectively locks the industry into a high-emission trajectory. It highlights the pressing need for innovative chemical solutions that can decouple industrial production from fossil carbon. In light of this imperative for new industrial paradigms, the circular carbon economy (CCE) concept offers a strategic framework for transforming carbon from a waste byproduct into a valuable resource. At the core of the CCE framework is the strategy of carbon capture and utilization (CCU), which treats captured CO 2 from industrial point sources as a renewable C1 feedstock. Among the numerous potential products derivable from CO 2 valorization, urea emerges as a particularly impactful target. As a commodity chemical of immense global significance, it had a market value of USD 69.16 billion in 2024, with projections indicating an expansion to USD 97.06 billion by 2032. The primary driver of this demand is its critical role in agriculture. With a nitrogen content of 46%, urea is recognized as the most widely used nitrogen fertilizer worldwide. This makes it crucial for ensuring global food security, particularly in the face of challenges such as declining soil fertility and population growth. The contemporary methodology for urea synthesis employs the Bazarov reaction, a two-step industrial process utilizing CO 2 and NH 3 . , The initial step involves the formation of ammonium carbamate, an exothermic reaction necessitating rapid heat dissipation under pressures sufficient to liquefy CO 2 and NH 3 . Subsequently, the dehydration of ammonium carbamate yields urea. This latter step is slow and endothermic, often limiting the overall urea yield. While the Bazarov reaction remains the predominant route for urea production, its high energy consumption has spurred ongoing research into catalytic, more environmentally benign alternatives for both steps. Various catalysts have been reported to facilitate this process, including water, ammonia, cerium dioxide, and diverse soluble metal complexes. ,− In this context, the development of a sustainable, low-energy pathway for urea production from waste CO 2 offers dual advantages as it simultaneously valorizes a greenhouse gas and decarbonizes the production of a crucial global commodity. This investigation aims to explore the use of chitosan for noncovalent catalysis, a naturally occurring biopolymer derived from chitin. , Chitin, the second most naturally abundant polysaccharide after cellulose, functions as the primary structural element in the exoskeletons of arthropods (crustaceans and insects) and the cell walls of fungi. Its widespread availability positions it as a renewable and sustainable resource. Notably, the principal commercial source of chitin is the waste generated by the seafood processing industry, thereby establishing it as an exemplary material for waste valorization and a prime example of circular economy principles in action. Through deacetylation, chitin is converted into chitosan. Chitosan is widely recognized for its superior biocompatibility, biodegradability, and nontoxicity, attributes that align with the principles of green chemistry, making it an environmentally friendly choice for CO 2 conversion. The selection of chitosan is based not solely on its environmentally benign characteristics but also on its intrinsic chemical functionality, which renders it particularly well-suited for the intended reaction. Chitosan possesses a primary amine group at the C2 position, along with an abundance of primary and secondary hydroxyl groups on its polymer backbone. This endows chitosan with a high degree of chemical reactivity and versatility, enabling it to participate actively throughout the catalytic process. The rationale for employing chitosan for CO 2 conversion is further substantiated by its well-documented efficacy in CO 2 capture. − The present study not only marks a significant advancement in green catalysis but also aligns closely with several key United Nations Sustainable Development Goals (SDGs). Specifically, it addresses SDG 13 (Climate Action) by converting the potent greenhouse gas CO 2 into a valuable commodity, SDG 9 (Industry, Innovation, and Infrastructure) by proposing a greener alternative to a carbon-intensive industrial process, and SDG 12 (Responsible Consumption and Production) by demonstrating a circular economy approach that transforms greenhouse gas CO 2 into an essential agricultural product, urea. This work proposes using chitosan, guided by principles of noncovalent catalysis, to establish a low-energy reaction pathway for urea synthesis. The remarkable efficiency of noncovalent catalysis − inspires this approach, which employs weak, attractive interactions such as hydrogen bonds, halogen bonds, and π-interactions to bind substrates and stabilize reaction transition states. Unlike covalent catalysts, noncovalent catalysts offer greater flexibility in optimizing reaction conditions and implementing workup strategies. This adaptability can significantly enhance the effectiveness of catalytic processes, enabling broader applications across diverse chemical environments. Chitosan can leverage noncovalent interactions, primarily hydrogen bonding, through its functional groups to facilitate the conversion of CO 2 and NH 3 to urea. This research aims to demonstrate the viability of this sustainable strategy and make a significant contribution to the development of the circular and low-carbon chemical industry. Computational Methods Density Functional Theory (DFT) has emerged as a crucial computational tool for understanding reaction mechanisms and designing green catalysts across various sustainable applications. − In the current study, DFT was employed to investigate the conversion of CO 2 and NH 3 into urea, facilitated by noncovalent interactions with a chitosan monomer. The chitosan monomer was used as a model system rather than the complete polymer because modeling the entire polymeric structure is prohibitively computationally expensive. This methodological approach was adopted to mitigate the significant computational burden. The geometries of all stationary points were optimized in the gas phase at room temperature (298.15 K) using the ωB97XD method, which incorporates long-range dispersion corrections and is particularly effective for systems where noncovalent interactions are pivotal. The 6-311++G­(2d,2p) , basis set was applied to all atoms, ensuring high accuracy in the calculations, and providing the necessary diffuse and polarization functions for modeling weak intermolecular forces. After optimization, vibrational frequency analysis was conducted at the ωB97XD/6-311++G­(2d,2p) level to thoroughly characterize the nature of the stationary points on the potential energy surface. The absence of any imaginary frequencies unequivocally confirmed Minima, whereas transition states were identified by a single imaginary frequency, corresponding to the reaction coordinate. Furthermore, zero-point vibrational energies (ZPVEs) and thermal corrections were calculated and incorporated into the electronic energies to improve the accuracy of reaction energies. To elucidate and visually represent the noncovalent interactions between chitosan and the reactants, a noncovalent interactions (NCI) analysis was performed using Multiwfn 3.8 software. This analysis is fundamentally based on electron density (ρ) and its reduced density gradient (RDG). The NCI calculations were executed on the optimized geometries at the ωB97XD/6-311++G­(2d,2p) level of theory. The resulting data enables graphical identification of the weak interactions, such as hydrogen bonding and van der Waals interactions, which are indispensable for a comprehensive understanding of the catalytic mechanism. All quantum chemical calculations were executed using the Gaussian 16 software package. Results and Discussion In this study, chitosan monomer was employed for noncovalent catalysis. Initially, the geometry of the chitosan monomer was optimized. The optimized structure of chitosan monomer is displayed in Figure . This energy-minimized, optimized chitosan structure was used to convert CO 2 and NH 3 to urea (NH 2 CONH 2 ), and its proposed mechanism is illustrated in Figure . The molecular synthesis of urea occurs through the reaction of gaseous CO 2 and NH 3 in a stoichiometric ratio of 1:2. This synthesis process involves two ammonia addition steps, followed by H 2 O elimination, which leads to the production of urea. 1. Open in a new tab 2D structure of chitosan and 3D optimized structure of the chitosan monomer at the ωB97XD/6-311++G­(2d,2p) level. 2. Open in a new tab Proposed mechanism for the conversion of CO 2 and NH 3 to urea using chitosan monomer. Initially, CO 2 adsorbs onto the chitosan surface, primarily via the inherent primary amine functional groups, as depicted in Figure a. The optimized structure of the chitosan···CO 2 adsorbate reveals an interaction distance of 2.85 Å between the carbon atom of CO 2 and the nitrogen atom of the chitosan amine group. This adsorption is predominantly governed by weak, noncovalent van der Waals interaction as confirmed by NCI analysis in Figure b, which displays localized green patches in the region between the CO 2 molecule and the chitosan amine group, accompanied by a distinct spike in the corresponding graph. This confirms that the adsorption process does not involve the formation of stronger chemical bonds, but rather represents a surface-level phenomenon. 3. Open in a new tab (a) Optimized structure and bond distance in Å for chitosan···CO 2 adsorption. (b) Noncovalent interaction, 3D isosurfaces on the left and 2D-RDG graphs on the right, which illustrate the van der Waals interaction between chitosan and CO 2 at the ωB97XD/6-311++G­(2d,2p) level. The Gibbs free energy profile is given in Figure , and their corresponding optimized structures, including bond lengths, are presented in Figure (only the reactants are provided for a better view, excluding chitosan) of all the stationary points along the reaction pathway for the conversion of CO 2 and NH 3 to urea facilitated by noncovalent catalysis of chitosan. A comprehensive view of optimized structures, including those of the chitosan, for all stationary points is presented in Figure S1 . These were calculated at the ωB97XD/6-311++G­(2d,2p) level of theory. Additionally, the number of imaginary frequencies and thermal free energy corrections for all the stationary points in this reaction pathway are listed in Table S1 at the ωB97XD/6-311++G­(2d,2p) level of theory. The transition states, TS1 , TS2 , and TS3 , each exhibit a single imaginary frequency, confirming they are first-order saddle points along the reaction pathway. Conversely, structures A , B , C , D , and E have no imaginary frequencies, indicating they are minimum-energy structures along the reaction pathway. The reaction initiates through the interaction between the nucleophilic nitrogen atom of NH 3 and the electrophilic carbon atom of CO 2 in A , with a distance of 2.59 Å, resulting in the formation of a four-center transition state, TS1 , with an energy barrier of 39.56 kcal/mol. This interaction in TS1 facilitates the formation of a new C–N bond with a distance of 1.51 Å, which also involves the transfer of one hydrogen atom from NH 3 to the oxygen. This process ultimately yields carbamic acid in B , with C–N and O–H bond lengths of 1.35 and 0.96 Å, respectively. Subsequently, as a second NH 3 group approaches C , it undergoes a nucleophilic attack on the carbonyl carbon of the carbamic acid, leading once again to the formation of a four-center transition state, TS2, with an energy barrier of 54.34 kcal/mol. This results in the formation of an additional C–N bond and the transfer of a hydrogen atom from NH 3 to the carbonyl oxygen. This sequence results in the formation of a high-energy intermediate, D , with an energy of 24.72 kcal/mol. This high-energy intermediate D then progresses to transition state TS3 , with an energy barrier of 50.26 kcal/mol. In TS3 , the hydroxyl hydrogen undergoes transfer to the oxygen of another hydroxyl group, thereby prompting the elimination of a water molecule and the formation of the desired product, urea, in E . The zero-point-corrected energies are also provided in Table for comparison and closely align with the Δ G values. 4. Open in a new tab Gibbs free energy profile in kcal/mol for the conversion of CO 2 and NH 3 to urea facilitated by noncovalent catalysis of chitosan at ωB97XD/6-311++G­(2d,2p) level of theory. 5. Open in a new tab Calculated bond lengths in Å of all optimized stationary points along the reaction pathway for the conversion of CO 2 and NH 3 to urea facilitated by noncovalent catalysis of chitosan at ωB97XD/6-311++G­(2d,2p) level of theory. The dashed double-arrow line represents noncovalent interactions between the chitosan functional groups and the reactants. 1. Comparison between Δ E , Δ E + ZPVE, and Δ G Energies in kcal/mol for the Conversion of CO 2 and NH 3 to Urea Facilitated by Noncovalent Catalysis of Chitosan at the ωB97XD/6-311++G­(2d,2p) Level. energies (kcal/mol) structure labels Δ E Δ E + ZPVE Δ G A 0 0 0 TS1 37.90 37.61 39.56 B –3.63 –1.01 0.35 C 0 0 0 TS2 55.66 53.46 54.34 D 22.78 23.66 24.72 TS3 50.70 49.07 50.26 E 0.25 –0.38 –0.69 Open in a new tab The reaction under investigation proceeds heterogeneously on the chitosan surface, where chitosan influences the reaction via noncovalent interactions. An NCI analysis with a cutoff value of 0.5 was performed to elucidate the nature of interactions governing the conversion of CO 2 and NH 3 to urea, facilitated by chitosan’s noncovalent catalysis. NCI provides comprehensive insights into various weak interactions, including van der Waals and hydrogen bonds, thereby offering a visual representation of noncovalent interactions within a molecular system. The 3D isosurfaces and 2D RDG graphs, illustrating all stationary points along the reaction pathway, are presented in Figure . Chitosan facilitates the reaction predominantly through noncovalent interactions, particularly involving its OH and NH 2 functional groups. The OH and NH 2 groups on chitosan form hydrogen bonds with the reactant molecules at all stationary points, evidenced by the blue isosurfaces and distinct spikes in the corresponding RDG graphs. These findings confirm that chitosan’s role is not through direct chemical bonding but rather through influential hydrogen bonding interactions. This hydrogen-bonding interaction stabilizes the reaction’s transition state, thereby lowering the activation energy. This is supported by the comparison of reaction energies with and without chitosan, as illustrated in Table and displayed in Figure S2 , where the activation barriers for all the transition states are lower in the presence of chitosan. 6. Open in a new tab Noncovalent interaction, 3D isosurfaces on the left and 2D-RDG graphs on the right of all the optimized stationary points along the reaction pathway of the conversion of CO 2 and NH 3 to urea facilitated by noncovalent catalysis of chitosan at ωB97XD/6-311++G­(2d,2p) level of theory. Isosurfaces are colored as follows: van der Waals interactions (green), hydrogen bonding (blue), and repulsive interactions (red). 2. Comparison of Δ G in kcal/mol for Conversion of CO 2 and NH 3 to Urea with and without Chitosan at the ωB97XD/6-311++G­(2d,2p) Level. Δ G (kcal/mol) structure labels chitosan without chitosan A 0.00 0.00 TS1 39.56 47.41 B 0.35 4.16 C 0.00 0.00 TS2 54.34 56.96 D 24.72 26.78 TS3 50.26 52.82 E –0.69 0.73 Open in a new tab Conclusions This study elucidated the catalytic role of chitosan in the conversion of CO 2 and NH 3 to urea using DFT calculations. The findings confirm that chitosan effectively facilitates this conversion via noncovalent interactions, as confirmed by NCI analysis. The reaction mechanism initiates with the adsorption of CO 2 onto the chitosan surface. Subsequently, NH 3 attacks the adsorbed CO 2 to form carbamic acid. A second NH 3 molecule then attacks the carbamic acid, leading to the elimination of a water molecule and the formation of the desired urea product. The NH 2 and OH functional groups in chitosan play a crucial role in the reaction by interacting noncovalently with the reactants via hydrogen bonds. This interaction effectively lowers the activation energy barriers by stabilizing the transition state complexes. While this study demonstrates that a chitosan monomer effectively facilitates the conversion of CO 2 and NH 3 to urea via noncovalent interactions, the model is inherently finite. The extent to which the full polymeric structure of chitosan might influence catalytic efficiency remains unexplored. Quantifying these potential differences remains an important open question for future research, which could be addressed through molecular dynamics simulations. Importantly, these theoretical predictions require rigorous experimental validation to confirm chitosan’s efficiency for noncovalent catalysis in practice and to optimize reaction conditions to maximize urea yield. As such, this study acts as a foundational step, offering detailed mechanistic insights that strongly encourage and guide future experimental investigations. The use of a readily available, biodegradable biopolymer, such as chitosan, for noncovalent catalysis represents a promising approach to developing greener chemical processes. Supplementary Material pg6c00008_si_001.pdf (336KB, pdf) Acknowledgments The authors express gratitude to the management of the Vellore Institute of Technology for providing the necessary facilities and covering the article processing charge for this manuscript. The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsphyschemau.6c00008 . Comprehensive view of optimized structures of all the stationary points; electronic energy, thermal correction to free energy, EE + thermal free energy correction, Δ G , and the number of imaginary frequencies (NImag) for optimized structures of the stationary points involved in the reaction pathway for the conversion of CO 2 and NH 3 to urea using the facilitated by noncovalent catalysis of chitosan at ωB97XD/6-311++G­(2d,2p) level; and Cartesian coordinates of all the optimized geometries ( PDF ) Conceptualization, investigation, and methodology K.T., A.H.M., and R.P.; supervision, K.T.; writingoriginal draft, A.H.M.; writingreview and editing, K.T. The authors declare no competing financial interest. References International Energy Agency. CO2 Emissions in 2022; International Energy Agency: Paris, 2023. https://www.iea.org/reports/co2-emissions-in-2022 (accessed Oct 10, 2025). [ Google Scholar ] SteelWatch . SteelWatch Explainer: Why steelmaking drives climate change – and why it doesn’t have to be this way. https://steelwatch.org/steelwatch-explainers/climate/ (accessed Oct 10, 2025). Amar, O. Steel’s Sustainability: Metal’s Environmental Impact Calculated. Arbor. https://www.arbor.eco/blog/metal-environmental-impact (accessed Oct 10, 2025). Alsarhan L. M., Alayyar A. S., Alqahtani N. B., Khdary N. H.. Circular Carbon Economy (CCE): A Way to Invest CO2 and Protect the Environment, a Review. Sustainability. 2021;13(21):11625. doi: 10.3390/su132111625. [ DOI ] [ Google Scholar ] Yoon H.-J., Atsbha T. A., Yoon T., Shin D., An J., Zarei M., Cherif A., Suh S., Lee C.-J.. Sustainable and Feasible Carbon Capture and Utilization Pathways towards Net-Zero. Renewable Sustainable Energy Rev. 2025;211:115331. doi: 10.1016/j.rser.2025.115331. [ DOI ] [ Google Scholar ] Hassan A., Dar M., Dar M. A.. Driving CO2 Conversion to C1 and C2 Products on Single-Atom Catalysts: Recent Experimental and Theoretical Insights. ACS Appl. Mater. Interfaces. 2025;17(38):53056–53080. doi: 10.1021/acsami.5c11447. [ DOI ] [ PubMed ] [ Google Scholar ] Fortune Business Insights . Urea Market Size, Share & Industry Analysis, By Grade, By Application, and Regional Forecast, 2024–2032. https://www.fortunebusinessinsights.com/urea-market-106850 (accessed Oct 10, 2025). Tsipis C. A., Karipidis P. A.. Mechanistic Insights into the Bazarov Synthesis of Urea from NH3 and CO2 Using Electronic Structure Calculation Methods. J. Phys. Chem. A. 2005;109(38):8560–8567. doi: 10.1021/jp051334j. [ DOI ] [ PubMed ] [ Google Scholar ] Bazarov A. I.. Obtaining Urea Directly from Urea and Carbon Dioxide. J. Prakt. Chem. 1870;2:283–312. [ Google Scholar ] Ding J., Ye R., Fu Y., He Y., Wu Y., Zhang Y., Zhong Q., Kung H. H., Fan M.. Direct Synthesis of Urea from Carbon Dioxide and Ammonia. Nat. Commun. 2023;14(1):4586. doi: 10.1038/s41467-023-40351-5. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Koizumi H., Takeuchi K., Matsumoto K., Fukaya N., Sato K., Uchida M., Matsumoto S., Hamura S., Choi J.-C.. One-Pot Catalytic Synthesis of Urea Derivatives from Alkyl Ammonium Carbamates Using Low Concentrations of CO2 . Commun. Chem. 2021;4(1):66. doi: 10.1038/s42004-021-00505-2. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Hanson D. S., Wang Y., Zhou X., Washburn E., Ekmekci M. B., Dennis D., Paripati A., Xiao D., Zhou M.. Catalytic Urea Synthesis from Ammonium Carbamate Using a Copper­(II) Complex: A Combined Experimental and Theoretical Study. Inorg. Chem. 2021;60(8):5573–5589. doi: 10.1021/acs.inorgchem.0c03467. [ DOI ] [ PubMed ] [ Google Scholar ] Xu M., Jupp A. R., Ong M. S. E., Burton K. I., Chitnis S. S., Stephan D. W.. Synthesis of Urea Derivatives from CO2 and Silylamines. Angew. Chem., Int. Ed. 2019;131(17):5763–5767. doi: 10.1002/ange.201900058. [ DOI ] [ PubMed ] [ Google Scholar ] Aranaz I., Alcántara A. R., Civera M. C., Arias C., Elorza B., Heras Caballero A., Acosta N.. Chitosan: An Overview of Its Properties and Applications. Polymers (Basel). 2021;13(19):3256. doi: 10.3390/polym13193256. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Peter S., Lyczko N., Gopakumar D., Maria H. J., Nzihou A., Thomas S.. Chitin and Chitosan Based Composites for Energy and Environmental Applications: A Review. Waste Biomass Valorization. 2021;12(9):4777–4804. doi: 10.1007/s12649-020-01244-6. [ DOI ] [ Google Scholar ] Lalarukh, Hussain S. M., Ali S., Zahoor A. F., Azmat H., Nazish N., Alshehri M. A., Riaz D., Naeem E., Mahrukh. Innovation of Advanced Polymers from Seafood Waste: Applications of Chitin and Chitosan. Polym. Adv. Technol. 2024;35(6):e6471. doi: 10.1002/pat.6471. [ DOI ] [ Google Scholar ] Sharma P., Barnes B., Johnson R., Volkis V. V.. Hydrolyzed Chitin and Chitosan Blends as Reversible and Biocompatible Sorbents for Carbon Dioxide Sorption. Compounds. 2025;5(2):18. doi: 10.3390/compounds5020018. [ DOI ] [ Google Scholar ] Loryuenyong V., Nakhlo W., Srikaenkaew P., Yaidee P., Buasri A., Eiad-Ua A.. Enhanced CO2 Capture Potential of Chitosan-Based Composite Beads by Adding Activated Carbon from Coffee Grounds and Crosslinking with Epichlorohydrin. Int. J. Mol. Sci. 2024;25(16):8916. doi: 10.3390/ijms25168916. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Shahin Shamsabadi A., Zhang Z., Rumi S. S., Chabi S., Lucia L. A., Abidi N.. High-Pressure CO2 Treatment of Cellulose, Chitin and Chitosan: A Mini Review and Perspective. Int. J. Biol. Macromol. 2025;308:142097. doi: 10.1016/j.ijbiomac.2025.142097. [ DOI ] [ PubMed ] [ Google Scholar ] United Nations . The 17 Sustainable Development Goals. Department of Economic and Social Affairs. https://sdgs.un.org/ (accessed Nov 3, 2025). Sak M. H., Jacobsen E. N.. Selective Noncovalent Catalysis with Small Molecules. Chem. Rev. 2025;125(17):8319–8369. doi: 10.1021/acs.chemrev.5c00121. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Toste F. D., Sigman M. S., Miller S. J.. Pursuit of Noncovalent Interactions for Strategic Site-Selective Catalysis. Acc. Chem. Res. 2017;50(3):609–615. doi: 10.1021/acs.accounts.6b00613. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Wheeler S. E., Seguin T. J., Guan Y., Doney A. C.. Noncovalent Interactions in Organocatalysis and the Prospect of Computational Catalyst Design. Acc. Chem. Res. 2016;49(5):1061–1069. doi: 10.1021/acs.accounts.6b00096. [ DOI ] [ PubMed ] [ Google Scholar ] Zhang W., Hu L., Zhang H., Pan C., Tang J.. Stable Non-Covalent Co­(Salphen)-Based Polymeric Catalyst for Highly Efficient and Selective Oxidation of 2,3,6-Trimethylphenol. Polymers (Basel). 2020;12(5):1076. doi: 10.3390/polym12051076. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Oiarbide M., Palomo C.. Extended Enolates: Versatile Intermediates for Asymmetric C-H Functionalization via Noncovalent Catalysis. Chem. - Eur. J. 2021;27(40):10226–10246. doi: 10.1002/chem.202100756. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Storch G., Trapp O.. By-Design Enantioselective Self-Amplification Based on Non-Covalent Product–Catalyst Interactions. Nat. Chem. 2017;9(2):179–187. doi: 10.1038/nchem.2638. [ DOI ] [ PubMed ] [ Google Scholar ] Hassani N.. Towards Green Catalysis: DFT Study of Water Splitting on Metal-Free Porous 3D-C6B6. J. Mol. Liq. 2025;434:127968. doi: 10.1016/j.molliq.2025.127968. [ DOI ] [ Google Scholar ] Tankov I., Yankova R.. DFT Analysis, Reaction Kinetics and Mechanism of Esterification Using Pyridinium Nitrate as a Green Catalyst. J. Mol. Liq. 2019;277:241–253. doi: 10.1016/j.molliq.2018.12.087. [ DOI ] [ Google Scholar ] Kumar K., Khatri V., Parveen F., Kashyap H. K., Upadhyayula S.. Synthesis of an Oxygenated Fuel Additive from a Waste Biomass Derived Aldehyde Using a Green Catalyst: An Experimental and DFT Study. Sustainable Energy Fuels. 2020;4(6):2924–2936. doi: 10.1039/D0SE00100G. [ DOI ] [ Google Scholar ] Hernández Fernández J., Prieto Palomo J., Ortega-Toro R.. Application of DFT and Experimental Tests for the Study of Compost Formation Between Chitosan-1,3-Dichloroketone with Uses for the Removal of Heavy Metals in Wastewater. J. Compos. Sci. 2025;9(2):91. doi: 10.3390/jcs9020091. [ DOI ] [ Google Scholar ] Chai J.-D., Head-Gordon M.. Long-Range Corrected Hybrid Density Functionals with Damped Atom–Atom Dispersion Corrections. Phys. Chem. Chem. Phys. 2008;10(44):6615–6620. doi: 10.1039/b810189b. [ DOI ] [ PubMed ] [ Google Scholar ] Krishnan R., Binkley J. S., Seeger R., Pople J. A.. Self-consistent Molecular Orbital Methods. XX. A Basis Set for Correlated Wave Functions. J. Chem. Phys. 1980;72(1):650–654. doi: 10.1063/1.438955. [ DOI ] [ Google Scholar ] Clark T., Chandrasekhar J., Spitznagel G. W., Schleyer P. V. R.. Efficient Diffuse Function-Augmented Basis Sets for Anion Calculations. III. The 3–21+G Basis Set for First-Row Elements, Li-F. J. Comput. Chem. 1983;4(3):294–301. doi: 10.1002/jcc.540040303. [ DOI ] [ Google Scholar ] Cukrowski I., de Lange J. H., Adeyinka A. S., Mangondo P.. Evaluating Common QTAIM and NCI Interpretations of the Electron Density Concentration through IQA Interaction Energies and 1D Cross-Sections of the Electron and Deformation Density Distributions. Comput. Theor. Chem. 2015;1053:60–76. doi: 10.1016/j.comptc.2014.10.005. [ DOI ] [ Google Scholar ] Lu T., Chen F.. Multiwfn: A Multifunctional Wavefunction Analyzer. J. Comput. Chem. 2012;33(5):580–592. doi: 10.1002/jcc.22885. [ DOI ] [ PubMed ] [ Google Scholar ] Frisch, M. J. ; Trucks, G. W. ; Schlegel, H. B. ; Scuseria, G. E. ; Robb, M. A. ; Cheeseman, J. R. ; Scalmani, G. ; Barone, V. ; Petersson, G. A. ; Nakatsuji, H. ; Li, X. ; Caricato, M. ; Marenich, A. V. ; Bloino, J. ; Janesko, B. G. ; Gomperts, R. ; Mennucci, B. ; Hratchian, H. P. ; Ortiz, J. V. ; Izmaylov, A. F. ; Sonnenberg, J. L. ; Williams-Young, D. ; Ding, F. ; Lipparini, F. ; Egidi, F. ; Goings, J. ; Peng, B. ; Petrone, A. ; Henderson, T. ; Ranasinghe, D. ; Zakrzewski, V. G. ; Gao, J. ; Rega, N. ; Zheng, G. ; Liang, W. ; Hada, M. ; Ehara, M. ; Toyota, K. ; Fukuda, R. ; Hasegawa, J. ; Ishida, M. ; Nakajima, T. ; Honda, Y. ; Kitao, O. ; Nakai, H. ; Vreven, T. ; Throssell, K. ; Montgomery, Jr., J. A. ; Peralta, J. E. ; Ogliaro, F. ; Bearpark, M. J. ; Heyd, J. J. ; Brothers, E. N. ; Kudin, K. N. ; Staroverov, V. N. ; Keith, T. A. ; Kobayashi, R. ; Normand, J. ; Raghavachari, K. ; Rendell, A. P. ; Burant, J. C. ; Iyengar, S. S. ; Tomasi, J. ; Cossi, M. ; Millam, J. M. ; Klene, M. ; Adamo, C. ; Cammi, R. ; Ochterski, J. W. ; Martin, R. L. ; Morokuma, K. ; Farkas, O. ; Foresman, J. B. ; Fox, D. J. . Gaussian; Gaussian, Inc.: Wallingford, CT, 2016. [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials pg6c00008_si_001.pdf (336KB, pdf) Articles from ACS Physical Chemistry Au are provided here courtesy of American Chemical Society ACTIONS View on publisher site PDF (4.8 MB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

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