{"id":{"repo_id":"njit","oai_identifier":"oai:digitalcommons.njit.edu:dissertations-1744"},"canonical_url":"https://search.dev.ndltd.org/etd/njit/oai:digitalcommons.njit.edu:dissertations-1744","repository":{"repo_id":"njit","name":"NJIT","base_url":"https://digitalcommons.njit.edu/do/oai/"},"display":{"title":"Fluid-phase thermodynamics from molecular-level properties and interactions based in quantum theory","abstract":"A methodology to predict the thermodynamics of macroscopic fluid systems from quantum chemistry and statistical thermodynamics has been developed. This work extends the group-contribution concepts most utilized in chemical engineering. Computational chemistry software is used to define the geometries and electron density profiles of target molecules. Atoms in Molecules theory and associated software packages are used to calculate rigorous properties of the functional groups within molecules of interest. These properties are incorporated into an intermolecular potential energy function which describes interactions between entire molecules as a set of interactions between functional groups. This information is applied to a lattice-fluid model with the capability to predict volumetric properties of pure fluids and vapor/liquid equilibrium properties of mixture systems. This work develops a bridge from chemistry at the molecular level to the statistical mechanics at the macroscopic system level. The rigorous properties of functional groups lead to the application of firstprinciples mathematical models that qualitatively agree with volumetric properties of pure fluids and predict vapor/liquid equilibrium behavior for near-ambient mixtures of alkanes, alcohols and ethers. The theoretical and computational efforts developed in this work offer engineers the ability to determine molecular-level modeling parameters within engineering models without the use of experiment.","abstract_html":"A methodology to predict the thermodynamics of macroscopic fluid systems from quantum chemistry and statistical thermodynamics has been developed. This work extends the group-contribution concepts most utilized in chemical engineering. Computational chemistry software is used to define the geometries and electron density profiles of target molecules. Atoms in Molecules theory and associated software packages are used to calculate rigorous properties of the functional groups within molecules of interest. These properties are incorporated into an intermolecular potential energy function which describes interactions between entire molecules as a set of interactions between functional groups. This information is applied to a lattice-fluid model with the capability to predict volumetric properties of pure fluids and vapor/liquid equilibrium properties of mixture systems. This work develops a bridge from chemistry at the molecular level to the statistical mechanics at the macroscopic system level. The rigorous properties of functional groups lead to the application of firstprinciples mathematical models that qualitatively agree with volumetric properties of pure fluids and predict vapor/liquid equilibrium behavior for near-ambient mixtures of alkanes, alcohols and ethers. The theoretical and computational efforts developed in this work offer engineers the ability to determine molecular-level modeling parameters within engineering models without the use of experiment.","abstract_has_math":false,"creators":["Arturo, Steven G."],"institution":null,"degree_name":"Doctor of Philosophy in Chemical Engineering - (Ph.D.)","degree_level":null,"degree_discipline":"Chemical Engineering","degree_department":null,"school":null,"contributors":["Dana E. Knox","Robert Benedict Barat","Joseph W. Bozzelli"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2005,"date_issued":"2005-05-31T07:00:00Z","date_published":"2005-05-31T07:00:00Z","updated_at":"2026-07-24T03:22:58Z","subjects":["Engineering thermodynamics","Computational chemistry","Molecular interactions","Statistical thermodynamics","Atoms in molecular theory","Phase equilibrium","Chemical Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.njit.edu/dissertations/689","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dana E. Knox","Robert Benedict Barat","Joseph W. 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This work extends the group-contribution concepts most utilized in chemical engineering. Computational chemistry software is used to define the geometries and electron density profiles of target molecules. Atoms in Molecules theory and associated software packages are used to calculate rigorous properties of the functional groups within molecules of interest. These properties are incorporated into an intermolecular potential energy function which describes interactions between entire molecules as a set of interactions between functional groups. This information is applied to a lattice-fluid model with the capability to predict volumetric properties of pure fluids and vapor/liquid equilibrium properties of mixture systems. This work develops a bridge from chemistry at the molecular level to the statistical mechanics at the macroscopic system level. The rigorous properties of functional groups lead to the application of firstprinciples mathematical models that qualitatively agree with volumetric properties of pure fluids and predict vapor/liquid equilibrium behavior for near-ambient mixtures of alkanes, alcohols and ethers. The theoretical and computational efforts developed in this work offer engineers the ability to determine molecular-level modeling parameters within engineering models without the use of experiment."]},{"key":"dc:title","label":"Title","values":["Fluid-phase thermodynamics from molecular-level properties and interactions based in quantum theory"]}]}],"canonical_facts":{"dc:contributor":["Dana E. Knox","Robert Benedict Barat","Joseph W. Bozzelli"],"dc:creator":["Arturo, Steven G."],"dc:description.abstract":["A methodology to predict the thermodynamics of macroscopic fluid systems from quantum chemistry and statistical thermodynamics has been developed. This work extends the group-contribution concepts most utilized in chemical engineering. Computational chemistry software is used to define the geometries and electron density profiles of target molecules. Atoms in Molecules theory and associated software packages are used to calculate rigorous properties of the functional groups within molecules of interest. These properties are incorporated into an intermolecular potential energy function which describes interactions between entire molecules as a set of interactions between functional groups. This information is applied to a lattice-fluid model with the capability to predict volumetric properties of pure fluids and vapor/liquid equilibrium properties of mixture systems. This work develops a bridge from chemistry at the molecular level to the statistical mechanics at the macroscopic system level. The rigorous properties of functional groups lead to the application of firstprinciples mathematical models that qualitatively agree with volumetric properties of pure fluids and predict vapor/liquid equilibrium behavior for near-ambient mixtures of alkanes, alcohols and ethers. The theoretical and computational efforts developed in this work offer engineers the ability to determine molecular-level modeling parameters within engineering models without the use of experiment."],"dc:identifier":["https://digitalcommons.njit.edu/dissertations/689"],"dc:subject":["Engineering thermodynamics","Computational chemistry","Molecular interactions","Statistical thermodynamics","Atoms in molecular theory","Phase equilibrium","Chemical Engineering"],"dc:title":["Fluid-phase thermodynamics from molecular-level properties and interactions based in quantum theory"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Chemical Engineering"],"thesis:degree_name":["Doctor of Philosophy in Chemical Engineering - (Ph.D.)"]},"updated_at":"2026-07-24T03:22:58Z"}