{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/132638"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/132638","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Single-particle instrument simulator: Bridging experiments and models","abstract":"Aerosol mixing state, the distribution of chemical species across individual aerosol particles, is crucial for quantifying aerosol optical, chemical, and micro-physical properties. For example, a particle’s absorptivity depends on whether light-absorbing components such as black carbon are externally or internally mixed. Particle-resolved models such as PartMC track the mass of each species per particle, whereas single-particle mass spectrometers report ion signals as a function of mass-to-charge ratios. Since ion signals depend not only non-linearly on species mass but also on species-specific ionization efficiencies, fragmentation patterns, and overlapping signals, there is no straightforward mapping between mass spectra and model species masses. To bridge this gap, we develop SPIN-sim (Single-Particle Instrument Simulator), a framework that converts mass spectrometer ion signals into model-comparable composition estimates. SPIN-sim uses Non-negative Matrix Factorization (NMF) to decompose the measured mass spectra and estimate the fractional contribution of individual species in mixed particles. Tests on synthetic mixtures show that SPIN-sim can reconstruct species fractions with errors below 5% for most particles. Tests with two contrasting systems, NaCl + ammonium sulfate and NaCl + KI, show that accurate interpretation requires instrument-specific calibration, since signal–composition relationships differ even in simple binary mixtures. By providing a path towards a quantitative mapping between single-particle measurements and particle-resolved model outputs, SPIN-sim enables direct evaluation of mixing state representation in models. This approach advances model-measurement integration in aerosol science and supports improved characterization of aerosol impacts on climate and air quality.","abstract_html":"Aerosol mixing state, the distribution of chemical species across individual aerosol particles, is crucial for quantifying aerosol optical, chemical, and micro-physical properties. For example, a particle’s absorptivity depends on whether light-absorbing components such as black carbon are externally or internally mixed. Particle-resolved models such as PartMC track the mass of each species per particle, whereas single-particle mass spectrometers report ion signals as a function of mass-to-charge ratios. Since ion signals depend not only non-linearly on species mass but also on species-specific ionization efficiencies, fragmentation patterns, and overlapping signals, there is no straightforward mapping between mass spectra and model species masses. To bridge this gap, we develop SPIN-sim (Single-Particle Instrument Simulator), a framework that converts mass spectrometer ion signals into model-comparable composition estimates. SPIN-sim uses Non-negative Matrix Factorization (NMF) to decompose the measured mass spectra and estimate the fractional contribution of individual species in mixed particles. Tests on synthetic mixtures show that SPIN-sim can reconstruct species fractions with errors below 5% for most particles. Tests with two contrasting systems, NaCl + ammonium sulfate and NaCl + KI, show that accurate interpretation requires instrument-specific calibration, since signal–composition relationships differ even in simple binary mixtures. By providing a path towards a quantitative mapping between single-particle measurements and particle-resolved model outputs, SPIN-sim enables direct evaluation of mixing state representation in models. This approach advances model-measurement integration in aerosol science and supports improved characterization of aerosol impacts on climate and air quality.","abstract_has_math":false,"creators":["Lee, Kyuhaeng"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Atmospheric Sciences","degree_department":null,"school":null,"contributors":["Riemer, Nicole","West, Matt","Nesbitt, Steve"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12","date_published":"2025-12","updated_at":"2026-07-22T22:25:07Z","subjects":["aerosol","mixing state","mass spectrometer","non-negative matrix factorization"],"languages":["en"],"rights":["Copyright 2025 Kyuhaeng Lee"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/132638","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Riemer, Nicole","West, Matt","Nesbitt, Steve"]},{"key":"dc:creator","label":"Author","values":["Lee, Kyuhaeng"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12","2025-11-17"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Atmospheric Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["aerosol","mixing state","mass spectrometer","non-negative matrix factorization"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Kyuhaeng Lee"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/132638"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Aerosol mixing state, the distribution of chemical species across individual aerosol particles, is crucial for quantifying aerosol optical, chemical, and micro-physical properties. For example, a particle’s absorptivity depends on whether light-absorbing components such as black carbon are externally or internally mixed. Particle-resolved models such as PartMC track the mass of each species per particle, whereas single-particle mass spectrometers report ion signals as a function of mass-to-charge ratios. Since ion signals depend not only non-linearly on species mass but also on species-specific ionization efficiencies, fragmentation patterns, and overlapping signals, there is no straightforward mapping between mass spectra and model species masses. To bridge this gap, we develop SPIN-sim (Single-Particle Instrument Simulator), a framework that converts mass spectrometer ion signals into model-comparable composition estimates. SPIN-sim uses Non-negative Matrix Factorization (NMF) to decompose the measured mass spectra and estimate the fractional contribution of individual species in mixed particles. Tests on synthetic mixtures show that SPIN-sim can reconstruct species fractions with errors below 5% for most particles. Tests with two contrasting systems, NaCl + ammonium sulfate and NaCl + KI, show that accurate interpretation requires instrument-specific calibration, since signal–composition relationships differ even in simple binary mixtures. By providing a path towards a quantitative mapping between single-particle measurements and particle-resolved model outputs, SPIN-sim enables direct evaluation of mixing state representation in models. This approach advances model-measurement integration in aerosol science and supports improved characterization of aerosol impacts on climate and air quality.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-12-01","The student, Kyuhaeng Lee, accepted the attached license on 2025-11-12 at 11:01.","The student, Kyuhaeng Lee, submitted this Thesis for approval on 2025-11-17 at 10:55.","This Thesis was approved for publication on 2025-11-17 at 15:00.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22861 on 2026-02-19 at 18:45:41"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Single-particle instrument simulator: Bridging experiments and models"]}]}],"canonical_facts":{"dc:contributor":["Riemer, Nicole","West, Matt","Nesbitt, Steve"],"dc:creator":["Lee, Kyuhaeng"],"dc:date":["2025-12","2025-11-17"],"dc:description":["Aerosol mixing state, the distribution of chemical species across individual aerosol particles, is crucial for quantifying aerosol optical, chemical, and micro-physical properties. For example, a particle’s absorptivity depends on whether light-absorbing components such as black carbon are externally or internally mixed. Particle-resolved models such as PartMC track the mass of each species per particle, whereas single-particle mass spectrometers report ion signals as a function of mass-to-charge ratios. Since ion signals depend not only non-linearly on species mass but also on species-specific ionization efficiencies, fragmentation patterns, and overlapping signals, there is no straightforward mapping between mass spectra and model species masses. To bridge this gap, we develop SPIN-sim (Single-Particle Instrument Simulator), a framework that converts mass spectrometer ion signals into model-comparable composition estimates. SPIN-sim uses Non-negative Matrix Factorization (NMF) to decompose the measured mass spectra and estimate the fractional contribution of individual species in mixed particles. Tests on synthetic mixtures show that SPIN-sim can reconstruct species fractions with errors below 5% for most particles. Tests with two contrasting systems, NaCl + ammonium sulfate and NaCl + KI, show that accurate interpretation requires instrument-specific calibration, since signal–composition relationships differ even in simple binary mixtures. By providing a path towards a quantitative mapping between single-particle measurements and particle-resolved model outputs, SPIN-sim enables direct evaluation of mixing state representation in models. This approach advances model-measurement integration in aerosol science and supports improved characterization of aerosol impacts on climate and air quality.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-12-01","The student, Kyuhaeng Lee, accepted the attached license on 2025-11-12 at 11:01.","The student, Kyuhaeng Lee, submitted this Thesis for approval on 2025-11-17 at 10:55.","This Thesis was approved for publication on 2025-11-17 at 15:00.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22861 on 2026-02-19 at 18:45:41"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/132638"],"dc:language":["en"],"dc:rights":["Copyright 2025 Kyuhaeng Lee"],"dc:subject":["aerosol","mixing state","mass spectrometer","non-negative matrix factorization"],"dc:title":["Single-particle instrument simulator: Bridging experiments and models"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Atmospheric Sciences"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:07Z"}