Microwave-Assisted Synthesis of Dual-Emissive N, Fe-Co Carbon Dot Nanocomposites for Latent Fingerprint Visualization Via AI-Enhanced Analytical Engineering
| dc.contributor.author | Lighvan, Vahid Ashrafi | |
| dc.contributor.author | Arsalani, Nasser | |
| dc.contributor.author | Gulcan, Mehmet | |
| dc.contributor.author | Aytin, Rahel Yildirim | |
| dc.date.accessioned | 2026-01-30T18:35:04Z | |
| dc.date.available | 2026-01-30T18:35:04Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | There is an urgent need for strategies that enable the rapid synthesis of long-wavelength fluorescent carbon dots (CDs). In forensic science, the development and analysis of latent fingerprints (LFPs) are vital for criminal investigations. This process involves two steps: enhancing LFP visualization for better detectability and using digital processing techniques for accurate database comparisons. Long-wavelength-emitting CDs can significantly improve LFP visualization by enhancing contrast and minimizing background interference. This study focuses on synthesizing dual green/red-emissive nitrogen and iron co-doped carbon dots (N, Fe-CD (green/red)) using microwave-assisted methods. These N, Fe-CD (green/red) were integrated into a plaster and starch composite, resulting in N, Fe-CD (green/red) @plaster-starch phosphors. The phosphors effectively enhanced LFP development through dusting techniques. Under blue/green light excitation, the emitted green/red fluorescence from the N, Fe-CD (green/red) significantly improved LFP visualization, proving their usefulness in forensic applications. Additionally, artificial intelligence (AI) algorithms were utilized to analyze fluorescence images of developed LFPs, achieving match scores exceeding 73.0%, which indicates a high similarity to control references. These findings highlight the AI algorithms' effectiveness in reliably identifying and comparing fingerprint features. | en_US |
| dc.description.sponsorship | echnological Research Council of Turkey (TUBITAK) [TUBITAK 2221 - Fellowships] | en_US |
| dc.description.sponsorship | The investigation was supported by the TUBITAK-2221 project. | en_US |
| dc.identifier.doi | 10.1007/s00604-025-07804-8 | |
| dc.identifier.issn | 0026-3672 | |
| dc.identifier.issn | 1436-5073 | |
| dc.identifier.scopus | 2-s2.0-105027285041 | |
| dc.identifier.uri | https://doi.org/10.1007/s00604-025-07804-8 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14720/29648 | |
| dc.language.iso | en | en_US |
| dc.publisher | Springer Wien | en_US |
| dc.relation.ispartof | Microchimica Acta | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Latent Fingerprints (LFPs) | en_US |
| dc.subject | Fluorescence Detection | en_US |
| dc.subject | N, Fe-Cd (Green/Red) | en_US |
| dc.subject | N, Fe-Cd (Green/Red) Plaster-Starch Phosphors | en_US |
| dc.subject | Artificial Intelligence | en_US |
| dc.title | Microwave-Assisted Synthesis of Dual-Emissive N, Fe-Co Carbon Dot Nanocomposites for Latent Fingerprint Visualization Via AI-Enhanced Analytical Engineering | en_US |
| dc.type | Article | en_US |
| dspace.entity.type | Publication | |
| gdc.author.scopusid | 59326401000 | |
| gdc.author.scopusid | 6602646380 | |
| gdc.author.scopusid | 8226754300 | |
| gdc.author.scopusid | 60329338500 | |
| gdc.author.wosid | Arsalani, Nasser/Abg-6145-2020 | |
| gdc.description.department | T.C. Van Yüzüncü Yıl Üniversitesi | en_US |
| gdc.description.departmenttemp | [Lighvan, Vahid Ashrafi; Arsalani, Nasser] Univ Tabriz, Fac Chem, Dept Organ & Biochem, Res Lab Polymer, 29 Bahman Blvd, Tabriz, East Azarbiajan, Iran; [Arsalani, Nasser; Gulcan, Mehmet; Aytin, Rahel Yildirim] Van Yuzuncu Yil Univ, Fac Sci, Dept Chem, TR-65080 Van, Turkiye | en_US |
| gdc.description.issue | 2 | en_US |
| gdc.description.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| gdc.description.scopusquality | Q1 | |
| gdc.description.volume | 193 | en_US |
| gdc.description.woscitationindex | Science Citation Index Expanded | |
| gdc.description.wosquality | Q1 | |
| gdc.identifier.pmid | 41521302 | |
| gdc.identifier.wos | WOS:001659349200001 | |
| gdc.index.type | WoS | |
| gdc.index.type | Scopus | |
| gdc.index.type | PubMed |
