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Record W4409480780 · doi:10.1063/5.0268179

Fe-doped asphaltenes carbon dots for tumor magnetic resonance imaging

2025· article· en· W4409480780 on OpenAlexafffund
Ozioma Udochukwu Akakuru, Sabad-e Gul, Zhusheng Liu, Steven L. Bryant, Aiguo Wu, Milana Trifkovic

Bibliographic record

VenuePhysics of Fluids · 2025
Typearticle
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsUniversity of Calgary
FundersAlberta Innovates
KeywordsPhysicsMagnetic resonance imagingAsphalteneDopingNuclear magnetic resonanceCarbon fibersNanotechnologyCondensed matter physicsMedicineChemical engineeringRadiologyComposite material

Abstract

fetched live from OpenAlex

Iron-based contrast agents have recently garnered attention as positive (T1) magnetic resonance imaging (MRI) contrast agents providing an alternative to gadolinium-based contrast agents mired with the nephrogenic systemic fibrosis downsides. Whereas the magnetic cores of iron enable T1 MRI contrast enhancement, the non-magnetic materials (e.g., polymers) usually deployed to stabilize the metal cores affect bulk water diffusion to the iron centers. We present an innovative approach in designing biocompatible complex of asphaltene-derived carbon dots (ACDs) with iron (ACD-Fe), where the ACDs enhanced hydrophilicity improves accessibility of Fe3+ centers to water molecules to achieve effective tumor MRI. The ACD-Fe design strategy involves synthesizing ACDs at a lower temperature (80 °C) with high mass yield, followed by iron doping via easy complexation with FeCl3 at room temperature. The ACD-Fe complex thus serves as tissue-tolerant T1 MRI contrast agent (r1 = 1.33 mM−1s−1) for tumor imaging. This report pioneers the use of asphaltene-derived materials in MRI and tumor imaging, presenting a low-cost, scalable synthesis protocol for the ACD-Fe complex as an effective T1 MRI contrast agent.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.273
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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