First-Principles Approach for Assessing the Detection of Alzheimer’s Biomarkers Using Titanium Carbide MXenes
Bibliographic record
Abstract
Alzheimer’s disease (AD) is considered the most common neurodegenerative condition and is the main cause of dementia. AD progresses rapidly and is the major cause of death in the elderly population; thus, an early diagnosis is of vital importance. Medical research has successfully characterized a set of volatile organic compounds (VOCs) present in exhaled patient’s breath to be considered as fingerprints of AD. The present work, for the first time, aims at computationally designing highly efficient nano-biosensors capable of detecting the VOC biomarkers. We apply density functional theory (DFT) to study the adsorption properties of three representative VOCs, namely, 2,3-dimethylheptane (23-DMH), butylated hydroxytoluene (BHT), and pivalic acid (PVA), versus four interfering air molecules (i.e., N 2, O 2, CO 2, and H 2 O) on four different MXenes (i.e., thick Ti 3 C 2 T x and thin Ti 2 CT x MXenes, T x = O or S). All the molecules are found to exhibit physisorption interactions on the studied MXenes. Nevertheless, the energetic analysis shows clear selective adsorption of BHT on Ti 3 C 2 O 2 with an adsorption energy of −1.513 eV, which is desirable for practical sensing applications. Furthermore, distinct from all other VOCs and interfering air molecules, BHT oxidizes to the O passivation layer of MXenes with a charge transfer of +0.421e and induces magnetization of 0.467 μ B to transform the surface to become ferromagnetic. These changes are very promising to rectify the current–voltage characteristics and yield a high sensor response. We further performed thermodynamic analysis through the Langmuir adsorption model, which ensures the excellent adsorption performance of Ti 3 C 2 O 2 over a broad range of BHT concentrations at ambient temperature. Therefore, Ti 3 C 2 O 2 could be considered as highly sensitive and selective nano-biosensors toward the fingerprint VOCs of AD.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".