In-silico study on perovskites application in capturing and distorting coronavirus
Bibliographic record
Abstract
The COVID-19 pandemic, known as coronavirus pandemic, a global pandemic, emerged from the beginning of 2020 and became dominant in many countries. As COVID-19 is one of the deadliest pandemics in history and has a high rate of distribution, a fast and extensive reaction was needed. Considering its composition, revealing the infection mechanism is beneficial for effective decisions against the spread and attack of COVID-19. Investigating data from numerous studies confirms that the penetration of SARS-CoV-2 occurs along with bonding spike protein (S protein) and through ACE2; Therefore, these two parts were the focus of research on the suppression and control of the infection. Performing lab research on all promising candidates requires years of experimental study, which is time-consuming and not an acceptable solution. Molecular dynamic simulation can decipher the performance of nano-structures in preventing the spread of coronavirus in a shorter time. This study surveyed the effect of three nano-perovskite structures (SrTiO3, CaTiO3, and BaTiO3), a cutting-edge group of perovskite materials with outstanding properties on coronavirus. Various computational parameters evaluate the effectiveness of these structures. Results of the simulation indicated that SrTiO3 performs better in SARS-CoV-2 suppression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".