Investigating the effect of Taheri Consciousness Field A on the Behavior of Biomimetic Micellar Supramolecular Models
Bibliographic record
Abstract
Supramolecular bio-mimicry models are aggregates of chemical components that have the ability to mimic the function of biological molecules. There are many applications for bio-mimicry models in the industry and other research fields. In basic biology, bio-mimicry models can help investigate vital behaviors and life-beginning forms. The effects of Taheri Consciousness Fields (TCFs) on different levels of life in living systems and non-living components such as metal elements have been studied and confirmed prior to this study. In the present research, we investigated the effect of a TCF on biomimetic models, as chemical structures with biologic-like behaviors, with the aim of understanding how vital behaviors are formed from basic chemical components giving rise to life. In this way, the effect of a type of TCF, named TCF (A), on the structure and function of biomimetic micellar supramolecular models mimicking enzyme behaviors was investigated. To mimic the behavior of heme proteins, such as horseradish peroxidase enzyme, this model contained sodium dodecyl sulphate (SDS) micelles body with histidinate hematin in its core. The results showed that TCF (A) does not change the chemical structure of the built-in biomimetic models. However, the predominant population of the sample model was observed to have a smaller particle size, unlike the untreated control. Additionally, the catalytic activity of the biomimetic sample model had an 8% increase in catalytic efficiency, which resembled the performance of the natural enzyme better than the untreated control. Moreover, zeta potential, conductivity, and mobility of the sample model under the influence of TCF (A) were changed by 40%-45% in comparison with the control. In conclusion, according to the results, the TCF (A) treatment made the micellar supramolecular biomimetic model more stable. It more resembles the structure and function of the colloidal solution with the biological molecules of living organisms.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".