Investigating the Effects of Taheri Consciousness Field 1 on the Enzyme-Like Behavior of Gold Nanozyme
Bibliographic record
Abstract
The study of chemical structures, with the bio-molecular activity, that mimic the behavior of biological molecules has always been of interest to researchers in the field of early life as well as technology and industry. Among these compounds, a type of nanomaterial with enzyme-like activity, called Nanozyme, has comparable performance with endogenous cellular compounds and has great potential in replacing natural enzymes. Taheri Consciousness Fields, as novel Fields, was founded and introduced by Mohammad Ali Taheri, according to previous several studies. These Fields are neither matter nor energy, having a different nature from known physical fields. The TCFs are capable of influencing the matter and energy of the studied system in order to make it more functional and efficient. These Fields are neither matter nor energy, therefore cannot be measured directly. But it is possible to study their effects on objects through controlled experiments. In this study, we investigate the effects of a type of Taheri Consciousness Field 1 (TCF1) on the physicochemical structure and function of the gold Nano-chemical models. Using electron microscopy as well as dynamic light scattering and kinetic assays, we observed significant changes in the nanoparticle particle size distribution (about 20%) and its kinetic constants (about 4%) under the influence of the TCF1. The results of this study confirmed a reproducible effect of TCF1 on the gold Nanozyme behaviors. We suggest that the effect of TCFs on various biomimetic molecules should be investigated to further understand the mechanism of TCFs.
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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".