Optimization of Differential pH Sensors Device Operation Conditions to Be Used in Quantification of Low Glucose Concentration
Bibliographic record
Abstract
The aim of this study is to find optima of operating conditions of phosphorylation enzymatic reaction of glucose within differential pH sensors device. Five variables were studied, i.e., amount of enzyme (7.45-44.7 µl), buffer concentration (20-50mM), pH of buffer (6.8 to 8.1), ATP concentration (0.2-2mM) and Mg+2 concentration (1.2 to 6mM).The kinetic study indicated optima of amount of enzyme of (30µl), buffer concentration of (40mM), pH value of buffer of (7.6), ATP concentration of (1.2mM) and Mg2+ concentration of (2.2mM) for phosphorylation of 1g/l glucose concentration sample. A calibration curve of glucose quantification was done for low glucose concentration range i.e. from 0 to 1 g/l. This low range makes the assay of this study efficient to be used in many applications. For instance, the assay can be used in glucose quantification during cultivation of variety kinds of cells at low glucose concentration. The assay was developed by using HEPES buffer as new carrying buffer system.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".