Preparation and Characterization of Strong Polar Macro Porous Resin
Bibliographic record
Abstract
The macro porous adsorption resin produced in this experiment mainly uses dipole dichloride etc. as the material, a certain proportion of mixed solution composed of pyridine and 1, 4-dioxane as the composite oxidant. Then adding a certain proportion of mixed solution composed of cyclopean and paraffin in the homogeneous solution of cellulose as the photogenic agent. As a result, these materials will polymerize, and generate the resin. Among these materials, the cellulose is polymeric monomer, dipole dichloride is cross linker, and they crosslink and polymerize each other, forming the porous skeletal structure of the macro porous adsorption resin, which is a kind of cross linked polymer consisting of ion exchange group. For the synthetic macro porous resin, using Fourier infrared spectrometer for pressed characterization, and scanning electron microscope (SEM) for resin pore-forming characterization, the result shows that the new macro porous resin has been synthesized. This paper taking ruin as adsorb ate explores the adsorption property of the synthetic macro porous resin to glycoside material, including property of equilibrium adsorption, static adsorption and adsorption. Meanwhile the influence of different solvents on the resin adsorption property is testified. After a general comparison, we have found that the resin has the strongest adsorption property in ethanol solvent, while for adsorption, the best effect is reached when using water as the elegant. What's more, the adsorption data shows that the adsorption of ruin on the very resin conforms to the Freundlich isothermal adsorption equation.
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) | 0.000 | 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".