Effect of ultrasonic pre-treatment of thermomechanical pulp on hydrogen peroxide bleaching
Bibliographic record
Abstract
Ultrasound pre-treatments of softwood TMP had been carried to evaluate its impact on the efficiency of hydrogen peroxide bleaching. The trials were performed after a factorial design of experiment using frequency, power and time as variables. The experiments were conducted in an ultrasonic bath and then bleached with hydrogen peroxide. Measurements such as brightness, L*A*B* color system coordinate, residual hydrogen peroxide and metal content were evaluated on bleached pulp. The results indicate that the effect of ultrasonic treatment on brightness was dependent on the ultrasound frequency used; the brightness increased slightly at 68 kHz and decreased at 40 and 170 kHz. These results were correlated to the ultrasound effect on the generation of transition metals (copper, iron and manganese) which are responsible for catalytic decomposition of hydrogen peroxide. The influence of metal interference was minimized by using a chelating agent such as diethylene triamine pentaacetic acid (DTPA). With the results obtained in this study we have identified a set of option conditions, e.g. 1000 W, 40 kHz, 1.5 % consistency and 0.2% addition of DTPA prior to the bleaching stage (after ultrasonic pre-treatment) who improve brightness by 2.5 %ISO.
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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".