Percolation and Adsorption of Volatile Organic Compounds onto Hyper Activated Renewable Carbon for Low-carbon Composites Manufacturing
Bibliographic record
Abstract
Low-carbon composites (LCCs), as a type of biodegradable material consisting of lignocellulosic fibers and polymers, are considered as promising alternatives for petroleum derived plastics. However, the elimination of volatile organic compounds (VOCs) during the compounding of LCCs has been a long-struggled issue. These VOCs are highly toxic pollutants and carcinogens with strong odor, which have significantly limited the applications of LCCs in interior environments. Seeking an efficient adsorbent to capture the VOCs as well as systematically profiling its adsorption kinetics remains a challenge. In this study, we for the first time developed an innovative hyper activated renewable carbon (HARC), which possesses dominant alkaline groups of 0.133 mmol/g, large Brunauer-Emmett-Teller (BET) surface area of 1128.6 m2/g, as well as high adsorption energy of 25.24 kJ/mol. Its adsorption kinetics towards VOCs was further investigated to maximize the removal efficiency of VOCs. For this purpose, three different micrometer-sized HARC samples were prepared by alkali (NaOH) modification of renewable carbon, named as small sized HARC (S-HARC), medium sized HARC (M-HARC) and large sized HARC (L-HARC), respectively. Among these three samples, S-HARC exhibited the highest activity in capturing VOCs. Its fast migration velocity and better distribution inside the LCCs domain was demonstrated via dynamic simulation, which is advantageous in increasing its contact opportunities with VOCs molecules and favoring their adsorption. Scanning electron microscopy (SEM) revealed the different morphological structure of three HARC samples, and the concentration of surface groups was determined by Boehm titration. More than 120 compounds were identified via gas chromatography-mass spectroscopy (GC/MS), and the adsorption kinetics were analyzed based on the quantified VOCs content from mass spectroscopy (MS). The obtained adsorption data was better fitted in the linear form of Freundlich isotherms, rather than Langmuir isotherms. Compared with physical properties, the adsorption capacity of HARC was more correlated to the change in its chemical functionalities, especially the total alkaline groups. The finding in this study provides important information for deeper understanding of the adsorption and percolation dynamics of VOCs onto porous HARC. Using HARC, especially S-HARC, can significantly promote the removal of odorous VOCs, and open up new horizons of using LCCs as substitutes for traditional high-carbon plastics in broader industries.
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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 | 0.001 | 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".