Sequential Ammonia and Carbon Dioxide Adsorption on Pyrolyzed Biomass to Recover Waste Stream Nutrients
Bibliographic record
Abstract
Abstract The amine-rich surfaces of pyrolyzed human solid waste (py-HSW) can be “primed” or “regenerated” with carbon dioxide (CO2) to enhance their adsorption of ammonia (NH3) for use as a soil amendment. To better understand the mechanism by which CO2 exposure facilitates NH3 adsorption to py-HSW, we artificially enriched a model sorbent, pyrolyzed, oxidized wood (py-ox wood) with amine functional groups through exposure to NH3. We then exposed these N-enriched materials to CO2 and then resorbed NH3. The high heat of CO2 adsorption (Qst) on py-HSW, 49 kJ mol–1, at low surface coverage, 0.4 mmol CO2 g–1, showed that the naturally occurring N compounds in py-HSW have a high affinity for CO2. The Qst of CO2 on py-ox wood also increased after exposure to NH3, reaching 50 kJ mol–1 at 0.7 mmol CO2 g–1, demonstrating that the incorporation of N-rich functional groups by NH3 adsorption is favorable for CO2 uptake. Adsorption kinetics of py-ox wood revealed continued, albeit diminishing NH3 uptake after each CO2 treatment, averaging 5.9 mmol NH3 g–1 for the first NH3 exposure event and 3.5 and 2.9 mmol NH3 g–1 for the second and third; the electrophilic character of CO2 serves as a Lewis acid, enhancing surface affinity for NH3 uptake. Furthermore, penetration of 15NH3 and 13CO2 measured by NanoSIMS reached over 7 μm deep into both materials, explaining the large NH3 capture. We expected similar NH3 uptake in py-HSW sorbed with CO2 and py-ox wood because both materials, py-HSW and py-ox wood sorbed with NH3, had similar N contents and similarly high CO2 uptake. Yet NH3 sorption in py-HSW was unexpectedly low, apparently from potassium (K) bicarbonate precipitation, reducing interactions between NH3 and sorbed CO2; 2-fold greater surface K in py-HSW was detected after exposure to CO2 and NH3 than before gas exposure. We show that amine-rich pyrolyzed waste materials have high CO2 affinity, which facilitates NH3 uptake. However, high ash contents as found in py-HSW hinder this mechanism.
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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) | 0.002 | 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".