Thermodynamic Analysis of Nickel(II) and Zinc(II) Adsorption to Biochar
Bibliographic record
Abstract
While numerous studies have investigated metal uptake from solution by biochar, few of these have developed a mechanistic understanding of the adsorption reactions that occur at the biochar surface. In this study, we explore a combined modeling and spectroscopic approach for the first time to describe the molecular level adsorption of Ni(II) and Zn(II) to five types of biochar. Following thorough characterization, potentiometric titrations were carried out to measure the proton (H + ) reactivity of each biochar, and the data was used to develop protonation models. Surface complexation modeling (SCM) supported by synchrotron-based extended X-ray absorption fine structure (EXAFS) was then used to gain insights into the molecular scale metal–biochar surface reactions. The SCM approach was combined with isothermal titration calorimetry (ITC) data to determine the thermodynamic driving forces of metal adsorption. Our results show that the reactivity of biochar toward Ni(II) and Zn(II) directly relates to the site densities of biochar. EXAFS along with FT-IR analyses, suggest that Ni(II) and Zn(II) adsorption occurred primarily through proton-active carboxyl (−COOH) and hydroxyl (−OH) functional groups on the biochar surface. SCM-ITC analyses revealed that the enthalpies of protonation are exothermic and Ni(II) and Zn(II) complexes with biochar surface are slightly exothermic to slightly endothermic. The results obtained from these combined approaches contribute to the better understanding of molecular scale metal adsorption onto the biochar surface, and will facilitate the further development of thermodynamics-based, predictive approaches to biochar removal of metals from contaminated water.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".