Influence of Biochar Feedstocks on Nitrate Adsorption Capacity
Bibliographic record
Abstract
The demand for intensive agriculture to boost food and crop production has increased. High nitrogen (N) fertilizer use is crucial for increasing agricultural productivity but often leads to significant nitrate losses, posing risks to surface and groundwater quality. This study examines the role of biochar as a soil amendment to enhance nutrient retention and mitigate nitrate leaching. By improving nitrogen efficiency, biochar offers a sustainable strategy to reduce the environmental impacts of intensive agriculture while maintaining soil fertility. An incubation study investigated four biochar feedstocks: spruce bark biochar at 550 °C (SB550), hardwood biochar (75% sugar maple) at 500 °C (HW500), sawdust (fir/spruce) biochar at 427 °C (FS427), and softwood biochar at 500 °C (SW500), to identify the most effective nitrate adsorbent. Scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FT-IR) were employed to analyze biochar morphology and surface functional groups. Adsorption isotherms were modeled using the Langmuir and Freundlich equations. The results indicated that surface functional groups, such as aromatic C=C stretching and bending, aromatic C–H bending, and phenolic O–H bending, play crucial roles in enhancing electrostatic attraction and, consequently, the nitrate adsorption capacity of biochar. The equilibrium adsorption data from this study fit well with both the Langmuir and Freundlich isotherm models. Among the four biochar types tested, SB550 exhibited the highest nitrate adsorption capacity, with a maximum of 184 mg/g. The adsorption data showed excellent conformity to the Langmuir and Freundlich models, with correlation coefficients (R2) exceeding 0.987 for all biochar types. These findings highlight the high accuracy of these models in predicting nitrate adsorption capacities.
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) | 0.000 | 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".