Sustainable Valorization of Rice Straw into Biochar and Carbon Dots Using a Novel One-Pot Approach for Dual Applications in Detection and Removal of Lead Ions
Bibliographic record
Abstract
Lead (Pb) is a highly toxic heavy metal that causes significant health hazards and environmental damage. Thus, the detection and removal of Pb2+ ions in freshwater sources are imperative for safeguarding public health and the environment. Moreover, the transformation of single resources into multiple high-value products is vital for achieving sustainable development goals (SDGs). In this regard, the present work focused on the preparation of two efficient materials, i.e., biochar (R-BC) and carbon dots (R-CDs) from a single resource (rice straw), via a novel approach by using extraction and hydrothermal process. The various microscopic and spectroscopy techniques confirmed the formation of porous structure and spherical morphology of R-BC and R-CDs, respectively. FTIR analysis confirmed the presence of hydroxyl (–OH), carboxyl (–COO) and amine (N–H) groups on the R-CDs’ surface. The obtained blue luminescent R-CDs were employed as chemosensors for the detection of Pb2+ ions. The sensor exhibited a strong linear correlation over a concentration range of 1 µM to 100 µM, with a limit of detection (LOD) of 0.11 µM. Furthermore, the BET analysis of R-BC indicated a surface area of 1.71 m2/g and a monolayer volume of 0.0081 cm3/g, supporting its adsorption potential for Pb2+. The R-BC showed excellent removal efficiency of 77.61%. The adsorption process followed the Langmuir isotherm model and second-order kinetics. Therefore, the dual use of rice straw-derived provides a cost-effective, environmentally friendly solution for Pb2+ detection and remediation to accomplish the SDGs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".