Upcycling trace amounts of biomass waste into flash graphene can boost crop yields by more than a quarter and offer climate benefits
Bibliographic record
Abstract
Global food security faces immense pressure from population growth and climate change, demanding sustainable agricultural intensification. While biochar offers promise for soil enhancement and carbon sequestration, its large-scale application requires significant biomass feedstock and energy-intensive production, raising economic and carbon footprint concerns. Nano-enabled foliar feeding is gaining momentum, but practical, eco-efficient field use from lab to farm remains challenging. Bridging this gap is essential for realizing nano-enabled agriculture without exacerbating environmental burdens. Here, we demonstrate on-site conversion of ecologically safe flash graphene via flash joule heating. Spraying 18 g/hectare of this graphene, produced from 75 g (<0.001%) of crop residues per hectare, on multi-crops over two seasons increased yields by 9.1%–27.3% through enhanced photosynthesis and alleviated oxidative stress. Compared to biochar, this approach reduces farmers' inputs by 86%–91% and lowers life-cycle carbon emissions by up to 10,000-fold. We offered a self-sufficient, scalable, and climate-smart circular foliar feeding pathway to advance food security sustainably.
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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".