Iron Nanostructure Primes Arbuscular Mycorrhizal Fungi Symbiosis Tightly Connecting Maize Leaf Photosynthesis via a Nanofilm Effect
Bibliographic record
Abstract
It is crucial to clarify how the iron nanostructure activates plant growth, particularly in combination with arbuscular mycorrhizal fungi (AMF). We first identified 1.0 g·kg –1 of nanoscale zerovalent iron (nZVI) as appropriate dosage to maximize maize growth by 12.7–19.7% in non-AMF and 18.9–26.4% in AMF, respectively. Yet, excessive nZVI at 2.0 g·kg –1 exerted inhibitory effects while FeSO 4 showed slight effects ( p > 0.05). Under an appropriate dose, a nano core–shell structure was formed and the transfer and diffusion of electrons between PS II and PS I were facilitated, significantly promoting the reduction of ferricyanide and NADP ( p < 0.05). SEM images showed that excessive nZVI particles can form stacked layers on the surface of roots and hyphae, inhibiting water and nutrient uptake. TEM observations showed that excessive nanoparticles can penetrate into root cortical cells, disrupt cellular homeostasis, and substantially elevate Fe content in roots ( p < 0.05). This exacerbated membrane lipid peroxidation and osmotic regulation, accordingly restricting photosynthetic capacity and AMF colonization. Yet, appropriate nZVI can be adhered to a mycelium surface, forming a uniform nanofilm structure. The strength of the mycelium network was evidently enhanced, under an increased root colonization rate and an extramatrical hyphal length ( p < 0.05). Enhanced mycorrhizal infection was tightly associated with higher gas exchange and Rubisco and Rubisco enzyme activities. This enabled more photosynthetic carbon to input into AMF symbiont. There existed a positive feedback loop connecting downward transfer of photosynthate and upward transport of water/nutrients. FeSO 4 only slightly affected mycorrhizal development. Thus, it was the Fe nanostructure but not its inorganic salt state that primed AMF symbionts for better growth.
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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 | 0.001 |
| 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) | 0.009 | 0.002 |
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; both teacher heads agree on what is shown here.
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".