Extreme Biomimetics: Designing of the First Nanostructured 3D Spongin–Atacamite Composite and its Application
Bibliographic record
Abstract
The design of new composite materials using extreme biomimetics is of crucial importance for bioinspired materials science. Further progress in research and application of these new materials is impossible without understanding the mechanisms of formation, as well as structural features at the molecular and nano-level. It presents a challenge to obtain a holistic understanding of the mechanisms underlying the interaction of organic and inorganic phases under conditions of harsh chemical reactions for biopolymers. Yet, an understanding of these mechanisms can lead to the development of unusual-but functional-hybrid materials. In this work, a key way of designing centimeter-scale macroporous 3D composites, using renewable marine biopolymer spongin and a model industrial solution that simulates the highly toxic copper-containing waste generated in the production of printed circuit boards worldwide, is proposed. A new spongin-atacamite composite material is developed and its structure is confirmed using neutron diffraction, X-ray diffraction, high-resolution transmission electron microscopy/selected-area electron diffraction, X-ray photoelectron spectroscopy, near-edge X-ray absorption fine structure spectroscopy, and electron paramagnetic resonance spectroscopy. The formation mechanism for this material is also proposed. This study provides experimental evidence suggesting multifunctional applicability of the designed composite in the development of 3D constructed sensors, catalysts, and antibacterial filter systems.
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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) | 0.000 | 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 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".