Biotemplated Silica and Silicon Materials as Building Blocks for Micro- to Nanostructures
Bibliographic record
Abstract
Silicon is essential in several energy-related devices, including solar cells, batteries, and some electrochemical systems. These devices often rely on micro- or nanostructures to function efficiently, and require patterning of metallic surfaces. Currently, constructing silicon features at the micro- and nanoscale requires top-down energy-intensive processes, such as e-beam lithography, chemical etching, or anodization. While it is difficult to form silicon in aqueous solution, its oxide, silica, can easily be synthesized using sol–gel chemistry and nucleated onto templates with diverse shapes to create porous or continuous architectures. Here, we demonstrate that novel silica nanostructures can be synthesized via biomineralization, and that they can be reduced to silicon using magnesiothermal reduction. We selected three biotemplates to create silica structures with various aspect ratios and length scales. First, we use diatomaceous earth as a model silica material to optimize our process, and we also biomineralize silica onto two microorganisms, the high aspect ratio M13 bacteriophage, and the helical Spirulina major algae. During our process, the shape of the materials is preserved, resulting in silicon nanowires, nanoporous networks, spirals, and other micro- and nanostructures with high surface area. Our method provides an alternative for the creation of silicon nanostructures, using preformed silica synthesized in solution. The process could be extended to a broader range of microorganisms and metal oxides for the rational design of on-demand micro- and nanostructured metals. In addition, we show that the intrinsic composition of the biotemplates as well as their growth medium can introduce impurities that could potentially be used as dopants in the final silicon structures, and that could allow for tuning the composition of n-doped or p-doped biotemplated silicon for use as semiconducting building blocks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".