Nanostructured Indium Oxide Coated Silicon Nanowire Arrays: A Hybrid Photothermal/Photochemical Approach to Solar Fuels
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Bibliographic record
Abstract
The field of solar fuels seeks to harness abundant solar energy by driving useful molecular transformations. Of particular interest is the photodriven conversion of greenhouse gas CO2 into carbon-based fuels and chemical feedstocks, with the ultimate goal of providing a sustainable alternative to traditional fossil fuels. Nonstoichiometric, hydroxylated indium oxide nanoparticles, denoted In2O3-x(OH)y, have been shown to function as active photocatalysts for CO2 reduction to CO via the reverse water gas shift reaction under simulated solar irradiation. However, the relatively wide band gap (2.9 eV) of indium oxide restricts the portion of the solar irradiance that can be utilized to ∼9%, and the elevated reaction temperatures required (150-190 °C) reduce the overall energy efficiency of the process. Herein we report a hybrid catalyst consisting of a vertically aligned silicon nanowire (SiNW) support evenly coated by In2O3-x(OH)y nanoparticles that utilizes the vast majority of the solar irradiance to simultaneously produce both the photogenerated charge carriers and heat required to reduce CO2 to CO at a rate of 22.0 μmol·gcat(-1)·h(-1). Further, improved light harvesting efficiency of the In2O3-x(OH)y/SiNW films due to minimized reflection losses and enhanced light trapping within the SiNW support results in a ∼6-fold increase in photocatalytic conversion rates over identical In2O3-x(OH)y films prepared on roughened glass substrates. The ability of this In2O3-x(OH)y/SiNW hybrid catalyst to perform the dual function of utilizing both light and heat energy provided by the broad-band solar irradiance to drive CO2 reduction reactions represents a general advance that is applicable to a wide range of catalysts in the field of solar fuels.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it