Porous Silicon Sensors - from Single Layers to Multilayer Structures
Bibliographic record
Abstract
Silicon, one of the more common elements in nature, is defined as a metalloid, which corresponds to the number 14 in the Mendeleyev Periodic Table . It is heavier than Carbon (element number 6 in the periodic table and a key component in biochemistry), but both have chemical characteristics that are very close. Since human civilization began, volcanic stones containing this metalloid in the form of dioxide were used to create the first tools and weapons. Roman historian Plinio the Elder (23AD-79AD) mentioned the Silex-Silicis (silicon stones) in one of his works as very hard stones. These roman words are the Latin origin of the name Silicon J.J.Berzelius was credited for the discovery of this element in 1824 in Stockholm, Sweden, but Gay-Lussac and Thenard had already prepared impure amorphous silicon by 1811. After World War II, once the applied mechanical technology was ready to produce very pure silicon wafers (under the form of monolithic crystals) and succeeded to manage the problem of the surface impurities and contamination In summary, it is safe to say that Silicon's role along our evolution extends from prehistoric times to the exploration of the Solar System. In 1956 at the U.S. Bell Laboratories, Arthur Uhlir Jr. and Ingeborg Uhlir while trying a new technique for polishing Silicon crystalline wafers observed for the first time a red-green film formed on the wafer surface Since the discovery of its luminescence properties by Leigh Canham in 1990 This effort has permitted the fabrication of uniform porous layers with diameters as small as one nanometer, and permitting an enormous inner surface density.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".