SINTESIS DAN KARAKTERISASI NANOKOMPOSIT Fe3O4/TiO2 BERBASIS PASIR BESI SUNGAI BATANGHARI
Bibliographic record
Abstract
Abstrak Telah dilakukan penelitian Fe3O4/TiO2 dengan menggunakan variasi perbandingan massa. Nanopartikel Fe3O4 dengan menggunakan metode kopresipitasi dan disintesis dengan TiO2 menggunakan metode solid state reaction. Tujuan dilakukannya penelitian ini mengetahui pengaruh perbandingan massa terhadap pengujian yang dilakukan. Variasi perbandingan massa yang digunakan yaitu 3:1, 2:1, 1:1, 1:2 dan 1:3. Karakterisasi yang dilakukan, adalah X-Ray Diffraction (XRD), Spektrofotometer UV-Vis dan Scanning Electron Microscopy (SEM). Dari hasil XRD diperoleh bahwa penambahan TiO2 tidak menunjukkan perubahan yang signifikan terhadap ukuran kristal dan cenderung didominasi oleh fasa TiO2. Hasil Spektrofotometer UV-Vis nilai energi gap Fe3O4 murni, TiO2 murni dan perbandingan massa 1:1 berturut-turut sebesar 3,67 eV, 3,36 eV dan 3,50 eV. Dapat ditarik kesimpulan bahwa Fe3O4 didoping dengan TiO2 akan diperoleh nilai energi gap Fe3O4/ TiO2 berada diantara energi gap Fe3O4 dengan TiO2. Hasil SEM diperoleh morfologi berbentuk bulat dengan persebaran partikel tidak merata sehingga terjadi aglomerasi. Selain itu, diperoleh ukuran partikel Fe3O4, TiO2 dan perbandingan massa 1:1 dengan ukuran rata-rata partikel berturut-turut sebesar 148,006 nm, 184,72 nm dan 181,79 nm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".