Preparation of Nano-titanium Dioxide in Propane/Air Diffusion Flame
Bibliographic record
Abstract
［1］Ollis, D.F., Al-Ekabi, H.(Eds.). Photocatalytic Purification and Treatment of Water and Air, Proc. Of the lst Int. Conference on TiO2 Photocatalytic Purification and Treatment of Water and Air[C]. London, Ontario, Canada, Elsevier, Amsterdam, 1993.2-4. ［2］Goswami, D.Y., Trivedi, D.M., Block, S.S. Photocatalytic Purification and Treatment of Water and Air[J]. Transactions of the ASME, 1997,119:92. ［3］Pierre, A.C. Sol-gel Processing of Ceramic Powders[J]. Ceram, Bull., 1991, 70:1281. ［4］Shi, L.Y., Hua, B., Zhang, J.PL. Structure of Microemulsion and Application of W/O Microemulsion in The Preparation of Ultrafine Particles[J]. Funct. Mater., 1998, 29:136(In Chinese). ［5］Shi, L.Y., Li, C.Z., Fang, K.Y. Research Advance in Vapor-Phase Synthesis of Ultrafine Titania Particles[J]. Mater. Rev., 1998,12: 23(In Chinese). ［6］Zhu, W. and Pratsinis, S.E. Flame Synthesis of Nanosized Powders. in Nanotechnology, eds. G.-M. Chow and K.E. Gonsalves,ACS Symp. Ser. 1996,622:64. ［7］Formenti, M., Juillet, F., Meriaudeau, P., et.al. Colloid and Interface[J]. Science, 1972, 39:79.?? ［8］Pratsinis, S. E. Zhu, W.H. and Vemury, S. The Role of Gas Mixing in Flame Synthesis of Titania Powders[J].Powder Technology, 1996, 86( 1 ):87-93. ［9］Xie, H.Y. Ning, G.L., Mao, Z.Q., et. al. An Experimental and Theoretical Study on Titania Nanoparticles Synthesized by Flame CVD Method[J]. J. Process Eng., 2002, 2:183-186(In Chinese). ［10］Xie, H.Y. Ning, G..L. Zhang, D.W. A Kinetic Theory for the Growth of Nano-Particles in a Flame-CVD Process[J].Submitted to Powder Technology.?? ［11］Xiong, Y. Pratsinis, S. Gas Phase Production of Particles in Reactive Turbulent Flows [J]. J. Aerosol Sci., 1991,22:637. ［12］Machenzie, K. The Calcinations of Titania: the Effect of Additive on the Anatase-Rutile Transformation[J]. Trans. J. Br. Ceram.Soc., 1975,74:29. ［13］Friedlander, Smoke, S.K. Dust and Haze[A]. Wiley, New York, 1977. 234. ［14］Friedlander, S.K., UllmannHee, M., Jang D., et. al. Elastic
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".