Synthesis, XRD, XRF, TGA/DTG/DTA/DSC, thermal conductivity, SEM, AFM, TEM, EDS, FTIR and NMR spectral studies of calcium silicate hydrate-polymer nanocomposites
Bibliographic record
Abstract
Thermal performance of materials is very important in many industries, ranging from pharmacy, battery and aerospace to the electronics and construction industries. Therefore, it is highly desired to use efficient and effective testing methods to measure the thermal properties of nanocomposites [1, 2]. Thermal properties, XRD, XRF, EDS, FTIR and NMR spectra, AFM, SEM and TEM results are very important parameters for characterization of materials ranging from gaseous through liquid to solid. Therefore, it is not surprising that many authors studied the thermal properties together with XRF, XRD, spectral and microscopic studies to characterize various materials [1-8].Nanocomposites are a new class of composites, that are particle-filled polymers for which at least one dimension of the dispersed particles is in the nanometer range. Calcium silicate hydrate (C-S-H)-and polymer based nanocomposites (C-S-HPN) have evoked intense research interests lately due to their unique characteristics and many commercial applications. Nanocomposites are reported to promote the thermal, mechanical, molecular barrier, flame retardant and corrosion protection properties based on the recently published results [1, 2].Various C-S-HPN were prepared by incorporating poly(vinyl alcohol) (PVA) and poly(acrylic acid) (PAA) into the inorganic layers of C-S-H during precipitation of quasicrystalline C-S-H from aqueous solution. The as synthesized C-S-HPN materials were characterized by EDS, FTIR and NMR spectroscopy, XRF, XRD, SEM, AFM, TEM, thermal conductivity, TGA, DTG, DTA and DSC. The XRD peaks of C-S-HPN suggest the intermediate organizations presenting both intercalation of PVA and PAA and exfoliation of C-S-H. The AFM micrographs of C-S-H, PVA, PAA and C-S-HPN with different PVA and PAA contents exhibit the significant differences in their morphologies. The effects of the material compositions on the thermal stability of various C-S-HPN along with PVA, PAA and C-S-H were studied by TGA, DTG, DTA and DSC. Three significant decomposition temperature ranges were observed on the TGA curves of all C-S-HPN. Thermal conductivity of PVA, C-S-H and C-S-HPN materials was studied in the temperature range 25-50 oC. The lowest thermal conductivity at 25 oC was observed for C-S-H; however, PVA exhibited the lowest thermal conductivity at 50 oC. C-S-HPN exhibited the highest thermal conductivity in both cases. However, the highest thermal conductivity increase was observed for C-S-H. The thermal conductivity from 25-50 oC increases are 7.03, 17.46 and 14.85 % for PVA, C-S-H and C-S-HPN materials, respectively.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".