What absorbs at 810 cm⁻¹ in an FTIR spectrum?
A band near 810 cm⁻¹ can point to several functional groups. Below are the most likely assignments, ranked by how much published evidence supports each — every one traceable to literature (DOI) and cross-validated against our 130,000+ reference spectra and knowledge graph.
Backed by 8 cited sources
Mulige funksjonelle gruppe-tilordninger
| Funksjonell gruppe | Støttende fakta | Siterte kilder | Toppkonfidens |
|---|---|---|---|
| Alkene (C=C) | 2 | 1 | 0.9 |
| Siloxane (Si-O-Si) | 1 | 1 | 0.9 |
| Alkyl C-H | 1 | 1 | 0.9 |
| Silanol (Si-OH) | 1 | 1 | 0.9 |
| Silicon-oxygen (Si-O) | 1 | 1 | 0.9 |
| Thiocarbonyl | 1 | 1 | 0.8 |
Rangeringen gjenspeiler akkumulert litteraturbevis, ikke en enkelt autoritativ regel. Bekreft alltid mot prøvekonteksten din.
Litteratur bak disse tilordningene
-
konfidens 1.0
“Assigned to α-crystalline phase.”
Elashmawi 等 - 2007 - Spectroscopic and thermal investigations of poly(v DOI: 10.1002/crat.200610789 -
Silicon-oxygen (Si-O) konfidens 0.9
“Text: '810cm-1 was assigned to O-Si-O vibration mode of SiO2'”
Al-Harbi 等 - 2016 - Adsorption of Polyvinylpyrrolidone over the Silica DOI: 10.1155/2016/2417292 -
Silanol (Si-OH) konfidens 0.9
“Same sentence.”
Lin 等 - 2018 - Preparation and Characterization of Highly Ordered DOI: 10.3390/polym10080819 -
Alkyl C-H konfidens 0.9
“new peak at 810 attributed to -CH-”
Mondal 等 - 2018 - Tetrapolymer Network Hydrogels via Gum Ghatti-Graf DOI: 10.1021/acsomega.8b01218 -
Siloxane (Si-O-Si) konfidens 0.9
“Assigned to Si-O-Si vibration.”
Paun 等 - 2021 - Nanofiltration Composite Membranes Based on KIT-6 DOI: 10.3390/membranes11050300 -
Alkene (C=C) konfidens 0.9
“Characteristic peak for C=C.”
Sim 等 - 2012 - Design of Hard Coating Resin for In-mold Decoratio DOI: 10.7317/pk.2012.36.3.268 -
konfidens 0.9
“Characteristic peak of (meth)acrylate at 810 cm-1.”
Tran 等 - 2017 - Synthesis of Hydrogels with Antifouling Properties DOI: 10.3791/55426 -
konfidens 0.9
“Characteristic peak of matrix asphalt at 810 cm-1.”
Zhong 等 - 2022 - Artificial Neural Network (ANN) Modeling for Predi DOI: 10.3390/ma15238695
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