What absorbs at 1392 cm⁻¹ in an FTIR spectrum?
A band near 1392 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 5 cited sources
Möjliga identifieringar av funktionella grupper
| Funktionell grupp | Stödjande fakta | Citerade källor | Högsta förtroende |
|---|---|---|---|
| Carboxyl (COOH) | 2 | 2 | 1,0 |
| Amide | 1 | 1 | 1,0 |
| N n bond | 1 | 1 | 1,0 |
| N-O bond | 1 | 1 | 1,0 |
| Carbohydrate | 1 | 1 | 1,0 |
Rankingen speglar ackumulerade litteraturbevis, inte en enda auktoritativ regel. Bekräfta alltid mot din provkontext.
Litteratur bakom dessa identifieringar
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Carbohydrate förtroende 1,0
“The vibration 1392 is assigned to C stretching of carbohydrates, fatty acids, or amino acid side chains.”
Fourier transform infrared microspectroscopy detects biochemical changes during C. elegans lifespan DOI: 10.1016/j.vibspec.2019.04.005 -
Carboxyl (COOH) förtroende 1,0
“Sodium salicylate 1597cm-1 The peak at 1392 and are due to the s(-COOas(-COOν ν ) and ) vibrations (Fig.”
Das 等 - 2004 - Kinetics and adsorption behaviour of salicylate on DOI: 10.1016/j.colsurfa.2004.02.010 -
N-O bond förtroende 1,0
“There cm-1, 235 is no signal can be seen on blank 4-ATP capped mHNTs-AgNPs at 1143 1392 cm-1 cm-1”
4-Aminothiophenol capped Halloysite Nanotubes/Silver Nanoparticles as Surface-enhanced Raman Scattering Probe for In-situ Derivatization and Selective Determination of Nitrite Ions in Meat Product DOI: 10.1016/j.talanta.2020.121366 -
Carboxyl (COOH) förtroende 1,0
“The two intense bands at 1580 and 1392 cm-1 are attributed to asymmetric and symmetric stretching vibrations of terephthalate linker carboxylate groups, respectively”
Butova 等 - 2023 - In Situ FTIR Spectroscopy for Scanning Accessible DOI: 10.3390/nano13101675 -
Amide förtroende 1,0
“LLM confirmed rule peak-group candidate”
Development of novel spectroscopic and machine learning methods for the measurement of periodic changes in COVID-19 antibody level DOI: 10.1016/j.measurement.2022.111258
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