Answers are grounded in this paper and cite their sources.
Nuclear resonant vibrational spectroscopy (NRVS) is an excellent modern vibrational spectroscopy, in particular, for revealing site-specific information inside complicated molecules, such as enzymes. There are two different concepts about the energy calibration for a beamline or a monochromator (including a high resolution monochromator): the absolute energy calibration and the practical energy calibration. While the former pursues an as-fine-as-possible and as-repeatable-as-possible result, the latter includes the environment influenced variation from scan to scan, which often needs an in situ calibration measurement to track. However, an in situ measurement often shares a weak beam intensity and therefore has a noisy NRVS spectrum at the calibration sample location, not leading to a better energy calibration/correction in most cases. NRVS users for a long time have noticed that there are energy drifts in the vibrational spectra’s zero-energy positions from scan to scan (ΔEi), but their trend has not been explored and utilized in the past. In this publication, after providing a brief introduction to the critical issue(s) in practical NRVS energy calibrations, we have evaluated the trend and the mechanism for these zero-energy drifts (ΔEi) and explored their link to the energy scales (αi) from scan to scan. Via detailed analyses, we have established a new stepwise procedure for carrying out practical energy calibrations, which includes the correction for the scan-dependent energy variations using ΔEi values rather than running additional in situ calibration measurements. We also proved that one additional instrument-fixed scaling constant (α0) exists to convert such “calibrated” energy axis (E′) to the real energy axis (Ereal). The “calibrated” real energy axis (Ereal) has a preliminary error bar of ±0.1% (the 2σE divided by the vibrational energy position), which is 4–8 times better than that from the current practical energy calibration procedure.
No takes yet. Share an insight, caveat, or question.
Wang et al. (2022) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: