Machine learning concept in de-spiking process for nuclear resonant vibrational spectra - automation using no external parameter

X-ray and nuclear spectroscopies to reveal the element specific oxidation states and electronic spin states for nanoparticulated manganese cyanidoferrates and analogs

In this publication, the potential non-gadolinium magnetic resonant imaging agent - the nanoparticulate K2Mn[Fe(CN)6], its comparison sample KFe[Co(CN)6], as well as their reference samples were measured and analyzed with Mn, Co and Fe L-edge X-ray absorption spectroscopy (L XAS). From the information obtained, we conclude that K2Mn[Fe (CN)6] has a high spin (hs)-Mn(II) and a low spin (ls)-Fe(II), while KFe[Co(CN)6] has a hs-Fe(II) and a ls-Co(III). In these Prussian blue (PB) analog structures, L XAS also concluded that the hs-Mn(II) in K2Mn[Fe(CN)6] or the hs-Fe(II) in KFe[Co(CN)6] is bonding to the N in the [M(CN)6]4-/3- ions [where M = Fe(II) or Co(III)], while the ls-Fe(II) in K2Mn[Fe(CN)6] or the ls-Co(III) in KFe[Co(CN)6] is bonding to the C in the [M(CN)6]4-/3- ion, suggesting the complexed metalloligand [Mn(II) or Fe(II)] occupy the N-bound site in PB. Then, nuclear resonant vibrational spectroscopy (NRVS) was used to confirm the results from the L XAS measurements: the Mn(II), Eu(III), Gd(III), Fe(II) cations complexed by [M(CN)6]n--metalloligand are all taking the N-bound site in the PB-like structures. Our NRVS studies also approve that iron in K2Mn[Fe(CN)6] compound has 2+ oxidation state and is surrounding by the C donor atoms in the [M(CN)6]n- ions.

Artificial Intelligence for Surface-Enhanced Raman Spectroscopy

Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.

Tracking energy scale variations from scan to scan in nuclear resonant vibrational spectroscopy: In situ correction using zero-energy position drifts ΔEi rather than making in situ calibration measurements

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 (ΔE_i), 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 (ΔE_i) and explored their link to the energy scales (α_i) 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 ΔE_i 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 (E_real). The "calibrated" real energy axis (E_real) has a preliminary error bar of ±0.1% (the 2σ_E divided by the vibrational energy position), which is 4-8 times better than that from the current practical energy calibration procedure.