Semi-Batch Homogeneous Catalytic In-Situ Spectroscopic Data. FTIR Spectral Reconstructions Using Band-Target Entropy Minimization (BTEM) without Spectral Preconditioning

Abstract

In the preceding paper in this issue, the concept of band-target entropy minimization (BTEM) was introduced, and it was successfully applied to spectral reconstruction from a stoichiometric organometallic reaction system after spectral preconditioning. In this contribution, the BTEM algorithm is reapplied to semi-batch homogeneous catalytic reactions without spectral preconditioning. The homogeneous catalytic hydroformylation of 3,3-dimethylbut-1-ene, starting with Rh4(σ-CO)9(μ-CO)3 as catalyst precursor in n-hexane as solvent, was studied at 298 K and variable total pressure, using high pressure in situ infrared spectroscopy as the analytical tool. The non-preconditioned data were then subjected to BTEM in order to recover the pure component spectra of the species present. The pure component spectra of background moisture and carbon dioxide, hexane, dissolved CO in hexane, and the dissolved species present, namely the organic reactant 3,3-dimethylbut-1-ene, the organic product 4,4-dimethylpentanal, the catalyst precursor Rh4(σ-CO)9(μ-CO)3, the observable organometallic intermediate RCORh(CO)4, and Rh6(CO)16, were all readily recovered. An unexpected finding was a very well resolved spectrum with two terminal CO vibrations centered at 2068 and 2076 cm-1 (almost exactly overlapping with Rh4(σ-CO)9(μ-CO)3, but without bridging carbonyls). With reasonable certainty we are assigning this new spectrum to the previously unknown complex Rh4(σ-CO)12. The results indicate that spectral reconstruction, using no libraries and no a priori information, is indeed possible from semi-batch runs. This finding holds promise for rapid and cost-effective spectroscopic system identification of reactive organometallic and homogeneous catalytic systems.

Decomposition of in vivo spatially offset Raman spectroscopy data using multivariate analysis techniques

“…The MCR analysis was performed using the commercially avail- The BTEM [22,23] decomposition was performed using a programme that was written in-house. For the analysis, the data was combined into the same large 'X,Y,Y,Y…' file as with the MCR (i.e.…”

“…For these systems, more sophisticated methods, such as library-based fitting, need to be employed. [20] Another approach is to use multivariate analysis; band-target entropy minimization (BTEM), [21][22][23] coupled with SORS, has been used to measure the carbonate-to-phosphate ratio of rat tibia through 1 mm of skin (with an error of 2.3%), [12] of chicken tibia through 4 mm of skin/soft tissue (with an error of 8%) [12] and of porcine bone through~1 mm of soft tissue. [19] In the latter study, a second spectral decomposition method, parallel factor analysis (PARAFAC) [24] , was also used to retrieve the bone spectrum.…”

Recent advances in the development of Raman spectroscopy for deep non‐invasive medical diagnosis

“…at zero and non-zero spatial offsets) [1] scaled relative to each other to cancel the residual contribution from the surface layer present in the non-zero spectrum. For a multilayer sample or a sample with unknown number of layers processing would involve multivariate techniques [1,7], such as band targeted entropy minimization (BTEM) [8,9]. In both the cases the processing can be automated.…”

Application of FT‐Raman and FTIR measurements using a novel spectral reconstruction algorithm

“…For the analysis of˛,ω-dicarboxylic acids, a newly developed algorithm known as band-target entropy minimisation (BTEM) was utilized. 11,12 This method has a similar nature to the above-mentioned approach except that instead of resolving all pure component spectra simultaneously, the BTEM algorithm resolves the pure spectra one at a time. Briefly, in the BTEM method, one targets a spectral feature observed in the basis vectors which is of interest and which should be retained in the subsequent spectral reconstruction.…”