TY - JOUR A1 - Martins, Ines A1 - Al-Sabbagh, Dominik A1 - Bentrup, U. A1 - Marquardt, Julien A1 - Schmid, Thomas A1 - Scoppola, E. A1 - Kraus, Werner A1 - Stawski, Tomasz A1 - de Oliveira Guilherme Buzanich, Ana A1 - Yusenko, Kirill A1 - Weidner, Steffen A1 - Emmerling, Franziska T1 - Formation Mechanism of a Nano-Ring of Bismuth Cations and Mono-Lacunary Keggin-Type Phosphomolybdate N2 - A new hetero-bimetallic polyoxometalate (POM) nano-ring was synthesized in a one-pot procedure. The structure consists of tetrameric units containing four bismuth-substituted monolacunary Keggin anions including distorted [BiO8] cubes. The nano-ring is formed via self-assembly from metal precursors in aqueous acidic medium. The compound (NH4)16[(BiPMo11O39)4] ⋅ 22 H2O; (P4Bi4Mo44) was characterized by single-crystal X-ray diffraction, extended X-ray absorption fine structure spectroscopy (EXAFS), Raman spectroscopy, matrix-assisted laser desorption/ionisation-time of flight mass spectrometry (MALDI-TOF), and thermogravimetry/differential scanning calorimetry mass spectrometry (TG-DSC-MS). The formation of the nano-ring in solution was studied by time-resolved in situ small- and wide-angle X-ray scattering (SAXS/WAXS) and in situ EXAFS measurements at the Mo−K and the Bi−L3 edge indicating a two-step process consisting of condensation of Mo-anions and formation of Bi−Mo-units followed by a rapid self-assembly to yield the final tetrameric ring structure. KW - Bismuth KW - In situ EXAFS KW - In situ SAXS/WAXS KW - Lacunary Keggin ion KW - Polyoxometalates KW - Self-assembly PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-546823 DO - https://doi.org/10.1002/chem.202200079 SN - 0947-6539 VL - 28 IS - 27 SP - 1 EP - 7 PB - Wiley-VCH CY - Weinheim AN - OPUS4-54682 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Fink, Friedrich A1 - Stawski, Tomasz M. A1 - Emmerling, Franziska A1 - Falkenhagen, Jana T1 - A novel machine-learning approach to unlock technical lignin classification by NIR spectroscopy - bench to handheld N2 - In this research, the utilization of near-infrared (NIR) spectroscopy in conjunction with advanced machine learning methods is investigated for categorizing technical lignins obtained from different biomass sources and industrial procedures. Technical lignins, such as kraft, organosolv and lignosulfonates, have different chemical compositions, which continue to make uniform characterization and application in sustainable sectors extremely difficult. Fast, universally accessible analytics combined with data analysis is still an open question. For the first time three distinct NIR spectrometers—a high-performance benchtop system, a mid-priced compact device, and an economical handheld unit—were utilized to record NIR spectra of 31 unique lignin samples. The spectra underwent pre-processing through standard normal variate (SNV) transformation and Savitzky-Golay derivatives to amplify spectral features and decrease noise. Principal component analysis (PCA) was employed to reduce data complexity and extract crucial characteristics for classification purposes. Subsequently, four machine learning algorithms—Support Vector Machines (SVM), Gaussian Naive Bayes (GNB), Gaussian Process Classification (GPC), and Decision Tree Classification (DTC)—were implemented for the classification of the lignin samples. The DTC model exhibited the highest accuracy among them across different spectrometers. Although the benchtop spectrometer produced the most precise outcomes, the compact NeoSpectra system also displayed potential as a cost-efficient option. Nonetheless, the restricted spectral coverage of the handheld NIRONE spectrometer resulted in reduced classification accuracy. Our discoveries highlight the capability of NIR spectroscopy, combined with robust data analysis techniques, for the swift and non-destructive classification of technical lignins, facilitating their improved utilization in sustainable fields. KW - Technical lignins KW - NIR spectroscopy KW - Classification KW - Machine-learning KW - PCA PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-634518 DO - https://doi.org/10.1016/j.chemolab.2025.105467 SN - 0169-7439 VL - 264 SP - 1 EP - 10 PB - Elsevier B.V. CY - Amsterdam AN - OPUS4-63451 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -