Continuous sonochemical nanotransformation of lignin - Process design and control.

As the most abundant renewable aromatic polymer on the planet, lignin is gaining growing interest in replacing petroleum-based chemicals and products. However, only <5 % of industrial lignin waste is revalorized in its macromolecular form as additives, stabilizing agents or dispersant and surfactants. Herein, revalorization of this biomass was achieved by implementing an environmentally-friendly continuous sonochemical nanotransformation to obtain highly concentrated lignin nanoparticles (LigNPs) dispersions for added-value material applications. With the aim to further model and control a large-scale ultrasound-assisted lignin nanotransformation, a two-level factorial design of experiment (DoE) was implemented varying the ultrasound (US) amplitude, flow rate, and lignin concentration. Size and polydispersity measurements together with the UV-Vis spectra of lignin recorded at different time intervals of sonication allowed to monitor and understand the sonochemical process on a molecular level. The light scattering profile of sonicated lignin dispersions showed a significant particle size reduction in the first 20 min, followed by moderate particle size decrease below 700 nm until the end of the 2 h process. The response surface analysis (RSA) of the particle size data revealed that the lignin concentration and sonication time were the most important factors to achieve smaller NPs. From a mechanistic point of view, a strong impact of the particle-particle collisions due to sonication seems to be responsible for the decrease in particle size and homogenization of the particle distribution. Unexpectedly, a strong interaction between the flow rate and US amplitude on the particle size and nanotransformation efficiency was observed, yielding smaller LigNPs at high amplitude and low flow rate or vice versa. The data derived from the DoE were used to model and predict the size and polydispersity of the sonicated lignin. Furthermore, the use of the NPs spectral process trajectories calculated from the UV-Vis spectra showed similar RSA model as the dynamic light scattering (DLS) data and will potentially allow the in-line monitoring of the nanotransformation process.

Prediction of Residual Curing Capacity of Melamine-Formaldehyde Resins at an Early Stage of Synthesis by In-Line FTIR Spectroscopy.

Melamine-formaldehyde (MF) resins are widely used as surface finishes for engineered wood-based panels in decorative laminates. Since no additional glue is applied in lamination, the overall residual curing capacity of MF resins is of great technological importance. Residual curing capacity is measured by differential scanning calorimetry (DSC) as the exothermic curing enthalpy integral of the liquid resin. After resin synthesis is completed, the resulting pre-polymer has a defined chemical structure with a corresponding residual curing capacity. Predicting the residual curing capacity of a resin batch already at an early stage during synthesis would enable corrective measures to be taken by making adjustments while synthesis is still in progress. Thereby, discarding faulty batches could be avoided. Here, by using a batch modelling approach, it is demonstrated how quantitative predictions of MF residual curing capacity can be derived from inline Fourier Transform infrared (FTIR) spectra recorded during resin synthesis using partial least squares regression. Not only is there a strong correlation (R2 = 0.89) between the infrared spectra measured at the end of MF resin synthesis and the residual curing capacity. The inline reaction spectra obtained already at the point of complete dissolution of melamine upon methylolation during the initial stage of resin synthesis are also well suited for predicting final curing performance of the resin. Based on these IR spectra, a valid regression model (R2 = 0.85) can be established using information obtained at a very early stage of MF resin synthesis.

Unravelling the Phases of Melamine Formaldehyde Resin Cure by Infrared Spectroscopy (FTIR) and Multivariate Curve Resolution (MCR).

Here, we study resin cure and network formation of solid melamine formaldehyde pre-polymer over a large temperature range via dynamic temperature curing profiles. Real-time infrared spectroscopy is used to analyze the chemical changes during network formation and network hardening. By applying chemometrics (multivariate curve resolution, MCR), the essential chemical functionalities that constitute the network at a given stage of curing are mathematically extracted and tracked over time. The three spectral components identified by MCR were methylol-rich, ether linkages-rich and methylene linkages-rich resin entities. Based on dynamic changes of their characteristic spectral patterns in dependence of temperature, curing is divided into five phases: (I) stationary phase with free methylols as main chemical feature, (II) formation of flexible network cross-linked by ether linkages, (III) formation of rigid, ether-cross-linked network, (IV) further hardening via transformation of methylols and ethers into methylene-cross-linkages, and (V) network consolidation via transformation of ether into methylene bridges. The presented spectroscopic/chemometric approach can be used as methodological basis for the functionality design of MF-based surface films at the stage of laminate pressing, i.e., for tailoring the technological property profile of cured MF films using a causal understanding of the underlying chemistry based on molecular markers and spectroscopic fingerprints.

Tumor cell differentiation by label-free fluorescence microscopy.

Autofluorescence spectra, images, and decay kinetics of U251-MG glioblastoma cells prior and subsequent to activation of tumor suppressor genes are compared. While phase contrast images and fluorescence intensity patterns of tumor (control) cells and less malignant cells are similar, differences can be deduced from autofluorescence spectra and decay kinetics. In particular, upon near UV excitation, the fluorescence ratio of the free and protein-bound coenzyme nicotinamid adenine dinucleotide depends on the state of malignancy and reflects different cytoplasmic (including lysosomal) and mitochondrial contributions. While larger numbers of fluorescence spectra are evaluated by principal component analysis, a multivariate data analysis method, additional information on cell metabolism is obtained from spectral imaging and fluorescence lifetime imaging microscopy.

Using scattering and absorption spectra as MCR-hard model constraints for diffuse reflectance measurements of tablets.

One of the most often used tools in process analytical technology (PAT) is NIR spectroscopy as a non-destructive fast and reliable method to identify and quantify active pharmaceutical ingredients (API) in tablets. Very little work has been devoted to analyse the effects of scatter on quantitative analysis of the chemical composition. A novel approach to compensate scatter in reflectance spectroscopy which is more science based will be presented here. The basic assumption is to determine in step 1 a separate scattering spectral fingerprint, denoted as S spectra, and an absorption spectral fingerprint, denoted as K spectra. In the second step, the two spectra may then be used as input to the alternating least square (ALS) algorithm in multivariate curve resolution (MCR) in order to account for the spectral distortions due to the interaction of scatter and absorption. Standard tablets with a mass of 1.5 g and a diameter of 20 mm (thickness approx. 3.4 mm, optically infinite) were prepared according to a central composite design by mixing theophylline, magnesium stearate and cellactose at three different compactions of 31, 156 and 281 MPa. The samples are measured by an UV/Vis/NIR spectrometer attached with an integrating sphere in the wavelength range from 500 up to 2100 nm. The diffuse reflectance spectra of the center point sample with an optically infinite thickness R(infinity) as well as a sample of finite thickness R(0) ("optically thin") is measured as reference for the S and K spectra which are then calculated with the exponential solution of the Kubelka-Munk equation. After normalization, the S spectrum and the K spectrum of a single tablet are integrated as hard model constraints into the MCR-ALS procedure. In comparison to PLS modeling with EMSC pretreatment of the spectra, the hard model constrained MCR-ALS algorithm results in an improved prediction of the concentration of the API together with a higher robustness of the calibration models.

Multivariate curve resolution of spectrophotometric data for the determination of artificial food colors.

In the analysis of food additives, past emphasis was put on the development of chromatographic techniques to separate target components from a complex matrix. Especially in the case of artificial food colors, direct spectrophotometric measurement was seen to lack in specificity due to a high spectral overlap between different components. Multivariate curve resolution (MCR) may be used to overcome this limitation. MCR is able to (i) extract from a complex spectral feature the number of involved components, (ii) attribute the resulting spectra to chemical compounds, and (iii) quantify the individual spectral contributions with or without a priori knowledge. We have evaluated MCR for the routine analysis of yellow and blue food colors in absinthe spirits. Using calibration standards, we were able to show that MCR equally performs as compared to partial least-squares regression but with much improved chemical information contained in the predicted spectra. MCR was then applied to an authentic collective of different absinthes. As confirmed by reference analytics, the food colors were correctly assigned with a sensitivity of 0.93 and a specificity of 0.85. Besides the artificial colors, the algorithm detected a further component in some samples that could be assigned to natural coloring from chlorophyll.