Intermolecular interactions of substituted benzenes on multi-walled carbon nanotubes grafted on HPLC silica microspheres and interaction study through artificial neural networks

Purified multi-walled carbon nanotubes (MWCNTs) grafted onto silica microspheres by gamma-radiation were applied as a HPLC stationary phase for investigating the intermolecular interactions between MWCNTs and substituted benzenes. The synthetic route, simple and not requiring CNTs derivatization, involved no alteration of the nanotube original morphology and physical–chemical properties. The affinity of a set of substituted benzenes for the MWCNTs was studied by correlating the capacity factor (k′) of each probe to its physico-chemical characteristics (calculated by Density Functional Theory). The correlation was found through a theoretical approach based on feedforward neural networks. This strategy was adopted because today these calculations are easily affordable for small molecules (like the analytes), and many critical parameters needed are not known. This might increase the applicability of the proposed method to other cases of study. Moreover, it was seen that the normal linear fit does not provide a good model. The interaction on the MWCNT phase was compared to that of an octadecyl (C18) reversed phase, under the same elution conditions. Results from trained neural networks indicated that the main role in the interactions between the analytes and the stationary phases is due to dipole moment, polarizability and LUMO energy. As expected for the C18 stationary phase correlation, is due to dipole moment and polarizability, while for the MWCNT stationary phase primarily to LUMO energy followed by polarizability, evidence for a specific interaction between MWCNTs and analytes. The CNT-based hybrid material proved to be not only a chromatographic phase but also a useful tool to investigate the MWCNT-molecular interactions with variously substituted benzenes

Preliminary results of investigations into the use of artificial neural networks for discriminating gas chromatograph mass spectra of remote samples

Neural networks trained using mass spectra data from the National Institute of Standards and Technology (NIST) are studied. The investigations also included sample data from the gas chromatograph mass spectrometer (GCMS) instrument aboard the Viking Lander, obtained from the National Space Science Data Center. The work performed to data and the preliminary results from the training and testing of neural networks are described. These preliminary results are presented for the purpose of determining the viability of applying artificial neural networks in discriminating mass spectra samples from remote instrumentation such as the Mars Rover Sample Return Mission and the Cassini Probe

Fast and versatile chromatography process design and operation optimization with the aid of artificial intelligence

Preparative and process chromatography is a versatile unit operation for the capture, purification, and polishing of a broad variety of molecules, especially very similar and complex compounds such as sugars, isomers, enantiomers, diastereomers, plant extracts, and metal ions such as rare earth elements. Another steadily growing field of application is biochromatography, with a diversity of complex compounds such as peptides, proteins, mAbs, fragments, VLPs, and even mRNA vaccines. Aside from molecular diversity, separation mechanisms range from selective affinity ligands, hydrophobic interaction, ion exchange, and mixed modes. Biochromatography is utilized on a scale of a few kilograms to 100,000 tons annually at about 20 to 250 cm in column diameter. Hence, a versatile and fast tool is needed for process design as well as operation optimization and process control. Existing process modeling approaches have the obstacle of sophisticated laboratory scale experimental setups for model parameter determination and model validation. For a broader application in daily project work, the approach has to be faster and require less effort for non-chromatography experts. Through the extensive advances in the field of artificial intelligence, new methods have emerged to address this need. This paper proposes an artificial neural network-based approach which enables the identification of competitive Langmuir-isotherm parameters of arbitrary three-component mixtures on a previously specified column. This is realized by training an ANN with simulated chromatograms varying in isotherm parameters. In contrast to traditional parameter estimation techniques, the estimation time is reduced to milliseconds, and the need for expert or prior knowledge to obtain feasible estimates is reduced

Artificial Neural Network for Fast and Versatile Model Parameter Adjustment utilizin PAT signals of Chromatography Processes for Process Control under Production Conditions

Preparative chromatography is a well-established operation in the chemical and biotechnology manufacturing. Chromatography achieves high separation performances but often has to deal with the yield versus purity trade-off as the optimization criterium regarding through-put. The initial trade-off is often disturbed by the well-known phenomenon of chromatogram shifts over process lifetime and has to be corrected by operators via adjustment of peak fraction cutting. Nevertheless, with regards to autonomous operation and batch to continuous processing modes, any advanced process control strategy is needed to identify and correct shifts from the optimal operation point automatically. Previous studies already presented solutions for batch-to-batch variance and process control options with the aid of rigorous physico-chemical process model-ling. These models can be implemented as distinct digital twins as well as statistical process op-eration data analysers. In order to utilize such models for advanced process control, the model parameters have to be up dated with aid of inline PAT data to describe the actual operational status. Also including any occurring operational change phenomenon and its relation to their physico-chemical root cause. Typical phenomena are fluid dynamic changes due to packing breakage, channelling or compression as well as mass transfer and phase equilibrium related separation performance decrease due to adsorbent ageing or feed and buffer composition changes. In order to track these changes an Artificial Neural Network (ANN) is trained in this work. The ANN training is in this first step based on the simulation results of a distinct and pre-viously experimentally validated process model. The model is implemented in the open source tool CasADi for python. This allows the implementation of interfaces to e.g. process control systems with relatively low effort. Therefore, PAT signals can easily incorporated for the suffi-cient adjustment of the process model for appropriate process control. Further steps would be the implementation of optimization routines based on the PAT and ANN predictions to derive optimal operation points with the model

Efficient hybrid modeling and sorption model discovery for non-linear advection-diffusion-sorption systems: A systematic scientific machine learning approach

This study presents a systematic machine learning approach for creating efficient hybrid models and discovering sorption uptake models in non-linear advection-diffusion-sorption systems. It demonstrates an effective method to train these complex systems using gradientbased optimizers, adjoint sensitivity analysis, and JIT-compiled vector Jacobian products, combined with spatial discretization and adaptive integrators. Sparse and symbolic regression were employed to identify missing functions in the artificial neural network. The robustness of the proposed method was tested on an in-silico data set of noisy breakthrough curve observations of fixed-bed adsorption, resulting in a well-fitted hybrid model. The study successfully reconstructed sorption uptake kinetics using sparse and symbolic regression, and accurately predicted breakthrough curves using identified polynomials, highlighting the potential of the proposed framework for discovering sorption kinetic law structures.Comment: Preprint paper to be submitted soon in Elsevier Journa