A diagnostic procedure for improving the structure of approximated kinetic models

Kinetic models of chemical and biochemical phenomena are frequently built from simplifying assumptions. Whenever a model is falsified by data, its mathematical structure should be modified embracing the available experimental evidence. A framework based on maximum likelihood inference is illustrated in this work for diagnosing model misspecification and improving the structure of approximated models. In the proposed framework, statistical evidence provides a measure to justify a modification of the model structure, namely a reduction of complexity through the removal of irrelevant parameters and/or an increase of complexity through the replacement of relevant parameters with more complex state-dependent expressions. A tailored Lagrange multipliers test is proposed to support the scientist in the improvement of parametric models when an increase in model complexity is required

Model-based design of experiments in the presence of structural model uncertainty: an extended information matrix approach

The identification of a parametric model, once a suitable model structure is proposed, requires the estimation of its non-measurable parameters. Model-based design of experiment (MBDoE) methods have been proposed in the literature for maximising the collection of information whenever there is a limited amount of resources available for conducting the experiments. Conventional MBDoE methods do not take into account the structural uncertainty on the model equations and this may lead to a substantial miscalculation of the information in the experimental design stage. In this work, an extended formulation of the Fisher information matrix is proposed as a metric of information accounting for model misspecification. The properties of the extended Fisher information matrix are presented and discussed with the support of two simulated case studies

Aerosol mediated localized dissolution to enhance the electrical behavior and sensitivity of piezoresistive nanofiber-based flexible sensors

This work proposes the use of solvents in the form of small size droplets to improve the connections among nanofibers (NFs) in electrospun composite nanofibers with carbon nanotube multiwalled (MWCNT) by improving the electrical and piezoresistive behavior of such electrically conductive polymer composites. The here proposed Aerosol Mediated Localized Dissolution (AMLD) process has been shown to be effective in improving the 3D microporous NF mat by inducing local dissolution that is effective in improving the connections among fibers within the mat. The AMLD process is demonstrated here for polyethylene oxide (PEO) / MWCNTs composite nanofibers, showing that the electrical conductivity is particularly improved in those samples with low content of MWCNTs, even below the original percolation threshold. The improved electrical conductivity is coupled with exceptional sensitivity of the flex sensor for low MWCNTs contents, this is particularly due to the ability of the AMLD process to preserve the high surface area of the 3D mat by inducing better fiber-to-fiber contacts in few regions only. In addition, this work demonstrates the effectiveness of applying an electrical potential difference during the AMLD process to improve the alignment of MWCNTs within the 3D microporous NF mat. The combination of voltage and AMLD allow to obtain a gauge factor as high as 571.9 with a MWCNTs loading of 1 wt%

Aerogels for energy and environmental applications

Aerogels are emerging as one of the most intriguing and promising groups of microporous materials, characterized by impressive properties such as low density, high surface area, high porosity and tunable surface chemistry. Fostering unique thermal and acoustic insulation features, for several decades they mainly received attention from the aerospace and building sectors. More recently, new great opportunities arose due to significant advances in the drying technologies that currently, represent the enabling step for aerogel synthesis and fabrication. This process-ability dramatically increased the interest toward aerogels from new disciplines. This explains why in the last decade the Environmental Science and Energy fields significantly contributed to the expansion of the aerogel technology, suggesting novel uses and applications and contributing to extend the group of materials that can be synthetized by aerogel processing. New, unforeseen properties emerged for aerogel materials, such as adsorption of contaminants and fluids purification, catalysis of different reactions, electrical conductivity. The present short-review aims at providing a critical overview of the key advances in the development of aerogels for energy and environmental applications, especially emphasizing the common strategies and properties that are turning aerogels into one of the new key emerging technologies of these areas of science

Ferritin nanovehicle for targeted delivery of cytochrome C to cancer cells

In this work, we have exploited the unique properties of a chimeric archaeal-human ferritin to encapsulate, deliver and release cytochrome c and induce apoptosis in a myeloid leukemia cell line. The chimeric protein combines the versatility in 24-meric assembly and cargo incorporation capability of Archaeglobus fulgidus ferritin with specific binding of human H ferritin to CD71, the “heavy duty” carrier responsible for transferrin-iron uptake. Delivery of ferritin-encapsulated cytochrome C to the Acute Promyelocytic Leukemia (APL) NB4 cell line, highly resistant to transfection by conventional methods, was successfully achieved in vitro. The effective liberation of cytochrome C within the cytosolic environment, demonstrated by double fluorescent labelling, induced apoptosis in the cancer cells

A Multi-Objective Optimal Experimental Design Framework for Enhancing the Efficiency of Online Model-Identification Platforms

Recent advances in automation and digitization enable the close integration of physical devices with their virtual counterparts, facilitating the real-time modeling and optimization of a multitude of processes in an automatic way. The rich and continuously updated data environment provided by such systems makes it possible for decisions to be made over time to drive the process toward optimal targets. In many manufacturing processes, in order to achieve an overall optimal process, the simultaneous assessment of multiple objective functions related to process performance and cost is necessary. In this work, a multi-objective optimal experimental design framework is proposed to enhance the efficiency of online model-identification platforms. The proposed framework permits flexibility in the choice of trade-off experimental design solutions, which are calculated online—that is, during the execution of experiments. The application of this framework to improve the online identification of kinetic models in flow reactors is illustrated using a case study in which a kinetic model is identified for the esterification of benzoic acid (BA) and ethanol in a microreactor

IR ion spectroscopy in a combined approach with MS/MS and IM-MS to discriminate epimeric anthocyanin glycosides (cyanidin 3-O-glucoside and -galactoside)

Anthocyanins are widespread in plants and flowers, being responsible for their different colouring. Two representative members of this family have been selected, cyanidin 3-O-β-glucopyranoside and 3-O-β-galactopyranoside, and probed by mass spectrometry based methods, testing their performance in discriminating between the two epimers. The native anthocyanins, delivered into the gas phase by electrospray ionization, display a comparable drift time in ion mobility mass spectrometry (IM-MS) and a common fragment, corresponding to loss of the sugar moiety, in their collision induced dissociation (CID) pattern. However, the IR multiple photon dissociation (IRMPD) spectra in the fingerprint range show a feature particularly evident in the case of the glucoside. This signature is used to identify the presence of cyanidin 3-O-β-glucopyranoside in a natural extract of pomegranate. In an effort to increase any differentiation between the two epimers, aluminum complexes were prepared and sampled for elemental composition by FT-ICR-MS. CID experiments now display an extensive fragmentation pattern, showing few product ions peculiar to each species. More noteworthy is the IRMPD behavior in the OH stretching range showing significant differences in the spectra of the two epimers. DFT calculations allow to interpret the observed distinct bands due to a varied network of hydrogen bonding and relative conformer stability

Electrical conductivity modulation of crosslinked composite nanofibers based on PEO and PEDOT:PSS

The aim of this work is to investigate the development of nanofiber mats, based on intrinsically conductive polymers (ICPs), which show simultaneously a high electrical conductivity and mandatory insoluble water properties. In particular, the nanofibers, thanks to their properties such as high surface area, porosity, and their ability to offer a preferential pathway for electron flow, play a crucial role to improve the essential characteristics ensured by ICPs. The nanofiber mats are obtained by electrospinning process, starting from a polymeric solution made of polyethylene oxide (PEO) and poly(styrene sulfonate) (PEDOT:PSS). PEO is selected not only as a dopant to increase the electrical/ionic conductivity, as deeply reported in the literature, but also to ensure the proper stability of the polymeric jet, to collect a dried nanofiber mat. Moreover, in the present work, two different treatments are proposed in order to induce crosslinking between PEO chains and PEDOT:PSS, made insoluble into water which is the final sample. The first process is based on a heating treatment, conducted at 130°C under nitrogen atmosphere for 6 h, named the annealing treatment. The second treatment is provided by UV irradiation that is effective to induce a final crosslinking, when a photoinitiator, such as benzophenone, is added. Furthermore, we demonstrate that both crosslinking treatments can be used to verify the preservation of nanostructures and their good electrical conductivity after water treatment (i.e., water resistance). In particular, we confirm that the crosslinking method with UV irradiation results to being more effective than the standard annealing treatment. Indeed, we demonstrate that the processing time, required to obtain the final crosslinked nanofiber mats with a high electrical conductance, results to being smaller than the one needed during the heating treatment

Closed-Loop Model-Based Design of Experiments for Kinetic Model Discrimination and Parameter Estimation: Benzoic Acid Esterification on a Heterogeneous Catalyst

An autonomous reactor platform was developed to rapidly identify a kinetic model for the esterification of benzoic acid with ethanol with the heterogeneous Amberlyst-15 catalyst. A five-step methodology for kinetic studies was employed to systematically reduce the number of experiments required to identify a practical kinetic model. This included (i) initial screening using traditional factorial designed steady-state experiments, (ii) proposing and testing candidate kinetic models, (iii) performing an identifiability analysis to reject models whose model parameters cannot be estimated for a given experimental budget, (iv) performing online Model-Based Design of Experiments (MBDoE) for model discrimination to identify the best model from a list of candidates, and (v) performing online MBDoE for improving parameter precision for the chosen model. This methodology combined with the reactor platform, which conducted all kinetic experiments unattended, reduces the number of experiments and time required to identify kinetic models, significantly increasing lab productivity

An artificial neural network approach to recognise kinetic models from experimental data

The quantitative description of the dynamic behaviour of reacting systems requires the identification of an appropriate set of kinetic model equations. The selection of the correct model may pose substantial challenges as there may be a large number of candidate kinetic model structures. In this work, a model selection approach is presented where an Artificial Neural Network classifier is trained for recognising appropriate kinetic model structures given the available experimental evidence. The method does not require the fitting of kinetic parameters and it is well suited when there is a high number of candidate kinetic mechanisms. The approach is demonstrated on a simulated case study on the selection of a kinetic model for describing the dynamics of a three-component reacting system in a batch reactor. The sensitivity of the approach to a change in the experimental design and to a change in the system noise is assessed