Toward a digital polymer reaction engineering

Lazzari, Stefano; Lischewski, Andree; Orlov, Yury; Deglmann, Peter; Daiss, Andreas; Schreiner, Eduard; Vale, Hugo M.

doi:10.1016/bs.ache.2020.07.001

Cited by 14 publications

(11 citation statements)

References 94 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Virtual spectrometry changes this convenient role of the molecular models, shifting the emphasis from them as representatives of the substance under study only while placing them in the position of independent objects. This circumstance logically places virtual spectrometry on par with the processes of overall digitalization, being the general trend of the current modern development (see reviews [6][7][8]). Developed independently, this accelerating trend is based on a broad concept of digital twins (DTs), which are defined as virtual representations of physical assets enabled through data and simulators for realtime prediction, optimization, monitoring, controlling, and improved decision making [7].…”

Section: Introductionmentioning

confidence: 99%

Virtual Vibrational Spectrometry of Stable Radicals—Necklaced Graphene Molecules

Sheka

2022

Nanomaterials

View full text Add to dashboard Cite

The article presents results of an extended virtual experiment on graphene molecules performed using the virtual vibrational spectrometer HF Spectrodyn that exploits semiempirical Hartree-Fock approximation. The molecules are composed of flat graphene domains surrounded with heteroatom necklaces. Not existing individually, these molecules are met in practice as basic structure units of complex multilevel structure of all sp2 amorphous carbons. This circumstance deprives the solids’ in vitro spectroscopy of revealing the individual character of basic structural elements, and in silico spectrometry fills this shortcoming. The obtained virtual vibrational spectra allow for drawing first conclusions about the specific features of the vibrational dynamics of the necklaced graphene molecules, caused by spatial structure and packing of their graphene domains as well as by chemical composition of the relevant necklaces. As shown, IR absorption spectra of the molecules are strongly necklace dependent, once becoming a distinct spectral signature of the amorphous body origin. Otherwise, Raman spectra are a spectral mark of the graphene domain’s size and packing, thus disclosing the mystery of their universal D-G-band standard related to graphene-containing materials of various origins.

show abstract

Section: Introductionmentioning

confidence: 99%

Virtual Vibrational Spectrometry of Stable Radicals—Necklaced Graphene Molecules

Sheka

2022

Nanomaterials

View full text Add to dashboard Cite

show abstract

“…An overview of the potential of QCC for solvent and concentration dependencies of propagation rate coefficients for aqueous monomers was recently given by Deglmann et al [ 200 , 225 , 226 ]. The authors claimed that the relative changes in rate upon transition from one medium were in good agreement with the experimental trends in cases where the COSMO-RS model was applied.…”

Section: Case Studies For Connection Of Computational Chemistry and Kinetic Modelingmentioning

confidence: 99%

Connecting Gas-Phase Computational Chemistry to Condensed Phase Kinetic Modeling: The State-of-the-Art

et al. 2021

View full text Add to dashboard Cite

In recent decades, quantum chemical calculations (QCC) have increased in accuracy, not only providing the ranking of chemical reactivities and energy barriers (e.g., for optimal selectivities) but also delivering more reliable equilibrium and (intrinsic/chemical) rate coefficients. This increased reliability of kinetic parameters is relevant to support the predictive character of kinetic modeling studies that are addressing actual concentration changes during chemical processes, taking into account competitive reactions and mixing heterogeneities. In the present contribution, guidelines are formulated on how to bridge the fields of computational chemistry and chemical kinetics. It is explained how condensed phase systems can be described based on conventional gas phase computational chemistry calculations. Case studies are included on polymerization kinetics, considering free and controlled radical polymerization, ionic polymerization, and polymer degradation. It is also illustrated how QCC can be directly linked to material properties.

show abstract

“…Polymer informatics has been applied to essentially every aspect of the polymer lifecycle. It has been used to design new monomers for various applications 12,15,16 ; engineer reactions 17 ; model processing conditions and parameters [18][19][20] ; identify and predict polymer conformations and phases [21][22][23][24][25][26] ; predict materials properties [27][28][29][30][31][32][33][34][35] ; and finally offer insight into wear and end of life. 4,[36][37][38][39] Most polymer informatics literature focuses on property prediction, but recently other aspects of polymer synthesis, processing and lifetime have been gaining attention.…”

Section: Introductionmentioning

confidence: 99%

“…4,[36][37][38][39] Most polymer informatics literature focuses on property prediction, but recently other aspects of polymer synthesis, processing and lifetime have been gaining attention. 14,17,40 There are still many areas ripe for an informatics approach, such as designing for longer term stability or circular economies.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for polymeric materials: an introduction

Cencer

Moore

Assary

2021

Polymer International

View full text Add to dashboard Cite

Polymers are incredibly versatile materials and have become ubiquitous. Increasingly, researchers are using data science and polymer informatics to design new materials and understand their structure–property relationships. Polymer informatics is an emerging field. While there are many useful tools and databases available, many are not widely utilized. Herein, we introduce the field of polymer informatics and discuss some of the available databases and tools. We cover how to share polymer data, approaches for preparing a dataset for machine learning and recent applications of machine learning to polymer property prediction and polymer synthesis. © 2021 Society of Industrial Chemistry.

show abstract

Toward a digital polymer reaction engineering

Cited by 14 publications

References 94 publications

Virtual Vibrational Spectrometry of Stable Radicals—Necklaced Graphene Molecules

Virtual Vibrational Spectrometry of Stable Radicals—Necklaced Graphene Molecules

Connecting Gas-Phase Computational Chemistry to Condensed Phase Kinetic Modeling: The State-of-the-Art

Machine learning for polymeric materials: an introduction

Contact Info

Product

Resources

About