Polymer Workshop

09.06.2022 - 10.06.2022

Agenda - Day 1

2

Agenda - Day 2

3

Why do we need to make our data FAIR?

Steffen Neumann (IPB Halle)

09.06.2022

NFDI4Chem’s Vision

5

Standards

Community

Digitisation

Experiment Design

Experiment/ Data Collection

Data

Processing

Analysis

Electronic Lab Notebooks

Repositories

Data Life Cycle

FAIR data�

6

Aim: Improve data availability and quality

​

Not FAIR and Not Open:

a lot of information is lost.

Relation experiment ↔ data not clear.

​

The availability of data depends strongly

on the good will of the individual scientist

from: Auke Herrema – Het Bouwteam

wikimedia.org/wiki/File:FAIR_data_principles.jpg, 10.1038/sdata.2016.18

Findable

Accessible

Interoperable

Re-usable (Reproducible)

​

FAIR is not a buzzword, it’s a mind set !

• Findable: Easy to locate, using a unique and persistent identifier.�Descriptive metadata to enable discovery.
• Accessible: Easily retrieved by machines and humans�using standard protocols, with metadata, and archived long-term.
• Interoperable: Available in formats that can be exchanged, interpreted and combined, including metadata.
• Reusable: Metadata ensures re-use for future research,�with clear license and provenance, following community standards.

7

Adapted from: The FAIR Data Principles for Research Data

https://blogs.tib.eu/wp/tib/2017/09/12/the-fair-data-principles-for-research-data/

�

A special generic repository:

https://www.radar-service.eu/

• Format-independent preservation with metadata
• “The long tail of science”
• Dataset has a DOI
• Highly reliable Germany-based �multi-datacenter (Karlsruhe + Dresden)
• Institution needs to be contract member�Not in RADAR4Chem !

+

NFDI4Chem supports data publication

• We can support at different levels:
• Consulting service
• More intense data stewardship and collaboration
• Support to ...
• find open (Raw) data formats
• make tables and data behind figures machine readable
• choose appropriate repositor(y|ies)
• Could be from a current or future publication
• Get in touch via
• NFDI4Chem HelpDesk
• Provide information in this form: ogy.de/o5pc

9

NFDI4Chem Workshop series on RDM

Hungry for more information?

10

Best Practice

Flagship Labs

Data Organisation

Data Preservation

Data Management Plan

Storage

Electronic Lab Journals

Backup

Terminology

Metadata

Persistent Identifiers

Legal Aspects

Copyright

File Naming

Machine-�Readability

Ontology

Archiving

Data Versioning

Creative Commons

For more information, please register via this link

The Minimum Information on Chemistry Investigations (MIChI) Process(es)

S. Neumann (IPB Halle) with slides by C. Kettner (Beilstein Institute), 24.05.2022

Minimum Information Metadata

Domain Independent Metadata (DIM)

• Harmonisation of DIM across DataSets
• Compliance with DataCite schema in repositories.

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Minimum Information Metadata

Domain Specific Minimum Information (MI)

• Some call it MI, some Metadata, we call it MIChI: �Minimum Information on Chemistry Investigation
• Development of several components (see next slide)
• Dissemination in Manuscripts, �Knowledgebase, Software and Workshops

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screenshot-193.196.38.77-2021.07.08-16_22_45.png

armetPackages.png

10.1007/s11306-007-0082-2

10.1007/s11306-007-0082-2

Domain Specific MI: guideline article

• Guideline article is aimed at the broader scientific community
• introduces the minimum information guidelines
• its scope and coverage and the overall content
• publish in a repository or a journal of its target audience
• Supports writing good scientific manuscripts
• Reading material for early PhDs (and up !)
• A slidedeck (maybe even a recorded talk) �can accompany the article

15

Domain Specific MI: checklist

• A checklist is shorter
• Focus: list actual information to be reported
• possibly with brief explanation and examples
• published with open access license, e.g. on a repository
• Initial version of the checklist should be discussed �possibly added as SI in the guideline article
• Can be referenced from, or imported to, �author guidelines of scientific journals

​

16

Domain Specific MI: data model

• The data model is aimed at software and infrastructure developers
• implement software or data exchange formats to create, manage, validate, publish and archive Polymer Chemistry research data
• Model all objects and relations relevant to describe the data
• The “article & checklist” informs �what’s relevant here

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17

Domain Specific data: ontology terms

• Ontology Terms define concepts and relations the community agreed upon
• cover topics relevant in checklist & data model
• preferably in established (only if needed: newly created) ontologies
• Within the checklist ontologies serve as recommended vocabulary

More Material:

• knowledgebase.nfdi4chem.de/.../ontology/ �(incl. youtu.be/b5c2oawLPU8, 4 minutes)
• Past NFDI4Chem Joint Webinar Ontologies�(recording youtu.be/rGCoD-U4o7k caveat: 1 hour)
• The landscape of ontologies in chemistry �(10.1515/pac-2021-2007)

​

​

18

Domain Specific data: Implementation(s)

• One or more reference implementations for MI guidelines
• Ensures sufficiency, practicability and further adoption
• During specification process, the developers give feedback on specification documents while implementing prototypes
• Software, APIs, databases, user interfaces or data exchange formats
• initially, can be mockups
• Should support adhering to �the Guideline & Checklists
• Must not conflict with them ...

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​

​

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screenshot-193.196.38.77-2021.07.08-16_22_45.png

armetPackages.png

10.1007/s11306-007-0082-2

10.1007/s11306-007-0082-2

Topics / Questions during the workshop

• Session 1: Structure of Information/entities in polymer chemistry �=> Data model
• Session 2: Molecular structure and definitions => Implementations
• Session 3-5: mix of Data model + Implementation discussions

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Some things to keep in mind:

• Structured data is great for databases, searching and machine learning
• Structured data might be tedious to capture, and does not come for free
• Getting the right balance is key !

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21

Where we are - examples from scientists

Nicole Jung (KIT)

09.06.2022

Smart Lab - Seamless Data Flows

Electronic Lab Notebook

Publication

Documentation

Image-Source: Johannes Liermann, preparation of DFG-defense

Gif-Source: https://gifer.com/en/PsKr

Acquisition

Journals (Description/Info)

Repositories (Data)

Smart Lab

Research data and publications

24

https://doi.org/10.14272/reaction/SA-FUHFF-UHFFFADPSC-SPSSDDOTEZ-UHFFFADPSC-NUHFF-NUHFF-NUHFF-ZZZ.1

1

Supplemental

Information Part 1

2

Research Data in

Repository

Research data and publications

25

2

Research Data in

Repository

Smart Lab – From ELN to Repository

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From decentralized ELN-instances to central data collections on a repository

Nicole Jung – RDM with Chemotion Electronic Lab Notebook

Where we are - example for organic chemistry

RDM and data flow concepts

27

• Data flow concepts are established
• Processes to transfer data from smart lab to repositories available
• Single users/user groups transfer data into repositories -> not established for the wider community yet

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Typical objects in chemistry

Reaction

Sample

Analysis

Metadata

Dataset

Attachment

Research data and publications

28

Reaction information

Samples structures, conditions

Identifiers created

Descriptions according common practice

Characterization of results defined

not standardized

name: standardized

Examples from literature - general overview (10 min)

Dominik Voll (KIT)

09.06.2022

From Small Molecules to Polymers

30

Espeel, P.; Du Prez, F. E. Eur. Polym. J. 2015, 62, 247.

Starting from a small molecule where "simple" characterization methods such as NMR, IR, MS can be applied…

…one obtains oligomers or polymers which can only be characterized by specialized methods such as SEC, MALDI-ToF, SEC/ESI MS

Structural Flexibility in Polymer Chemistry

31

Heck, M.; Botha, C.; Wilhelm, M.; Hirschberg, V. Macromol. Rapid Commun. 2021, 42, 2100448.

With special methods, architectures of high molecular weight can be synthesized very quickly

n, m, p, q give information on the repeating units, these can have defined values or remain undefined

Nested polymer structures are very common

Structural Flexibility in Polymer Chemistry

32

Inglis, A. J.; Pierrat, P.; Muller, T.; Bräse, S.; Barner-Kowollik, C. Soft Matter 2010, 6, 82.

Complex starting materials are influenced in their properties by reaction with polymers.

Polymer Synthesis and Application

33

Lin, S.; Shang, J.; Theato, P. ASC Macro Lett. 2018, 7, 431.

Polymer synthesis, as shown here, can also be directly related from the beginning to the application as a material - a material that can still be post-functionalized as such to fulfil its function in an application.

Research Data and Publications in Polymer Chemistry

34

No repository entry, no identifiers created

Descriptions according common practice in polymer chemistry

Characterization of results not defined, depends on the molecular weight

name: not standardized

Reaction information

Samples structures, conditions: not standardized

Structure of Information / entities in polymer chemistry (5 min)

Patrick Theato (KIT)

09.06.2022

How are the sessions organized?

36

Polymer 101

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• macromolecule vs. polymer
• a polymer is the ensemble of many macromolecules, i.e. macromolecular chains
• information of macromolecules (i.e. chemical information)
• “identical” macromolecules can be made from different monomers
• copolymer composition
• information of polymer

organic chemistry vs. polymer chemistry

38

vs.

data practically independent of synthesis

data very dependent of synthesis

Challenge: naming polymers

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• source based naming
• IUPAC, constitutional repeating unit
• Abbreviations and acronyms
• Trade names

basic information

40

• Synthesis: method, yield, degree of polymerization
• Chemical identity: Which polymer is this? What additives are present? Is there contamination?
• Morphology: Amount of crystallinity (tacticity?), optical properties.
• Molecular weight M_n, M_w, etc. : Molecular weight distribution Ð = M_w/M_n
• Solution behavior: Viscosity, flow, gel.
• Thermal behavior: Glass transition, melt and decomposition temperature, melt viscosity.
• Mechanical properties: Tensile strength, modulus, impact, creep, dimensional stability.

What information is generated?

41

What information needs to be kept? and why?

42

additional questions to be discussed in upcoming meeting

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• Minimum Information in Synthesis?
• Minimum Information in Structural Characterization?
• Minimum Information in Property Characterization?
• others?

Session 2: Molecular Structures and definitions - structure editors

Nicole Jung (KIT)

09.06.2022

Session overview

45

Session 2 - Marvin and Ketcher - Options

46

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• Ketcher2
• Marvin
• ChemDraw
• HELM

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Most suitable tool?

Can all tools be used for polymer chemistry?

What definitions are necessary in ELN/for documentation?

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​

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Session 2 - ChemDraw

47

Session 2 - ChemDraw

48

Session 2 - Comparison

49

Marvin und Ketcher

• In principle the same functions
• Use a bit different - Ketcher 2 a bit more complicated, less export functions
• no calculation for []x-y -> since considered as undefined

ChemDraw

• a lot of functions -> need for documentation
• no option to define []x-y possible

Supported file formats and data aspects

Session 2 - Documentation information

50

Information that need to be defined in addition to structure:

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• head-to-head
• head-to-tail
• tail-to-tail

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n, m, … - definition

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• does that need to be described?
• How precisely can this be described?

Session 2 - Marvin and Ketcher - Options

51

Example made with Marvin

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possible drawings with Marvin and Ketcher

Session 2 - Marvin and Ketcher - Options

52

Example made with Marvin

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ELN implications

53

Example made with Marvin

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Session 2 - Marvin

54

Example made with Marvin

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Session 2 - Marvin

55

Polymer with []10 and []5 are different molecules?

Session 2 - Marvin

56

Example made with Marvin

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Description of which polymers are sorted together - will change with PolymerInChi..

Session 2 - Marvin

57

Example made with Marvin

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Use cases: is n etc sometimes undefined or will it be defined in every case?

Session 2 - Marvin

58

Example made with Marvin

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where to define n,m,o? in ELN masks?

Session 2 - Marvin and Ketcher - Options

59

Example made with Marvin

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Is the assignment to one molecule for undefined n and m correct? When should this definition be used? Do we want to use n and m for calculations or are they just placeholders?

Session 2 - Marvin

60

Example made with Marvin

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Is []n, []m the same molecule as []n, []5?

Session 2 - Marvin

61

Do polymers with []10 and []n belong to the same molecule as []10?

Session 2 - Marvin and Ketcher - Options

62

Example made with Marvin

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Will scientists use n,m,o etc and define n,m,o then in the details?

In consequence, polymer samples with different molecular weights will belong to the same molecule?

Session 2 - Marvin

63

How to deal with polymers with n = range

Currently = sorted to molecules having undefined number of repetition units

Session 3: Information on Samples in polymer chemistry

Nicole Jung (KIT) - Sample descriptions according to current polymer chemistry ELN tabs

09.06.2022

How are the sessions organized?

65

Current information and metadata: Layer 2

66

• which information should be added as multiple information field?
• for which information is important how it was gained? → instruments, conditions?

in those cases: is there a need to include it to the masks?

• is the current data and its structure correct?

• The benefit of an ELN is not to have data in a digital form
• The benefit of an ELN comes with the option to generate systematically structured data

67

Connection of processes, entities and data to clearly define their relation to each other

Reaction

Sample

Analysis

Metadata

Dataset

Attachment

Needed for: Metadata-Publishing/Scheme Datacite

Chemotion: systematic structuring of data matters

Taking existing sample and extend it to meet polymer needs

Nicole Jung – RDM with Chemotion Electronic Lab Notebook

Current information and metadata: Samples

68

Current information and metadata: Samples

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Current information and metadata

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Current information and metadata

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Current information and metadata

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Current information and metadata

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defined in detail -> needs review and extension in workshop

Current information and metadata

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not clearly defined yet -> needs additional work in workshop

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Implementation - ELN

Category Homopolymer

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Homopolymer is given as example - Information on all categories given in the additional materials

Nicole Jung – RDM with Chemotion Electronic Lab Notebook

Current information and metadata: Homopolymer

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L2

L2

L2

L2

Current information and metadata: Layer 2

77

identical: homopolymer, copolymer

Current information and metadata: Layer 2

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identical: homopolymer, copolymer

Current information and metadata: Layer 2

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identical: homopolymer, copolymer

Current information and metadata: Layer 2

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identical: homopolymer, copolymer

Information on Processes in polymer chemistry (reactions and beyond) (10 min)

Dominik Voll & Patrick Theato (KIT)

09.06.2022

Classification of polymerization methods

82

you can make the same polymer by different polymerization methods

example #1: molecular mass

83

• molecular mass: M_n, M_w, etc.
• dispersity Ð = M_w/M_n

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example #2: thermal properties via DSC

84

• T_g, T_c, T_m
• but how about the width?

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example #3: synthetic route

85

example #4: repetition synthetic

86

how to cope with this?

87

taken from:

PolyDAT: A Generic Data Schema for Polymer Characterization

Tzyy-Shyang Lin, Nathan J. Rebello, Haley K. Beech, Zi Wang, Bassil El-Zaatari, David J. Lundberg, Jeremiah A. Johnson, Julia A. Kalow, Stephen L. Craig, and Bradley D. Olsen�Journal of Chemical Information and Modeling 2021 61 (3), 1150-1163�DOI: 10.1021/acs.jcim.1c00028

→ It’s a good start, but what shall (or shall not) be included and in which format?

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→ Which identifiers? InChI? Smiles? Bigsmiles? HELM?

Conclusion: Which information do we need?

88

molecular information:

• synthesis route
• chemical composition
• degree of polymerization, dispersity
• architectural features (ring, branched, block, star, etc.)
• …

property information:

• thermal properties (T_g, T_m, etc.)
• mechanical properties
• optical properties
• …

​

​

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NFDI4Chem – Minimum Standards in Polymer Chemistry

Dr. Susanne Boye

Leibniz-Institut für Polymerforschung

Session 5 –

Analyses in Polymer Chemistry

Method Collection - Introduction

• Chromatography:  separates mixtures into individual components, for example, polymer and additives.
• Spectroscopic methods answer the question, “what is it?”
• Microscopy:  provides information about polymer size, shape, and structure of materials and surface topography information.
• Physical Testing:  provides performance information for samples under specified test conditions.  eg, “how strong is it?”
• Wet Chemistry: covers a large variety of testing techniques.  Wet chemistry methods are often a necessary part of a more complex testing plan.
• Thermal Analysis:  determines physical properties as a function of time, or frequency, and temperature in a controlled atmosphere. For example, “what is the melting point?”
• Titrations:  Quantitative chemical analysis for a particular analyte. For example, “how many acid groups are present?”  

Method Collection

1. Most frequently used methods in Polymer Chemistry

• Size Exclusion Chromatography (molecular weights and distributions)
• Spectroscopy: 1. NMR, 2. FTIR + 3. Raman (chemical structure)
• Thermal Analysis - Thermogravimetry and Dynamic Scanning Calorimetry (thermal properties)

Method Collection

2. Frequently used methods in most areas of Polymer Chemistry

• UV spectroscopy
• Rheometric methods (viscometry)
• Scattering: SLS, DLS, SAXS, SANS, WAXS, … (sizes)
• Dynamic Mechanical Analysis (material testing)
• Microscopy (optical, AFM, SEM, TEM, cryoEM… )
• GC-MS
• Zeta-potential determination (charge)

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Method Collection

3. Occasional used methods in some areas of Polymer Chemistry

• MALDI-ToF-MS
• X-ray diffraction, XRD and X-ray photoelectron spectroscopy, XPS
• Elemental analysis
• HPLC
• Fluorescence spectroscopy
• Conductivity meter

Method Collection

4. Rarely used methods / specific techniques in Polymer Chemistry

• Field-flow fractionation (separation technique, ThFFF, AF4, centr. FFF, electr. FFF)
• Ultracentrifuge
• Ellipsometry (determination of film thickness)
• Assay techniques (conjugate analysis)
• Hyphenated techniques (2DxLC, TGA-FTIR, TGA-GC-MS, AFM-FTIR, NMR-Rheology…)
• EDX, energy dispersive X-Ray analysis (elemental composition)
• Flow cytometry
• SDS-PAGE, gel electrophoreses
• Contact Angle Measurements

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Method Collection

4. Rarely used methods / specific techniques in Polymer Chemistry

• Thermoelectrics, conductivity measurement, resistance meter
• pVT analysis
• ToF-SIMS (chemical surface analysis, depth profiling, surface imaging)
• QCM, quartz crystal microbalance
• SPM, surface plasmon resonance (binding properties)

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Time for discussion.

Polymer Workshop  

10.06.2022

�

Session 6 – Selected Analyses in Polymer Chemistry

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Rheology and mechanical testing

​

​

Valerian Hirschberg

Christopher Klein

AK Wilhelm

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Experimental Setup

• Rheology (processing properties)
• Shear rheometer
• Stress controlled (e.g. DHR series from TA instruments)
• Strain controlled (e.g. ARES-G2)
• Elongational rheology
• Capillary rheometer

​

• Mechanical testing (product properties)
• Tensile machine
• DMTA

​

• Other
• Non-linear
• Creep test
• …

​

Experimental Setup

• Rheology (processing properties)
• Shear rheometer
• Stress controlled (e.g. DHR series from TA instruments)
• Strain controlled (e.g. ARES-G2)
• Elongational rheology
• Capillary rheometer

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• Mechanical testing (product properties)
• Tensile machine
• DMTA

​

• Other
• Non-linear
• Creep test
• …

​

Why so many?

Many Questions asked:�Important rheological parameter

• ​
• ​
• ​
• ​
• ​
• ​
• ​
• ​
• ​
• ​
• ​
• ​
• ​

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• Zero shear viscosity (melt)

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• Shear modulus G*

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• for industry: melt flow index (e.g. ISO 1133)

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• Youngs’s modulus E* (solids)

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• Elongation at break (solids)

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Unfortunately, rheology isn’t that simple….

​

Many Questions asked:�Important rheological parameter

​

​

Why so many?

• Zero shear viscosity (melt)

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• Shear modulus G*

​

• for industry: melt flow index (e.g. ISO 1133)

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• Youngs’s modulus E* (solids)

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• Elongation at break (solids)

​

Unfortunately, rheology isn’t that simple….

​

Many different kind of Samples

• Rheology

flow of matter under load or deformation

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• Rheology “liquid state” -> processing properties
• Mechanics “solid state” -> product properties

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• wide range of rheological + mechanical tests

not all are needed for all samples

-> Sometimes later analysis necessary

Many different kind of Samples

• Rheology

flow of matter under load or deformation

​

• Rheology “liquid state” -> processing properties
• Mechanics “solid state” -> product properties

​

• wide range of rheological + mechanical tests

not all are needed for all samples

-> Sometimes later analysis necessary

Many more !

What are we interested in?

Why So many different samples?

​

​

​

​

​

​

​

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Heraklit von Ephesos 520 v. Chr.; † um 460 v. Chr.

​

​

-> store raw data!!

• Polymers
• Food
• Biopolymers
• Rubbers
• Cement concrete

Panta Rei

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All flows

Important rheological parameter

Rheology of complex systems can not easily be quantified with a few parameters alone!

​

Same zero-shear viscosity, but different rheological mastercurve!

We need raw data!

Raw data, raw data, raw data ….

• Materials with same zero-shear viscosity can still behave rheologically very different
• For Rheology affine people raw data are most useful!!
• Very important to have an option to safe the raw data directly
• We put all our raw data in the SI
• Our current paper has in the SI ~40 pages raw data from rheological data …

​

​

• Additional option to store directly model predictions from constitutive equations

Analysis

What do we need for the standard

- What do we want to learn

(Processing or Product)

​

• Define test and test condition

​

• Few general material parameters

​

- Important store raw data

NFDI4Chem – Minimum Standards in Polymer Chemistry

Dr. Susanne Boye

Leibniz-Institut für Polymerforschung

Session 6 –

Selected Analyses in Polymer Chemistry

Introduction into SEC

1. What is it good for?

• Determination of average molecular weights (molar masses) Mn, Mw, Mz and distributions, dispersity Ð = Mw/Mn, under special conditions radii can be determined

2. How it is performed?

• Injection of a diluted sample solution into a SEC column, molecules will be transported by eluent trough the column densly packed with porous material
• Depending on size (hydrodynamic volume) molecules will be separated

Separation depending on hydrodynamic volume

Porous packing material

molecules with different sizes

2. Separation Techniques and Adequate Detection

3. What type of SEC types are applied?

• Relative molar mass determination: elution times of narrowly distributed standards with varied molar masses will be compared with unkown sample

​

• Universal calibration intrinsic viscosities [η]xM will be plotted against elution time

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• Absolute molar mass determination: concentration sensitive detection in combination with multi angle laser light scattering MALS, dn/dc or extinction coefficient is needed - determination of Rg is possible, too

What is needed for ELN?

Technical equipment:

• pump, degasser, autosampler, detectors, software data collection and processing

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Experimental conditions:

• Eluent
• Sample concentration, injection volume
• Type of column, pore size, packing material, supplier
• Type of molar mass determination: relative, universal or absolute
• Relative or universal: Used standards (polymer type, molar masses, supplier)
• Absolute: dn/dc, extinction coefficient, fitting model of scattering data

Processing of data:

• Peak ranges, baselines, despiking, units (kDa or g/mol), using MALS (fitting model of scattering data, fit order, negclection of angles)

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Outcomes:

• Data: Mn, Mw, Mz, Ð (Mw/Mn), with LS Rg, Rh, ν; with Visco [η], α
• Figures:

Chromatograms: (RI or UV) detector signal vs. Elution time (volume)

Molar mass distributions: log M vs. differential fraction

SEC-LS: M or R vs elution time (volume); M vs. R (scaling plot)

SEC-Visco: [η] vs. elution time, M vs. [η] (KMHS-Plot)

Time for discussion.

Session 7: Data availability - now and desired

Nicole Jung (KIT) - Repositories

10.06.2022

NFDI4Chem’s Vision

116

Standards

Community

Digitisation

Experiment Design

Experiment/ Data Collection

Data

Processing

Analysis

Electronic Lab Notebooks

Repositories

Data Life Cycle

FAIR data�

117

Aim: Improve data availability and quality

​

Not FAIR and Not Open:

a lot of information is lost.

Relation experiment ↔ data not clear.

​

The availability of data depends strongly

on the good will of the individual scientist

from: Auke Herrema – Het Bouwteam

wikimedia.org/wiki/File:FAIR_data_principles.jpg, 10.1038/sdata.2016.18

Findable

Accessible

Interoperable

Re-usable (Reproducible)

​

FAIR is not a buzzword, it’s a mind set !

• Findable: Easy to locate, using a unique and persistent identifier.�Descriptive metadata to enable discovery.
• Accessible: Easily retrieved by machines and humans�using standard protocols, with metadata, and archived long-term.
• Interoperable: Available in formats that can be exchanged, interpreted and combined, including metadata.
• Reusable: Metadata ensures re-use for future research,�with clear license and provenance, following community standards.

118

Adapted from: The FAIR Data Principles for Research Data

https://blogs.tib.eu/wp/tib/2017/09/12/the-fair-data-principles-for-research-data/

�

A special generic repository:

https://www.radar-service.eu/

• Format-independent preservation with metadata
• “The long tail of science”
• Dataset has a DOI
• Highly reliable Germany-based �multi-datacenter (Karlsruhe + Dresden)
• Institution needs to be contract member�Not in RADAR4Chem !

+

NFDI4Chem Workshop series on RDM

Hungry for more information?

120

Best Practice

Flagship Labs

Data Organisation

Data Preservation

Data Management Plan

Storage

Electronic Lab Journals

Backup

Terminology

Metadata

Persistent Identifiers

Legal Aspects

Copyright

File Naming

Machine-�Readability

Ontology

Archiving

Data Versioning

Creative Commons

For more information, please register via this link

Standards and guidelines – what for?

• Full description of experiments (methods, techniques, handlings, identifier, metadata) and results (values & units, models, raw data (?), identifier, metadata)

• Consideration of ontologies and controlled vocabularies

• Basis for structural arrangement of data and development of data exchange formats

• Making research data FAIR

• Creating “landscapes” for the integration of tools, repositories, databases

• Interconnecting the data flow from the bench to the publication

​

121

Essential prerequisites for acceptance

• What are standards not? – substitutes for review process

• Sufficiency

• Practicability

• Stability

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122

Standards are…

• Appreciated, demanded, and supported by the broader science community

• Created, used and implemented

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How to make standards successful?

• Lead by example

• Present working examples

123

How to create standards?

• Many various ways possible

• Relevant examples for NFDI4Chem: NMR, metabolomics, glycomics, enzymology

• SOPs or reporting guidelines?

• Identification of data to be stored and reused (only for these standards are required)

• Identify sub-discipline groups within chemistry interested in standards whatsoever

• Create core-groups with persons who are willing to dedicate time and nerves over a longer period of time

• Create an organizational structure and a schema of regular meetings

• Outreach to relevant community for broad consultancy

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Basics for how to generate, analyze, process, store and publish data

Nicole Jung (KIT)

24.05.2022

Polymer Workshop - “information” in Polymer Chemistry

Patrick Theato (KIT)

24.05.2022

• macromolecule vs. polymer
• a polymer is the ensemble of many macromolecules, i.e. macromolecular chains
• information of macromolecules (i.e. chemical information)
• “identical” macromolecules can be made from different monomers
• copolymer composition

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Polymer 101

Nicole Jung – RDM with Chemotion Electronic Lab Notebook

128

organic chemistry vs. polymer chemistry

vs.

data practically independent of synthesis

data very dependent of synthesis

Nicole Jung – RDM with Chemotion Electronic Lab Notebook

• Synthesis: method, yield, degree of polymerization
• Chemical identity: Which polymer is this? What additives are present? Is there contamination?
• Morphology: Amount of crystallinity (tacticity?), optical properties.
• Molecular weight M_n, M_w, etc. : Molecular weight distribution Ð = M_w/M_n
• Solution behavior: Viscosity, flow, gel.
• Thermal behavior: Glass transition, melt and decomposition temperature, melt viscosity.
• Mechanical properties: Tensile strength, modulus, impact, creep, dimensional stability.

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basic information

Nicole Jung – RDM with Chemotion Electronic Lab Notebook

130

Classification of polymerization methods

you can make the same polymer by different polymerization methods

Nicole Jung – RDM with Chemotion Electronic Lab Notebook

• molecular mass: M_n, M_w, etc.
• dispersity Ð = M_w/M_n

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example #1: molecular mass

Nicole Jung – RDM with Chemotion Electronic Lab Notebook

• T_g, T_c, T_m
• but how about the width?

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132

example #2: thermal properties via DSC

Nicole Jung – RDM with Chemotion Electronic Lab Notebook

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thoughts on line notations: Which one?

simplified molecular-input line-entry system (SMILES)

SYBYL line notation (SLN)

Wiswesser line notation (WLN)

ROSDAL

modular chemical descriptor language (MCDL)

international chemical identifier (InChI)

hierarchical editing language for macromolecules (HELM) developed by the Pistoia Alliance, the International Union of Pure and Applied (IUPAC) international chemical identifier (InChI), the CurlySMILES language, an extension of SMILES that aims to provide support for polymers, composite materials, and crystals.

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Nicole Jung – RDM with Chemotion Electronic Lab Notebook

• molecular structure
• BigSMILES representation

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134

combining bits and pieces

Nicole Jung – RDM with Chemotion Electronic Lab Notebook

135

combining bits and pieces

taken from:

PolyDAT: A Generic Data Schema for Polymer Characterization

Tzyy-Shyang Lin, Nathan J. Rebello, Haley K. Beech, Zi Wang, Bassil El-Zaatari, David J. Lundberg, Jeremiah A. Johnson, Julia A. Kalow, Stephen L. Craig, and Bradley D. Olsen�Journal of Chemical Information and Modeling 2021 61 (3), 1150-1163�DOI: 10.1021/acs.jcim.1c00028

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Nicole Jung – RDM with Chemotion Electronic Lab Notebook

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Conclusion: Which information do we need?

molecular information:

• synthesis route
• chemical composition
• degree of polymerization, dispersity
• architectural features (ring, branched, block, star, etc.)
• …

property information:

• thermal properties (T_g, T_m, etc.)
• mechanical properties
• optical properties
• …

​

​

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Nicole Jung – RDM with Chemotion Electronic Lab Notebook

Polymer Workshop - Polymer template

Nicole Jung (KIT)

24.05.2022

Polymer Workshop - Discussion on workshop agenda and roles

all

24.05.2022

Agenda

139

Workshop format and roles

hybrid format

140

Roles in detail (names)

141

Next steps (May 24th to June 9th)

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142

Next steps: Discussion of details in smaller groups on request

Preparation of the meeting: collect information and review of additional material

Questions for the accommodation/travel and others?

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Thank you very much for your attention

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