53941
2021
eng
87
98
1
10
article
IEEE
1
--
--
--
Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy Using a Novel Data Augmentation Method
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been realized to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra, to train an ANN with better prediction performance and speed than state-of-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
Transactions on Emerging Topics in Computing
2168-6750
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9638378
10.1109/TETC.2021.3131371
urn:nbn:de:kobv:b43-539412
publish
28.03.2022
Creative Commons - CC BY - Namensnennung 4.0 International
F. Fricke
M. Brandalero
Sascha Liehr
Simon Kern
Klas Meyer
Stefan Kowarik
R. Hierzegger
S. Westerdick
Michael Maiwald
M. Hübner
eng
uncontrolled
Industry 4.0
eng
uncontrolled
Cyber-Physical Systems
eng
uncontrolled
Artificial Neural Networks
eng
uncontrolled
Mass Spectrometry
eng
uncontrolled
Nuclear Magnetic Resonance Spectroscopy
eng
uncontrolled
Modular Production
Analytische Chemie
1 Analytische Chemie; Referenzmaterialien
1.4 Prozessanalytik
8 Zerstörungsfreie Prüfung
8.6 Faseroptische Sensorik
Umwelt
Chemie und Prozesstechnik
Chemische Charakterisierung und Spurenanalytik
Verlagsliteratur
Datei für die Öffentlichkeit verfügbar ("Open Access")
Wissenschaftliche Artikel der BAM
8.0 Abteilungsleitung und andere
Sensorik
Bundesanstalt für Materialforschung und -prüfung (BAM)
https://opus4.kobv.de/opus4-bam/files/53941/IEEE_TETC_10_2022_87-98_Fricke_et_al_AI_for_MS_and_NMR.pdf
55360
2021
eng
615
620
article
IEEE
1
--
--
--
Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
2021 Design, Automation & Test in Europe Conference & Exhibition (DATE)
10.23919/DATE51398.2021.9473958
2021 Design, Automation & Test in Europe Conference & Exhibition (DATE)
Grenoble, France
01.02.2021
05.02.2021
publish
25.07.2022
false
true
F. Fricke
S. Mahmood
J. Hoffmann
M. Brandalero
Sascha Liehr
Simon Kern
Klas Meyer
S. Kowarik
S. Westerdick
Michael Maiwald
M. Hübner
eng
uncontrolled
Industry 4.0,
eng
uncontrolled
Cyber-physical systems
eng
uncontrolled
Artificial neural networks
eng
uncontrolled
Mass spectrometry
eng
uncontrolled
Nuclear magnetic resonance spectroscopy
Analytische Chemie
1 Analytische Chemie; Referenzmaterialien
1.4 Prozessanalytik
Chemie und Prozesstechnik
Chemische Charakterisierung und Spurenanalytik
Verlagsliteratur
Datei im Netzwerk der BAM verfügbar ("Closed Access")
37356
2016
eng
39
52
Part A / SI
83
article
Elsevier
1
--
--
--
Process control with compact NMR
Compact nuclear magnetic resonance (NMR) instruments make NMR spectroscopy and relaxometry accessible in industrial and harsh environments for reaction and process control. An increasing number of applications are reported. To build an interdisciplinary bridge between “process control” and “compact NMR”,we give a short overviewon current developments in the field of process Engineering such as modern process design, integrated processes, intensified processes along with requirements to process control, model based control, or soft sensing. Finally, robust field integration of NMR systems into processes environments, facing explosion protection or Integration into process control systems, are briefly discussed.
Trends in Analytical Chemistry
10.1016/j.trac.2016.03.016
http://www.sciencedirect.com/science/article/pii/S0165993616300073
0165-9936
urn:nbn:de:kobv:b43-373562
23.02.2017
Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International
Klas Meyer
Simon Kern
Nicolai Zientek
G. Guthausen
Michael Maiwald
deu
uncontrolled
Prozessanalytik
deu
uncontrolled
Quantitative NMR-Spektroskopie
deu
uncontrolled
Industrie 4.0
eng
uncontrolled
Reaction monitoring
eng
uncontrolled
Process control
eng
uncontrolled
Online NMR spectroscopy
eng
uncontrolled
Compact NMR
Analytische Chemie
Verlagsliteratur
Datei für die Öffentlichkeit verfügbar ("Open Access")
Wissenschaftliche Artikel der BAM
Bundesanstalt für Materialforschung und -prüfung (BAM)
https://opus4.kobv.de/opus4-bam/files/37356/Process control with compact NMR - Manuskript.pdf
36333
2016
eng
698
709
6
88
article
Wiley-VCH Verlag GmbH & Co. KGaA
Weinheim, Germany
1
--
--
--
Quantitative online NMR spectroscopy in a nutshell
Online NMR spectroscopy is an excellent tool to study complex reacting multicomponent mixtures and gain process insight and understanding. For online studies under process conditions, flow NMR probes can be used in a wide range of temperature and pressure. This paper compiles the most important aspects towards quantitative process NMR spectroscopy in complex multicomponent mixtures and provides examples. After NMR spectroscopy is introduced as an online method and for technical samples without sample preparation in deuterated solvents, influences of the residence time distribution, pre-magnetization, and cell design are discussed. NMR acquisition and processing parameters as well as data preparation methods are presented and the most practical data analysis strategies are introduced.
Chemie Ingenieur Technik
10.1002/cite.201500120
0009-286X
27.06.2016
Nicolai Zientek
Klas Meyer
Simon Kern
Michael Maiwald
deu
uncontrolled
Prozessanalytik
eng
uncontrolled
Process analytical technology
eng
uncontrolled
Online NMR spectroscopy
eng
uncontrolled
Online monitoring
Analytische Chemie
Verlagsliteratur
Datei im Netzwerk der BAM verfügbar ("Closed Access")
38675
2016
deu
21
25
12
58
article
DIV Deutscher Industrieverlag GmbH
München
0
--
--
--
NMR-Spektroskopie im Feld - Eine neue Online-Methode für die Prozesskontrolle
Flexible, modulare Produktionsanlagen auf der Basis von verfahrenstechnischen Teilmodulen stellen einen vielversprechenden Ansatz für die kontinuierliche Produktion von Fein- und Spezialchemikalien dar. Der Schwerpunkt des Horizont-2020-Projekts der Europäischen Kommission CONSENS (Integrated Control and Sensing) liegt in der Implementierung von innovativen Sensorkonzepten zur Prozessüberwachung und -regelung innerhalb von containerbasierten modularen Produktionsan lagen. In diesem Artikel wird die Feldintegration eines Online-NMR-Sensors als smartes Modul für die Prozesskontrolle beschrieben. Dieses Modul basiert auf einem kommerziell erhältlichen Niederfeld-NMRSpektrometer, das zurzeit für die Anwendung im Laborbereich erhältlich ist. Für die Feldintegration wurde ein ATEX-zertifiziertes, explosionsgeschütztes Gehäuse entwickelt sowie Automationsschemen für den unbeaufsichtigten Betrieb und für die spektrale Datenauswertung erstellt. Nachdem die Machbarkeit und Leistungsfähigkeit des Sensorkonzeptes in Laborexperimente an einer aromatischen Substitutionsreaktion bereits erfolgreich demonstriert wurde, ist die Inbetriebnahme des NMR-Sensormoduls in
industrieller Umgebung für 2017 geplant.
ATP-Edition (Automatisierungstechnische Praxis)
2190-4111
Simon Kern
Klas Meyer
Michael Maiwald
deu
uncontrolled
Prozessanalytik
eng
uncontrolled
Reaction monitoring
deu
uncontrolled
Industrie 4.0
deu
uncontrolled
Online-NMR-Spektroskopie
deu
uncontrolled
CONSENS
deu
uncontrolled
Prozess-Sensoren
Analytische Chemie
Verlagsliteratur
Datei im Netzwerk der BAM verfügbar ("Closed Access")
44847
2018
eng
3349
3360
14
410
article
Springer
Berlin, Heidelberg
1
--
--
--
Online low-field NMR spectroscopy for process control of an industrial lithiation reaction—automated data analysis
Monitoring specific chemical properties is the key to chemical process control. Today, mainly optical online methods are applied, which require time- and cost-intensive calibration effort. NMR spectroscopy, with its advantage being a direct comparison method without need for calibration, has a high potential for closed-loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and rough environments for process monitoring and advanced process control strategies.
We present a fully automated data analysis approach which is completely based on physically motivated spectral models as first principles information (Indirect Hard Modelling – IHM) and applied it to a given pharmaceutical lithiation reaction in the framework of the European Union’s Horizon 2020 project CONSENS. Online low-field NMR (LF NMR) data was analysed by IHM with low calibration effort, compared to a multivariate PLS-R (Partial Least Squares Regression) approach, and both validated using online high-field NMR (HF NMR) spectroscopy.
Analytical and Bioanalytical Chemistry
10.1007/s00216-018-1020-z
https://link.springer.com/article/10.1007/s00216-018-1020-z
1618-2642
1618-2650
07.05.2018
Simon Kern
Klas Meyer
Svetlana Guhl
Patrick Gräßer
Andrea Paul
R. King
Michael Maiwald
eng
uncontrolled
Online NMR spectroscopy
eng
uncontrolled
Process analytical technology
eng
uncontrolled
Partial Least Squares Regression
eng
uncontrolled
Indirect Hard Modeling
eng
uncontrolled
Benchtop NMR Spectroscopy
eng
uncontrolled
Smart Sensors
eng
uncontrolled
CONSENS
Analytische Chemie
1 Analytische Chemie; Referenzmaterialien
1.4 Prozessanalytik
Umwelt
Chemie und Prozesstechnik
Chemische Charakterisierung und Spurenanalytik
Verlagsliteratur
Datei im Netzwerk der BAM verfügbar ("Closed Access")
Sensorik
48062
2019
eng
3037
3046
14
411
article
Springer Nature
Heidelberg
1
--
--
--
Flexible automation with compact NMR spectroscopy for continuous production of pharmaceuticals
Modular plants using intensified continuous processes represent an appealing concept for the production of pharmaceuticals. It can improve quality, safety, sustainability, and profitability compared to batch processes; besides, it enables plug-and-produce reconfiguration for fast product changes. To facilitate this flexibility by real-time quality control, we developed a solution that can be adapted quickly to new processes and is based on a compact nuclear magnetic resonance (NMR) spectrometer. The NMR sensor is a benchtop device enhanced to the requirements of automated chemical production including robust evaluation of sensor data. Beyond monitoring the product quality, online NMR data was used in a new iterative optimization approach to maximize the plant profit and served as a reliable reference for the calibration of a near-infrared (NIR) spectrometer. The overall approach
was demonstrated on a commercial-scale pilot plant using a metal-organic reaction with pharmaceutical relevance.
Analytical and Bioanalytical Chemistry
10.1007/s00216-019-01752-y
1618-2642
urn:nbn:de:kobv:b43-480623
1618-2650
27.05.2019
https://nbn-resolving.org/urn:nbn:de:kobv:b43-480630
Creative Commons - CC BY - Namensnennung 4.0 International
Simon Kern
Lukas Wander
Klas Meyer
Svetlana Guhl
A. R. Gottu Mukkula
M. Holtkamp
M. Salge
C. Fleischer
N. Weber
S. Engell
Andrea Paul
M. Pereira Remelhe
Michael Maiwald
eng
uncontrolled
NMR Spectroscopy
eng
uncontrolled
NIR Spectroscopy
eng
uncontrolled
Real-time process monitoring
eng
uncontrolled
Real-time quality control
eng
uncontrolled
Continuous processes
eng
uncontrolled
CONSENS
eng
uncontrolled
Data Fusion
Analytische Chemie
1 Analytische Chemie; Referenzmaterialien
1.4 Prozessanalytik
Umwelt
Chemie und Prozesstechnik
Chemische Charakterisierung und Spurenanalytik
Verlagsliteratur
Datei für die Öffentlichkeit verfügbar ("Open Access")
Wissenschaftliche Artikel der BAM
Sensorik
Bundesanstalt für Materialforschung und -prüfung (BAM)
https://opus4.kobv.de/opus4-bam/files/48062/ABC_411_2019_3037-3046_Kern.pdf
49041
2019
eng
19562
19570
42
58
article
American Chemical Society
Washington
1
--
--
--
Design and validation of an additively manufactured flowCell–static mixer combination for inline NMR spectroscopy
There have been an increasing number of publications on flow chemistry applications of compact NMR. Despite this, there is so far no comprehensive workflow for the technical design of flow cells. Here, we present an approach that is suitable for the design of an NMR flow cell with an integrated static mixing unit. This design moves the mixing of reactants to the active NMR detection region within the NMR instrument, presenting a feature that analyses chemical reactions faster (5–120 s region) than other common setups. During the design phase, the targeted mixing homogeneity of the components was evaluated for different types of mixing units based on CFD simulation. Subsequently, the flow cell was additively manufactured from ceramic material and metal tubing. Within the targeted working mass flow range, excellent mixing properties as well as narrow line widths were confirmed in validation experiments, comparable to common glass tubes.
Industrial & Engineering Chemistry Research
10.1021/acs.iecr.9b03746
https://pubs.acs.org/doi/abs/10.1021/acs.iecr.9b03746
0888-5885
1520-5045
Geburtsname von Bornemann-Pfeiffer, Martin: Bornemann, M. - Birth name of Bornemann-Pfeiffer, Martin: Bornemann, M.
18.12.2019
Martin Bornemann-Pfeiffer
Simon Kern
N. Jurtz
Tobias Thiede
M. Kraume
Michael Maiwald
eng
uncontrolled
Inline NMR Spectroscopy
eng
uncontrolled
Integrated Processes
eng
uncontrolled
Reaction Monitoring
eng
uncontrolled
Process Analytical Technology
eng
uncontrolled
Flow Chemistry
eng
uncontrolled
Static Mixing
eng
uncontrolled
Modular Production
Analytische Chemie
Ingenieurwissenschaften und zugeordnete Tätigkeiten
1 Analytische Chemie; Referenzmaterialien
1.4 Prozessanalytik
8 Zerstörungsfreie Prüfung
8.5 Röntgenbildgebung
Umwelt
Material
Chemie und Prozesstechnik
Chemische Charakterisierung und Spurenanalytik
Verlagsliteratur
Datei im Netzwerk der BAM verfügbar ("Closed Access")
Sensorik
50750
2020
eng
4447
4459
18
412
article
Springer
Berlin
1
--
--
--
Artificial neural networks for quantitative online NMR spectroscopy
Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger Training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data.
Analytical and bioanalytical chemistry
10.1007/s00216-020-02687-5
1618-2642
urn:nbn:de:kobv:b43-507508
18.05.2020
https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/50456
https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/54481
Creative Commons - CC BY - Namensnennung 4.0 International
Simon Kern
Sascha Liehr
Lukas Wander
Martin Bornemann-Pfeiffer
S. Müller
Michael Maiwald
Stefan Kowarik
eng
uncontrolled
Online NMR spectroscopy
eng
uncontrolled
Real-time process monitoring
eng
uncontrolled
Artificial neural networks
eng
uncontrolled
Automation
eng
uncontrolled
Process industry
Analytische Chemie
Ingenieurwissenschaften und zugeordnete Tätigkeiten
1 Analytische Chemie; Referenzmaterialien
1.4 Prozessanalytik
8 Zerstörungsfreie Prüfung
8.6 Faseroptische Sensorik
Umwelt
Material
Chemie und Prozesstechnik
Chemische Charakterisierung und Spurenanalytik
Verlagsliteratur
Datei für die Öffentlichkeit verfügbar ("Open Access")
Wissenschaftliche Artikel der BAM
8.0 Abteilungsleitung und andere
Sensorik
Bundesanstalt für Materialforschung und -prüfung (BAM)
https://opus4.kobv.de/opus4-bam/files/50750/AnalBioanalChem_412_2020.pdf
https://opus4.kobv.de/opus4-bam/files/50750/216_2020_2687_MOESM1_ESM.pdf
51726
2021
eng
62
70
1-2
93
article
Wiley-VCH
Weinheim
1
--
--
--
Calibration-Free Chemical Process and Quality Control Units as Enablers for Modular Production
Modular chemical production is a tangible translation of the digital transformation of the process industry for specialty chemicals. In particular, it enables the speeding-up of process development and thus a quicker time to market by flexibly connecting and orchestrating standardised physical modules and bringing them to life (i.e., parameterising them) with digitally accumulated process knowledge.
We focus on the specific challenges of chemical process and quality control, which in its current form is not well suited for modular production and provide possible approaches and examples of the change towards direct analytical methods, analytical model transfer or machine-supported processes.
Chemie Ingenieur Technik
10.1002/cite.202000150
1522-2640
urn:nbn:de:kobv:b43-517264
28.12.2020
Creative Commons - CC BY - Namensnennung 4.0 International
Martin Bornemann-Pfeiffer
Simon Kern
Michael Maiwald
Klas Meyer
eng
uncontrolled
Modular Production
eng
uncontrolled
Chemical Process Control
eng
uncontrolled
Process Analytical Technology
eng
uncontrolled
Digital Transformation
eng
uncontrolled
Industry 4.0
Analytische Chemie
1 Analytische Chemie; Referenzmaterialien
1.4 Prozessanalytik
Umwelt
Chemie und Prozesstechnik
Chemische Charakterisierung und Spurenanalytik
Verlagsliteratur
Datei für die Öffentlichkeit verfügbar ("Open Access")
Wissenschaftliche Artikel der BAM
Sensorik
Bundesanstalt für Materialforschung und -prüfung (BAM)
https://opus4.kobv.de/opus4-bam/files/51726/ChemIngTech_93_2021_62-70_Bornemann-Pfeiffer_et_al_Enablers_for_Modular_Production.pdf
52453
2020
eng
11773
11779
2
53
article
Elsevier
Amsterdam
1
--
--
--
An Application of Modifier Adaptation with Quadratic Approximation on a Pilot Scale Plant in Industrial Environment
The goal of this work is to identify the optimal operating input for a lithiation reaction that is performed in a highly innovative pilot scale continuous flow chemical plant in an industrial environment, taking into account the process and safety constraints. The main challenge is to identify the optimum operation in the absence of information about the reaction mechanism and the reaction kinetics. We employ an iterative real-time optimization scheme called modifier adaptation with quadratic approximation (MAWQA) to identify the plant optimum in the presence of plant-model mismatch and measurement noise. A novel NMR PAT-sensor is used to measure the concentration of the reactants and of the product at the reactor outlet. The experiment results demonstrate the capabilities of the iterative optimization using the MAWQA algorithm in driving a complex real plant to an economically optimal operating point in the presence of plant-model mismatch and of process and measurement uncertainties.
IFAC-PapersOnLine
1522-2640
10.1016/j.ifacol.2020.12.685
urn:nbn:de:kobv:b43-524531
publish
24.06.2021
Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International
A. R. Gottu Mukkula
Simon Kern
M. Salge
M. Holtkamp
Svetlana Guhl
C. Fleischer
Klas Meyer
M. Remelhe
Michael Maiwald
S. Engell
eng
uncontrolled
Process Analytical Technology
eng
uncontrolled
Online NMR Spectroscopy
eng
uncontrolled
Process Industry
eng
uncontrolled
Iterative real-time optimization
eng
uncontrolled
Modifier adaptation
eng
uncontrolled
Plant-model mismatch
eng
uncontrolled
Reactor control
eng
uncontrolled
CONSENS
Analytische Chemie
1 Analytische Chemie; Referenzmaterialien
1.4 Prozessanalytik
Umwelt
Chemie und Prozesstechnik
Chemische Charakterisierung und Spurenanalytik
Verlagsliteratur
Datei für die Öffentlichkeit verfügbar ("Open Access")
Wissenschaftliche Artikel der BAM
Sensorik
Bundesanstalt für Materialforschung und -prüfung (BAM)
https://opus4.kobv.de/opus4-bam/files/52453/IFAC_53_2020_11773-11779_Gottu-Mukula_Modifier_Adaptation_CONSENS.pdf
39323
2017
eng
154
161
277
article
Elsevier Inc.
Oxford
1
--
--
--
‘‘Click” analytics for ‘‘click” chemistry – A simple method for calibration–free evaluation of online NMR spectra
Driven mostly by the search for chemical syntheses under biocompatible conditions, so called "click" chemistry rapidly became a growing field of research. The resulting simple one-pot reactions are so far only scarcely accompanied by an adequate optimization via comparably straightforward and robust analysis techniques possessing short set-up times. Here, we report on a fast and reliable calibration-free online NMR monitoring approach for technical mixtures. It combines a versatile fluidic system, continuous-flow measurement of 1H spectra with a time interval of 20 s per spectrum, and a robust, fully automated algorithm to interpret the obtained data. As a proof-of-concept, the thiol-ene coupling between N-boc cysteine methyl ester and allyl alcohol was conducted in a variety of non-deuterated solvents while its time-resolved behaviour was characterized with step tracer experiments. Overlapping signals in online spectra during thiol-ene coupling could be deconvoluted with a spectral model using indirect hard modeling and were subsequently converted to either molar ratios (using a calibrationfree approach) or absolute concentrations (using 1-point calibration). For various solvents the kinetic constant k for pseudo-first order reaction was estimated to be 3.9 h-1 at 25 °C. The obtained results were compared with direct integration of non-overlapping signals and showed good agreement with the implemented mass balance.
Journal of Magnetic Resonance
10.1016/j.jmr.2017.02.018
http://www.sciencedirect.com/science/article/pii/S1090780717300575
urn:nbn:de:kobv:b43-393232
18.05.2017
Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International
Aleksandra Michalik-Onichimowska
Simon Kern
Jens Riedel
Ulrich Panne
R. King
Michael Maiwald
eng
uncontrolled
Online NMR Spectroscopy
eng
uncontrolled
Reaction Monitoring
eng
uncontrolled
Automated Data Evaluation
eng
uncontrolled
Thiol-ene click chemistry
eng
uncontrolled
Click Chemistry
eng
uncontrolled
Process Analytical Technology
Analytische Chemie
Verlagsliteratur
Datei für die Öffentlichkeit verfügbar ("Open Access")
Wissenschaftliche Artikel der BAM
Bundesanstalt für Materialforschung und -prüfung (BAM)
https://opus4.kobv.de/opus4-bam/files/39323/Manuskriptversion_Michalik-Onichimowska_and_Kern_et_al_Click_analytics_for_click_chemistry.pdf