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Home Automated Lab Reactors and In-Situ Analysis Automated Sampling Systems

Automated Reactor Sampling System

EasySampler for Unattended, Representative Sampling

Automated sampling enables increased productivity by addressing challenges associated with manual sampling tools such as pipettes and syringes. Automated, inline chemical reaction sampling with EasySampler™ is an intuitive and seamless extension that streamlines the experimental workflow for every chemist while eliminating the tedious task of manual sampling.

EasySampler is an automated and unattended sampling solution that delivers high-quality samples day and night for standard offline analysis, such as HPLC. This unique probe-based technology features a patented micro-pocket and allows for:

  • Sampling and quenching in situ and at reaction conditions
  • Taking samples at scheduled intervals
  • Diluting the sample in preparation for offline analysis
  • Simple operation with no expert knowledge required

EasySampler Automated Inline Sampling System

EasySampler Automated Inline Sampling System
Increase productivity and eliminate tedious tasks.
EasySampler 1210 Automated Sampling System for Chemical Reactors

EasySampler 1210 System

EasySampler eliminates sampling challenges—taking representative and reproducible analytical samples from almost any chemical reaction has never been easier!

Automated and unattended
Gain complete reaction profiles and reaction understanding, and improve yield and product quality—never miss a sample.

Superior quality samples
Improved reaction information by linking process conditions with the points at which species form.

Wide range of chemistries
Easy sampling of a wide range of chemistries, including slurries and reactions that are air and moisture-sensitive, even at elevated temperature and pressure.

HPLC-Ready Samples around the Clock

Increased productivity and understanding

The obligations of the workday (or night) often do not allow sampling of reactions as often as required. This leads to lost or missing data. Frequently, these missing data points omit optimum end-point and important understanding of impurity formation. EasySampler executes representative and reproducible sampling including in-situ quench and dilution—providing LC-ready samples at any time.

Sampling from reactors with EasySampler enables scientists to optimize their workflow and productivity by enabling increased reaction understanding while eliminating tedious manual sample practices. For even further productivity gains, sampling can be automatically scheduled or triggered as a function of varying process parameters including temperature, stirring, dosing, pH, or other real-time process information.

automated chemical reaction sampling

Solution-Phase Sampling with EasyFrit Filter

Automated sampling for crystallization process development

Expand the capabilities of EasySampler with the seamless integration of EasyFrit. The EasyFrit filter is compatible with all EasySampler probes and allows for selective sampling of the solution phase despite the presence of suspended particles. This accessory creates an easy-to-use, automated sampling solution for crystallization studies.

EasySampler with EasyFrit facilitates data-rich experimentation throughout a controlled crystallization. Embedding this solution in a particle engineering workstation enables a better understanding of the solution-phase by creating impurity profiles, uncovering the effects of supersaturation on impurity, and providing kinetic and thermodynamic information of the crystallization process.

automated sampling filter

automated sampling filter

Truly Representative Reaction Progression

Eliminate critical data gaps

EasySampler's intuitive touchscreen allows scientists to preprogram sampling and dilution sequences to track reaction progression over time—without manual intervention. This allows every scientist to reproducibly capture a representative sample for offline analysis, such as HPLC or NMR, to understand reaction pathways, kinetics, intermediates, and impurity profiles.

Automated sampling with EasySampler can be used in round bottom flasks, jacketed lab reactors, or seamlessly integrated with automated chemical reactors. This allows straightforward identification of the impact of varying process conditions on reaction performance. 

automated sampling reporting
pfizer demonstrates automated chemical sampling

Pfizer Evaluates Automated Sampling

View this white paper that highlights four case studies where Pfizer researchers successfully used unattended, automated chemical reaction sampling:

  1. Removing Barriers for Impurity and Kinetics Profiling in an Ullman Reaction
  2. Improving Confidence by Measuring the Effect of Process Parameters on an Imidazolide Reaction
  3. Extending Productivity with Endpoint Detection in a C–H Reaction
  4. Ensuring Successful Scale-up of an Amination Reaction

Reactions that Pfizer successfully sampled include:

  • Thick slurries
  • Dark mixtures with inorganic salts present
  • Tri-phasic mixtures
  • Oxygen-sensitive reactions

Sample Challenging Reactions

From slurries to elevated temperature

Manual sampling does not always provide precise and reproducible samples, especially in heterogeneous and multiphase reactions or reactions at sub-ambient or elevated temperatures and pressures. Delays in quenching typically lead to variable results and inaccurate analytical information. EasySampler provides an automated and robust inline method of capturing and quenching samples from reactions—even under difficult conditions.

The flexible nature of EasySampler allows scientists to obtain high-quality samples from a variety of processes from vial to kilo scale equipment including pressure vessels and reactor systems.

automated reaction sampling at elevated temperatures

Representative, Reproducible Results

More high-quality data points per experiment

Automated sampling is a key enabler for reaction understanding as it eliminates sampling error and ensures that offline analytical results truly represent the reaction as it exists under process conditions.

Representative, reproducible results are achieved by:

  • Sampling from the same position in the reactor, at reaction conditions
  • Time-stamped samples—circumvent errors caused by variances in recordkeeping. Data can be easily combined with results from inline PAT-based analytical methods.
  • Automatic and seamless data collection—increase the number of samples and data quality that also supports data-rich experimentation

easysampler vials

Limit Exposure to Hazardous Chemistry

Safety by Design

When working in the laboratory, scientists are potentially exposed to hazardous chemicals and are at risk of injury.

When taking samples manually using traditional sampling tools, the operator needs to open the reactor which can be exposed to toxic materials at elevated temperatures or pressures.

EasySampler automated reactor sampling system reduces the need for manual handling and exposure to hazardous chemicals, reducing risks and improving user safety.

automated reactor sampling system touchscreen
EasySampler automated reactor sampling system

Expand Your Capabilities

With the connectivity kit, EasySampler sampling data is automatically transferred to, and reported with, the referring automated reactor system.

Increase automation capabilities and improve productivity by combining automated sampling with synthesis reactors. All data from every connected instrument is collected at the end of an experiment. Data is stored in a common location with optional email notifications with links to files for each experiment.

What is an automated reactor sampling system and how does it work?

automated reactor sampling system

automated reactor sampling system

EasySampler combines three sampling steps into a single automated operation, sampling the same way, every time to provide reproducible and accurate samples.

  • Accurate sampling starts when the micro-pocket moves out and is immersed within the reaction mixture to ensure a representative sample is taken
  • In-situ quench immediately stops the reaction and ensures the time point is representative
  • Subsequent dilution and dispensing in the vial prepare samples for offline analysis  

These steps allow samples to be highly reproducible and provide representative analytical results. Above all, automated samples can be taken at preprogrammed or scheduled times. 

What are some common applications for automated reactor sampling?

What is the temperature range of the EasySampler probe?

At atmospheric pressure, EasySampler probes are rated for temperatures in the range of -20 to 140 °C. It is recommended to change sleeves after 100 samples within this temperature range, at atmospheric pressure. For reactions at elevated pressures, between 1.013 bar and 10 bar, the temperature range is 20 to 100 °C. 

Why should I automate my sampling of chemical reactions?

Sampling chemical reactions for offline analysis to determine reaction progress or impurity profiles is standard practice. However, the manual sampling process is challenging because it requires physical presence and experience, especially with air-sensitive reactions, reactions at elevated pressure, or studies with heterogeneous mixtures.

Benefits from automating manual sampling include:

  • Increased sample-to-sample representativeness and overall quality of data
  • Increased productivity by freeing scientists from time-consuming work
  • Increased efficiency by capturing more data from a single experiment and fewer repetitions
  • Increased understanding of complete and comprehensive datasets spanning over multiple hours/days
  • Sample at fixed time intervals or upon reaction events like reaching temperature setpoint or pH

Automate your sampling today with EasySampler.

I have a reactor from another vendor; can I use EasySampler with this reactor?

Yes, EasySampler can function as a stand-alone device and can be used in any reactor, including tube reactors, round-bottomed flasks, jacked lab reactors (JLR), and automated lab reactors (ALR). Points to consider:

  • All models of EasySampler probes are 9.5 mm in diameter
  • An appropriate adapter should be used to fit the EasySampler probe securely into the reactor port

Can EasySampler be used to sample reactions at elevated pressure?

Yes, EasySampler can sample reactions under pressure if all the following reaction conditions are met: 

  1. Pressure range: 1.013 bar – 10 bar (14.7 psi – 145 psi) 
  2. Temperature range: 20 to 100 °C 
  3. Reactor volume: up to 2500 mL 
  4. Number of samples per sleeve: 1 reaction (with up to 24 samples)
  5. High-pressure adapter: An appropriate high-pressure adapter (P/N14474404) must be used to securely position the EasySampler probe in the reactor

Note: Use of the EasySampler probe at elevated pressure (between 1.013 bar to 10 bar) will reduce the temperature range to 20 °C to 100 °C, the maximum reactor volume to 2500 mL, and the maximum number of samples per sleeve to 1 reaction (with up to 24 samples).

What is the automated sampling process?

The automated sampling process refers to the use of automated systems and equipment to collect samples for analysis in various industries such as pharmaceuticals and chemical manufacturing.

Automated sampling typically involves the use of a sampling system that is designed to obtain representative samples of materials or substances from a process. The sample is then typically transfered to analytical instrument for analysis.

What are the benefits of automated sampling over a manual injection system?

There are several benefits of automated sampling over a manual injection system, including:

  • Improved accuracy 
  • Increased efficiency
  • Consistency
  • Increased safety
  • Cost savings

What is the purpose of automated sampling in reaction modeling?

The purpose of automated sampling in reaction modeling is to obtain data on the reaction progress over time and to analyze the behavior of the reaction under different conditions. Automated sampling systems can be used to collect samples at regular intervals, allowing researchers to monitor the reaction progress and to identify reaction intermediates, products, and byproducts. This information can then be used to develop mathematical models to describe the reaction and to optimize the reaction conditions.

Learn more about chemical reaction modeling.

What are the challenges when sampling slurries?

Slurries are heterogeneous mixtures containing different types and sizes of particles, which can make it difficult to obtain representative samples. The particles in the slurry can cause blockages in the sampling equipment, which can affect the accuracy and representativeness of the sample.

EasySampler allows for selective sampling of the solution phase despite the presence of suspended particles. This autosampler creates an easy-to-use, automated sampling solution for crystallization studies.

 

autosampling of a slurry solution
pfizer automated sampling

Pfizer Evaluates Automated Sampling for Improved Impurity Profiling

In Pfizer's synthetic chemistry and process development labs, an automated and robust line method is...

EasySampler Tips and Tricks

EasySampler Tips and Tricks

Tips and tricks for EasySampler, including set-up, probe introduction, data management and sampling...

Crystallization and Filtration Application Note

Crystallization and Filtration Techniques at MSD Corp.

This study evaluated the performance of an automated, unattended, selective liquid phase sampling so...

pfizer autosampling

Sampling of Chemical Reactions at Pfizer

Dr. David Place reviews recent studies at Pfizer focused on the accurate and precise autosampling of...

The Modern Synthesis Lab

The Modern Synthesis Lab

This white paper discusses a new toolbox specifically designed for chemists that expands experimenta...

Endpoint Detection of a Hydrogenation

Endpoint Detection of a Hydrogenation

Automated sampling of a hydrogenation at 5 bar pressure improved product quality by enabling the stu...

Automated Chemical Sampling Systems in Peer-Reviewed Publications

Continued automated sampling with the EasySampler supports reaction and impurity profiling studies. A list of publications from peer-reviewed journals focuses on exciting and novel applications of EasySampler by researchers in academia and industry to support data-rich experimentation and advance their research.

2024

  • Agrawal, S., Rawal, S. H., Reddy, V. R., & Merritt, J. M. (2024). Use of automation, dynamic image analysis, and process analytical technologies to enable data rich particle engineering efforts at the Drug Substance / Drug Product interface: A case study using Lovastatin. Process Safety and Environmental Protection/Transactions of the Institution of Chemical Engineers. Part B, Process Safety and Environmental Protection/Chemical Engineering Research and Design/Chemical Engineering Research & Design. https://doi.org/10.1016/j.cherd.2024.04.032
  • McMullen, J. P., Wyvratt, B. M., Hong, C. M., & Purohit, A. K. (2024). Integrating Functional Principal Component Analysis with Data-Rich Experimentation for Enhanced Drug Substance Development. Organic Process Research & Development, 28(3), 719–728. https://doi.org/10.1021/acs.oprd.3c00379

2023

  • Cox, R. J., McCreanor, N. G., Morrison, J. A., Munday, R. H., & Taylor, B. (2023). Copper-Catalyzed Racemization-Recycle of a quaternary center and optimization using a combined Kinetics-DOE/MLR modeling approach. Journal of Organic Chemistry, 88(9), 5275–5284. https://doi.org/10.1021/acs.joc.2c02588
  • Deem, M. C., & Hein, J. E. (2023). A Method for Converting HPLC Peak Area from Online Reaction Monitoring to Concentration Using Nonlinear Regression. Journal of Organic Chemistry, 88(2), 1292–1297. https://doi.org/10.1021/acs.joc.2c02737
  • Kaldre, D., Stocker, S., Linder, D., Reymond, H., Schuster, A., Lamerz, J., Hildbrand, S., Püntener, K., Berry, M., & Sedelmeier, J. (2023). Development of a continuous flow grignard reaction to manufacture a key intermediate of Ipatasertib. Organic Process Research & Development. https://doi.org/10.1021/acs.oprd.3c00235
  • Kukor, A. J., St-Jean, F., Stumpf, A., Malig, T. C., Piechowicz, K. A., Kurita, K., & Hein, J. E. (2023). Guided optimization of a Crystallization-Induced diastereomer transformation to access a key navoximod intermediate. Reaction Chemistry and Engineering, 8(6), 1294–1299. https://doi.org/10.1039/d3re00077j

2022

  • Deadman, B. J., Gian, S., Lee, V. E. Y., Adrio, L. A., Hellgardt, K., & Hii, K. K. (2022). On-demand, in situ, generation of ammonium caroate (peroxymonosulfate) for the dihydroxylation of alkenes to vicinal diols. Green Chemistry. https://doi.org/10.1039/d2gc00671e
  • Deem, M. C., Derasp, J. S., Malig, T. C., Legard, K., Berlinguette, C. P., & Hein, J. E. (2022). Ring walking as a regioselectivity control element in Pd-catalyzed C-N cross-coupling. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-30255-1
  • Kukor, A. J., Depner, N., Cai, I., Tucker, J. V., Culhane, J. C., & Hein, J. E. (2022). Enantioselective synthesis of (−)-tetrabenazine via continuous crystallization-induced diastereomer transformation. Chemical Science, 13(36), 10765–10772. https://doi.org/10.1039/d2sc01825j
  • LaPorte, A. J., Shi, Y., Hein, J. E., & Burke, M. D. (2022). Stereospecific Csp3 Suzuki–Miyaura Cross-Coupling That Evades β-Oxygen Elimination. ACS Catalysis, 12(17), 10905–10912. https://doi.org/10.1021/acscatal.2c03245
  • Lomont, J. P., Ralbovsky, N. M., Guza, C., Saha-Shah, A., Burzynski, J., Konietzko, J., Wang, S.-C., McHugh, P. M., Mangion, I., & Smith, J. P. (2022). Process monitoring of polysaccharide deketalization for vaccine bioconjugation development using in situ analytical methodology. Journal of Pharmaceutical and Biomedical Analysis, 209, 114533. https://doi.org/10.1016/j.jpba.2021.114533
  • Malig, T. C., Kumar, A., & Kurita, K. L. (2022). Online and In Situ Monitoring of the Exchange, Transmetalation, and Cross-Coupling of a Negishi Reaction. Organic Process Research & Development, 26(5), 1514–1519. https://doi.org/10.1021/acs.oprd.2c00081
  • Spöring, J., Wiesenthal, J., Pfennig, V., Gätgens, J., Beydoun, K., Bolm, C., Klankermayer, J., & Rother, D. (2022). Effective production of selected dioxolanes by sequential bio‐ and chemocatalysis enabled by adapted solvent switching. Chemsuschem, 16(2). https://doi.org/10.1002/cssc.202201981
  • Wheelhouse, K. M. P., Fenner, S., & Whiting, M. (2022). Application of High-Throughput experimentation in identification of conditions for selective nitro group hydrogenation. In Acs Symposium Series (pp. 79–91). https://doi.org/10.1021/bk-2022-1420.ch005

2021

  • Ashworth, I. W., Frodsham, L., Moore, P., & Ronson, T. O. (2021). Evidence of Rate Limiting Proton Transfer in an SNAr Aminolysis in Acetonitrile under Synthetically Relevant Conditions. The Journal of Organic Chemistry. https://doi.org/10.1021/acs.joc.1c01768
  • Jurica, J. A., & McMullen, J. P. (2021). Automation Technologies to Enable Data-Rich Experimentation: Beyond Design of Experiments for Process Modeling in Late-Stage Process Development. Organic Process Research & Development, 25(2), 282–291. https://doi.org/10.1021/acs.oprd.0c00496
  • Kukor, A. J., Guy, M. A., Hawkins, J. M., & Hein, J. E. (2021). A robust new tool for online solution-phase sampling of crystallizations. Reaction Chemistry and Engineering, 6(11), 2042–2049. https://doi.org/10.1039/d1re00284h
  • Pollack, S. R., & Dion, A. (2021). Metal-Free Stereoselective Synthesis of (E)- and (Z)-N-Monosubstituted β-Aminoacrylates via Condensation Reactions of Carbamates. The Journal of Organic Chemistry, 86(17), 11748–11762. https://doi.org/10.1021/acs.joc.1c01212
  • Zhao, X., Webb, N. J., Muehlfeld, M. P., Stottlemyer, A. L., & Russell, M. W. (2021). Application of a Semiautomated Crystallizer to Study Oiling-Out and Agglomeration Events—A Case Study in Industrial Crystallization Optimization. Organic Process Research & Development, 25(3), 564–575. https://doi.org/10.1021/acs.oprd.0c00494

2020

  • Malig, T. C., Yunker, L. P. E., Steiner, S., & Hein, J. E. (2020). Online High-Performance Liquid Chromatography Analysis of Buchwald–Hartwig Aminations from within an Inert Environment. ACS Catalysis, 10(22), 13236–13244. https://doi.org/10.1021/acscatal.0c03530
  • Malig, T. C., Tan, Y., Wisniewski, S. R., Higman, C. S., Carrasquillo-Flores, R., Ortiz, A., Purdum, G. E., Kolotuchin, S., & Hein, J. E. (2020). Development of a telescoped synthesis of 4-(1H)-cyanoimidazole core accelerated by orthogonal reaction monitoring. Reaction Chemistry & Engineering, 5(8), 1421–1428. https://doi.org/10.1039/d0re00234h

2019

  • Beutner, G. L., Coombs, J. R., Green, R. A., Inankur, B., Lin, D., Qiu, J., Roberts, F., Simmons, E. M., & Wisniewski, S. R. (2019). Palladium-Catalyzed Amidation and Amination of (Hetero)aryl Chlorides under Homogeneous Conditions Enabled by a Soluble DBU/NaTFA Dual-Base System. Organic Process Research & Development, 23(8), 1529–1537. https://doi.org/10.1021/acs.oprd.9b00196
  • Carter, H. L., Connor, A. W., Hart, R., McCabe, J., McIntyre, A. C., McMillan, A. E., Monks, N. R., Mullen, A. K., Ronson, T. O., Steven, A., Tomasi, S., & Yates, S. D. (2019). Rapid route design of AZD7594. Reaction Chemistry & Engineering, 4(9), 1658–1673. https://doi.org/10.1039/c9re00118b
  • Huffman, M. A., Fryszkowska, A., Alvizo, O., Borra-Garske, M., Campos, K. R., Canada, K. A., Devine, P. N., Duan, D., Forstater, J. H., Grosser, S. T., Halsey, H. M., Hughes, G. J., Jo, J., Joyce, L. A., Kolev, J. N., Liang, J., Maloney, K. M., Mann, B. F., Marshall, N. M., & McLaughlin, M. (2019). Design of an in vitro biocatalytic cascade for the manufacture of islatravir. Science, 366(6470), 1255–1259. https://doi.org/10.1126/science.aay8484
  • Mennen, S. M., Alhambra, C., Allen, C. L., Barberis, M., Berritt, S., Brandt, T. A., Campbell, A. D., Castañón, J., Cherney, A. H., Christensen, M., Damon, D. B., Eugenio de Diego, J., García-Cerrada, S., García-Losada, P., Haro, R., Janey, J., Leitch, D. C., Li, L., Liu, F., Lobben, P. C., MacMillan, D. W. C., Magano, J., McInturff, E., Monfette, S., Post, R. J., Schultz, D., Sitter, B., Stevens, J. M., Strambeanu, I. I., Twilton, J., Wang, K., & Zajac, M. A. (2019). The Evolution of High-Throughput Experimentation in Pharmaceutical Development and Perspectives on the Future. Organic Process Research & Development, 23(6), 1213–1242. https://doi.org/10.1021/acs.oprd.9b00140
  • Wang, K., Han, L., Mustakis, J., Li, B., Magano, J., Damon, D. B., Dion, A., Maloney, M. T., Post, R., & Li, R. (2019). Kinetic and Data-Driven Reaction Analysis for Pharmaceutical Process Development. Industrial & Engineering Chemistry Research, 59(6), 2409–2421. https://doi.org/10.1021/acs.iecr.9b03578

2018

  • Nykaza, T. V., Ramirez, A., Harrison, T. S., Luzung, M. R., & Radosevich, A. T. (2018). Biphilic Organophosphorus-Catalyzed Intramolecular Csp2–H Amination: Evidence for a Nitrenoid in Catalytic Cadogan Cyclizations. Journal of the American Chemical Society, 140(8), 3103–3113. https://doi.org/10.1021/jacs.7b13803

2017

  • Malig, T. C., Koenig, J. D. B., Situ, H., Chehal, N. K., Hultin, P. G., & Hein, J. E. (2017). Real-time HPLC-MS reaction progress monitoring using an automated analytical platform. Reaction Chemistry & Engineering, 2(3), 309–314. https://doi.org/10.1039/c7re00026j
  • Rougeot, C., Situ, H., Cao, B. H., Vlachos, V., & Hein, J. E. (2017). Automated reaction progress monitoring of heterogeneous reactions: crystallization-induced stereoselectivity in amine-catalyzed aldol reactions. Reaction Chemistry & Engineering, 2(2), 226–231. https://doi.org/10.1039/c6re00211k
  • Zawatzky, K., Grosser, S., & Welch, C. J. (2017). Facile kinetic profiling of chemical reactions using MISER chromatographic analysis. Tetrahedron, 73(33), 5048–5053. https://doi.org/10.1016/j.tet.2017.05.048

2016

  • Duan, S., Place, D., Perfect, H. H., Ide, N. D., Maloney, M., Sutherland, K., Price Wiglesworth, K. E., Wang, K., Olivier, M., Kong, F., Leeman, K., Blunt, J., Draper, J., McAuliffe, M., O’Sullivan, M., & Lynch, D. (2016). Palbociclib Commercial Manufacturing Process Development. Part I: Control of Regioselectivity in a Grignard-Mediated SNAr Coupling. Organic Process Research & Development, 20(7), 1191–1202. https://doi.org/10.1021/acs.oprd.6b00070
  • Gurung, S. R., Mitchell, C., Huang, J., Jonas, M., Strawser, J. D., Daia, E., Hardy, A., O’Brien, E., Hicks, F., & Papageorgiou, C. D. (2016). Development and Scale-up of an Efficient Miyaura Borylation Process Using Tetrahydroxydiboron. Organic Process Research & Development, 21(1), 65–74. https://doi.org/10.1021/acs.oprd.6b00345