Automated air sensitivity profiling is a quantitative approach to assess how chemical compounds degrade when exposed to air over time, using controlled and repeatable workflows rather than manual observation. This helps researchers better understand the stability and behaviour of air-sensitive species, reducing experimental uncertainty and improving reproducibility.
In the publication “ReactPyR: a python workflow for ReactIR allows for quantification of the stability of sensitive compounds in air”, Nicola Bell and his team present an automated workflow that combines digital control, automated liquid handling and inline spectroscopy to systematically profile air sensitivity of reactive compounds.
Congratulations to the authors for this work, which demonstrates how automated workflows can overcome limitations of conventional methods and brings new quantitative insight to a domain where qualitative classifications have long prevailed.
Automated air sensitivity profiling is a method used to quantify how sensitive chemical compounds are to air exposure in a controlled and repeatable way. Many chemical species degrade, react, or lose activity when exposed to oxygen or moisture. Understanding this sensitivity is essential to design reliable experiments, safe handling procedures, and scalable chemical processes.
Traditionally, air sensitivity is evaluated using manual experiments and qualitative observations, such as visual changes, loss of signal, or reaction failure. These approaches are often time-consuming, difficult to reproduce, and strongly dependent on the operator and experimental conditions. As a result, comparing air sensitivity between compounds or across laboratories remains challenging.
Automated air sensitivity profiling addresses these limitations by combining automated liquid handling, precise timing control, and online analytical readout. Exposure to air can be applied in a defined and repeatable manner, while the chemical response is measured over time. This enables a quantitative evaluation of degradation and stability, with reduced uncertainty and improved reproducibility.
By turning air sensitivity from a qualitative label into a measurable parameter, automated profiling provides researchers with clearer insights and more reliable data, especially when working with sensitive chemical systems and time-dependent processes.
Profiling air-sensitive chemical systems is challenging because degradation can occur rapidly and is often influenced by multiple parameters at the same time. Exposure to oxygen or moisture may trigger side reactions, loss of activity, or complete decomposition, sometimes within seconds. This makes it difficult to capture reliable data, especially when timing and handling are not tightly controlled.
Conventional approaches typically rely on manual handling and step-by-step evaluation. Small variations in exposure time, flow conditions, or sample transfer can lead to large differences in results. As a consequence, uncertainty remains high and results are often hard to reproduce or compare across experiments, laboratories, or research groups.
Another limitation is the experimental effort required. Manual profiling is time-consuming and poorly suited for systematic studies or comparative analysis across multiple compounds. Scaling these experiments increases cost and workload, while still offering limited quantitative resolution.
These challenges highlight the need for more structured and automated approaches. To reliably assess air sensitivity, workflows must ensure precise control of exposure conditions, stable fluid handling, and consistent data acquisition over time.
In this publication, the authors introduce an automated workflow designed to systematically profile the air sensitivity of chemical compounds. The approach replaces manual handling steps with a digitally controlled process, allowing exposure to air and data acquisition to be precisely defined and repeated.
The core idea of the study is to transform air sensitivity evaluation from a qualitative observation into a quantitative and comparable measurement. This is achieved by combining controlled liquid handling with online analytical monitoring, enabling time-resolved analysis of compound stability.
The automated workflow presented in the study is built around several key elements:
By structuring the experiment as an automated process, the authors are able to reduce variability, limit operator-dependent effects, and improve the reliability of sensitivity profiling. This approach also makes it possible to compare different compounds under identical conditions, which remains difficult with conventional, manual methods.
Overall, the study demonstrates how automation and digital control can support more robust workflows for studying air-sensitive chemical systems, while remaining compatible with standard laboratory environments.
The automated air sensitivity profiling workflow described in this study relies on a combination of controlled liquid handling, precise timing, and online analytical monitoring. Together, these elements allow air exposure and data acquisition to be performed in a reproducible and systematic manner.
At the core of the method is an automated liquid handling setup that controls how samples are transferred, exposed to air, and delivered to the analytical tool. Flow rate, exposure time, and sequencing are digitally defined, which ensures that each experiment follows the same protocol and reduces variability introduced by manual handling.
Key methodological elements include:
The use of online spectroscopy allows degradation or transformation of air-sensitive compounds to be detected as it happens, rather than after the fact. This provides a more accurate picture of stability and sensitivity, especially for fast or time-dependent processes.
By combining these methods into a single automated workflow, the authors demonstrate a practical way to perform quantitative air sensitivity analysis with improved accuracy, repeatability, and experimental efficiency.
Reliable air sensitivity profiling depends on the ability to control time, exposure, and handling with a high level of precision. Even small variations in these parameters can strongly influence the outcome when working with air-sensitive compounds. Automation plays a central role in reducing this variability by ensuring that each step of the workflow is executed in the same way, every time.
In manual experiments, exposure to air often starts and ends in an uncontrolled manner, depending on operator actions. Automated workflows allow exposure time to be defined digitally and applied consistently across experiments. This makes it possible to link observed degradation directly to exposure duration, rather than to uncontrolled handling differences.
Automation also improves reproducibility by limiting human intervention. Flow rates, sample volumes, and sequencing are controlled by the system rather than by the operator, reducing uncertainty and experimental noise. As a result, the data collected is more stable and easier to compare across different compounds or experimental runs.
Beyond accuracy, automation enables scalability. Once a workflow is defined, multiple compounds or conditions can be evaluated using the same protocol, with minimal additional effort. This makes automated air sensitivity profiling suitable not only for individual studies, but also for broader screening or comparative projects where consistency and efficiency are essential.
Together, these aspects highlight why automation is not just a convenience, but a key requirement for transforming air sensitivity profiling into a robust and quantitative analytical method.
In this study, Advanced Microfluidics (AMF) liquid handling technology is integrated as part of the automated experimental workflow. The system is used to manage liquid transfer and exposure steps with a high level of precision, supporting the controlled conditions required for air sensitivity profiling.
The role of AMF is not to drive the analysis itself, but to ensure that key parameters such as flow rate, timing, and sequencing remain stable throughout the experiment. This stability is essential when evaluating sensitive compounds, where small variations in handling can introduce significant uncertainty in the results.
By embedding automated liquid handling into the workflow, the authors are able to reduce manual intervention and align sample delivery with online analytical monitoring. This integration allows exposure to air to be applied in a consistent manner, while chemical changes are tracked in real time, supporting the quantitative approach presented in the publication.
The use of AMF technology in this context illustrates how modular and programmable liquid handling systems can be combined with analytical tools to build reliable, automated workflows for studying air-sensitive chemical systems.
The automated workflow described in the publication relies on a laboratory programmable syringe pump to deliver stable and well-controlled liquid flow throughout the experiment. In this case, the authors use the LSPone syringe pump from Advanced Microfluidics, selected for its ability to combine precision, programmability, and ease of integration.
Accurate flow control is a critical requirement for air sensitivity profiling. Flow rate directly influences exposure time, shear conditions, and the repeatability of sample delivery to the analytical tool. The syringe pump used in this setup allows these parameters to be defined digitally and reproduced consistently across experiments, reducing variability linked to manual operation.
Another key aspect is stability over time. For time-resolved measurements, the pump must deliver a smooth and stable flow without pulsation or drift. This ensures that observed changes in the analytical signal are linked to chemical behaviour rather than to fluctuations in liquid handling.
Finally, the modular and programmable nature of the syringe pump makes it well suited for automated laboratory workflows. It can be integrated into larger systems, controlled via software, and adapted to different experimental protocols. This flexibility supports the development of automated methods that remain compatible with evolving research needs and sensitive chemical systems.
Automated air sensitivity profiling opens the door to a more structured and quantitative way of working with air-sensitive chemical systems. By replacing manual handling with automated workflows, researchers gain better control over experimental parameters and can generate data that is easier to interpret and compare.
One of the main benefits is the ability to compare compounds under identical conditions. Exposure time, flow rate, and sequencing can be kept constant across experiments, allowing sensitivity to be evaluated as a measurable parameter rather than a qualitative label. This supports clearer decision-making when selecting compounds, solvents, or process conditions.
Automation also improves experimental efficiency. Once a workflow is defined, profiling can be repeated with limited additional effort, making it suitable for systematic studies or screening projects. This reduces overall experimental cost and allows researchers to focus on data analysis rather than manual execution.
Finally, automated workflows improve reproducibility and data quality. By limiting operator-dependent variability and ensuring stable liquid handling, automated air sensitivity profiling provides more reliable datasets that can be shared, reviewed, and reused across teams or projects. This makes it a valuable approach for laboratories aiming to move from exploratory experiments toward more standardized and scalable research practices.
If you would like to explore the full methodology, experimental setup, and quantitative results in more detail, the complete publication is available online.
The article ReactPyR: a python workflow for ReactIR allows for quantification of the stability of sensitive compounds in air presents the full automated air sensitivity profiling workflow, including figures, data analysis, and discussion of the results.
If you are working on similar automated workflows or facing challenges with air-sensitive compounds, this study provides valuable insight into how automation and controlled liquid handling can support more reliable and quantitative research.
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