Robert PolliceJul 26, 2025
#RobSelects preprint of the week #ChemRxiv: Transferring samples between automated stations using a swarm of drone carts driving on near ceiling tracks. #autochem https://doi.org/10.26434/chemrxiv-2025-8x3zv
The 2D-drone swarm, a safe open-source sample transfer system for laboratory full automation

Laboratory automation is an active field in biology, drug discovery, and more recently in synthetic chemistry and materials science. Local automation has existed in the field for quite some time, but long-range or total laboratory automation is much less developed. In this article, we present a complete, open and decentralized, global automation system called the 2D drone swarm system. It is based on a simple approach of small mobile robots moving autonomously in a dedicated track suspended above the scientific equipment for the long-distance sample and closely connected to localized robotic arms dedicated to short-distance transfers, interaction with scientific equipment and direct sample processing. This approach is inspired by the Kiva/Amazon model, where isolated autonomous mobile robots automatically deliver goods to external operators. It is also inspired by the modern automotive industry, such as Tesla's Gigafactories, to provide an evolutionary and flexible system that can adapt to numerous types of tasks with a minimum of resources and easily adapt to different types of workstations. This global automation system is controlled directly from the Laboratory Scheduler by a Robot Subscheduler, coded in an open-source environment, which takes care of all mobile and local robot operations. The result is an operator and scientific equipment safe, cost and energy-efficient, easily extensible and open-source global laboratory automation system that can be adapted to many different applications and laboratories.

ChemRxiv
Robert PolliceJul 17, 2025
#RobSelects preprint of the week #ChemRxiv: A frugal flow-based self-driving laboratory platform for optimization of diverse organic reactions. #autochem https://doi.org/10.26434/chemrxiv-2025-73xqf
A Flexible and Affordable Self-Driving Laboratory for Automated Reaction Optimization

Self-driving laboratories (SDLs) have the potential to revolutionize chemical discovery and optimization, yet their widespread adoption remains limited by high costs, complex infrastructure, and limited accessibility. Here, we introduce RoboChem-Flex, a low-cost, modular self-driving laboratory platform designed to democratize autonomous chemical experimentation. The system combines customizable, in-house-built hardware with a flexible Python-based software framework that integrates real-time device control and advanced Bayesian optimization strategies, including multi-objective and transfer learning workflows. RoboChem-Flex supports both fully autonomous closed-loop operation and human-in-the-loop configurations, enabling seamless integration with shared analytical equipment and minimizing entry barriers. We validate the versatility of the platform across six diverse case studies, including photocatalysis, biocatalysis, thermal cross-couplings, and enantioselective catalysis, spanning both single and multi-objective optimizations. Through these campaigns, we demonstrate RoboChem-Flex’s ability to navigate large, complex chemical spaces, autonomously identify scalable high-performance reaction conditions, and flexibly adapt to a variety of analytical setups. By providing an affordable, scalable, and open platform, RoboChem-Flex offers a tangible step toward making SDLs accessible to resource-limited laboratories, fostering broader participation in automated chemical research.

ChemRxiv
Robert PolliceJun 10, 2025
#RobSelects preprint of the week #ChemRxiv: Integrating computer vision into self-driving laboratories. #autochem https://doi.org/10.26434/chemrxiv-2025-sxfvl
Closed-Loop: Vision-Guided Experimental Control in Self-Driving Labs

In iterative optimization, actions are adjusted based on what we see—such as dosing until dissolution or stirring until mixing is complete. Self-driving laboratories (SDLs) offer an opportunity to guide experimental adjustments based on such visual feedback in an autonomous, iterative way. However, current SDLs do not monitor these visual cues. HeinSight 4.0 fills this gap by integrating computer vision into SDLs to enable real-time experimental adjustments based on visual feedback. The computer vision detects equipment (e.g., reactor, vial), classifies chemical phases (solid, liquid, air), and analyzes image features such as turbidity and color. HeinSight 4.0 tracks these physical characteristics frame by frame and interprets physical states (e.g., dissolution, separation). This data feeds into a rule-based system that integrates with the SDL to make real-time experimental adjustments. We demonstrate HeinSight 4.0 adaptability across two pharmaceutical case studies: purification (solubility screening) and drug formulation (melt spray congeal). We also developed a hardware-agnostic architecture and deployed it across two institutions with distinct robotic systems. The open-source HeinSight 4.0 enables SDLs to see, think, and act in real time.

ChemRxiv
Robert PolliceMay 12, 2025
#RobSelects preprint of the week #ChemRxiv: Enabling automated iterative carbon-nitrogen cross coupling via a boronate-substituted carboxy benzyl protecting group for amines. #autochem https://doi.org/10.26434/chemrxiv-2025-fvznl
Automated Iterative N-C and C-C Bond Formation

Small molecule solutions to many contemporary societal challenges await discovery, but the artisanal and manual process via which this class of chemical matter is typically accessed limits the discovery of new functions. Automated iterative cross-coupling with (N-methyl iminodiacetic acid) MIDA or (tetramethyl N-methyl iminodiacetic acid) TIDA boronate building blocks alternatively enables generalized and automated preparation of many different types of small molecules in a modular fashion. But in its current form, this engine cannot leverage nitrogen atoms as iteration handles. Here, we disclose a new iteration-enabling group, CbzT, that reversibly attenuates the reactivity of nitrogen atoms and enables generalized catch-and-release purification. CbzT is leveraged to achieve the automated modular synthesis of Imatinib (Gleevec), an archetypical clinically approved kinase inhibitor, in which building blocks are iteratively linked by both N-C and C-C bonds. This work substantially expands the types of small molecules that can be made in an automated modular fashion. It also advances the concept of intentionally developing chemistry that machines can do.

ChemRxiv
Robert PolliceFeb 24, 2025
#RobSelects paper of the week #ACSCentSci: Automated flow-based platform for high-throughput optimization of triplet-triplet annihilation photon upconversion of sensitizer-annihilator mixtures. #autochem https://doi.org/10.1021/acscentsci.4c02059
Robert PolliceAug 11, 2024
#RobSelects preprint of the week #ChemRxiv: Automated screening platform for late-stage carbon-hydrogen borylation. #autochem https://doi.org/10.26434/chemrxiv-2024-cs5vg
High-throughput enabled iridium-catalyzed C-H borylation platform for late-stage functionalization

In this work, we present an automated platform designed to facilitate the expedited use of late-stage C-H borylation in fast-moving discovery chemistry projects. Our microscale reaction optimization panel emphasizes the regiodivergent nature of different borylation protocols and provides a rapid assessment of all accessible positions with minimal starting material consumption. The approach taken provides a rapid and sustainable tool to evaluate reaction conditions targeting multiple C-H bonds. We illustrate the workflow by screening numerous fragment-like compounds, drugs and agrochemicals and demonstrate its practicality by successfully isolating 36 derivatives of bioactive compounds. Additionally, a systematic comparison of various catalytic methods using an informer library approach provides valuable insights regarding the desirable future direction of C-H borylation research.

ChemRxiv
Robert PolliceJul 16, 2024
#RobSelects preprint of the week #ChemRxiv: Simple open-Source scheduling environment for self-driving laboratories. #autochem https://doi.org/10.26434/chemrxiv-2024-bf0bq-v2
GLAS : An open-source easily expandable Git-based Scheduling Architecture for integral Lab Automation

This paper presents GLAS (Git-based Lab Automated Scheduler or Get Lab Automation Simplified), an open-source, robust, and highly expandable Git-based architecture designed for laboratory automation. GLAS can be deployed in both partially and fully automated experimental science laboratories, enabling the development of a multi-layer scheduling system while maintaining a systematic architecture grounded in a Git repository. We demonstrate the effectiveness of GLAS through case studies from the Swiss Cat+ automated chemistry laboratory, showcasing its versatility and potential for widespread applicability in various laboratory automation contexts. By offering an open-source scheduling environment, our aim is to foster the development of accessible and adaptable laboratory automation solutions within the scientific community.

ChemRxiv
Robert PolliceOct 23, 2023
#RobSelects preprint of the week #ChemRxiv: Platform for autonomous study of electrochemical reaction mechanisms via cyclic voltammetry. #autochem https://doi.org/10.26434/chemrxiv-2023-psqxj
Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation

Electrochemical research often requires stringent combinations of experimental parameters that are demanding to manually locate. Recent advances in automated instrumentation and machine-learning algorithms unlock the possibility for accelerated studies of electrochemical fundamentals via high-throughput, online decision-making. Here we report an autonomous electrochemical platform that implements an adaptive, closed-loop workflow for mechanistic investigation of molecular electrochemistry. As a proof-of-concept, this platform autonomously identifies and investigates an EC mechanism, an interfacial electron transfer (E step) followed by a solution reaction (C step), for cobalt tetraphenylporphyrin exposed to a library of organohalide electrophiles. The generally applicable workflow accurately discerns the EC mechanism’s presence amid negative controls and outliers, adaptively designs desired experimental conditions, and quantitatively extracts kinetic information of the C step spanning over 7 orders of magnitude, from which mechanistic insights into oxidative addition pathways are gained. This work opens opportunities for autonomous mechanistic discoveries in self-driving electrochemistry laboratories without manual intervention.

ChemRxiv
Robert PolliceAug 8, 2023
#RobSelects paper of the week #ChemicalScience: Developing and demonstrating a multi-droplet reaction platform with parallel reaction channels for reaction optimization. #autochem https://doi.org/10.1039/D3SC02082G
Parallel multi-droplet platform for reaction kinetics and optimization

We present an automated droplet reactor platform possessing parallel reactor channels and a scheduling algorithm that orchestrates all of the parallel hardware operations and ensures droplet integrity as well as overall efficiency. We design and incorporate all of the necessary hardware and software to enable the p

Robert PolliceJul 17, 2023
#RobSelects preprint of the week #ChemRxiv: Implementing a droplet flow reactor with spectroscopic and chromatographic analysis capabilities showcased via the optimization of a Buchwald-Hartwig amination. #autochem https://doi.org/10.26434/chemrxiv-2023-gb117
A Droplet Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction Optimization

Flow processing offers many opportunities to optimize reactions in a rapid and automated manner, yet often requires relatively large quantities of input materials. To combat this, we report the use of a flexible droplet flow reactor, equipped with two analytical instruments, for low-volume optimization experiments. A Buchwald-Hartwig amination toward the drug olanzapine, with 6 independent optimizable variables, was optimized using three different automated approaches: self-optimization, design of experiments and kinetic modeling. These approaches are complementary and provide differing information on the reaction: pareto optimal operating points, response surface models and mechanistic models, respectively. The results were achieved using <10% of the material that would be required for standard flow operation. Finally, a chemometric model was built utilizing automated data handling and three subsequent validation experiments demonstrated good agreement between the droplet flow reactor and a standard (larger scale) flow reactor.

ChemRxiv