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.

#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

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.

#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

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.

#RobSelects preprint of the week #ChemRxiv: Integrating computer vision into self-driving laboratories. #autochem https://doi.org/10.26434/chemrxiv-2025-sxfvl

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.

#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

#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