Chemspeed

Analytics usually get added last. That's where the trouble starts. Many teams build the synthesis or formulation platform first, then try to connect NMR, IR, Raman, or XRD later. It works, but samples get re-handled by hand and throughput drops right where you need it most. Design the analytics in from the start, and the platform stays flexible: • One make platform, your choice of modality: NMR, IR, Raman, and / or XRD • "Make" and "measure" stay in one loop, so no manual re-handling • Add or swap a technique as projects change, instead of rebuilding • Consistent prep means clean, comparable samples every run Analytics designed in on day one beats analytics forced in on year two. Speccing a new platform? Which technique would you want ready from the start: NMR, IR, Raman, and / or XRD? Explore the workflows with Bruker BioSpin, Bruker Optics, and Bruker AXS: NMR: https://lnkd.in/gwFmSbdD IR & Raman: https://lnkd.in/eBtjcTA3 XRD: https://lnkd.in/erNygWud #LabAutomation #AnalyticalChemistry #Chemspeed

Battery research still runs on manual protocols in 2026. That's the real bottleneck. Next-gen batteries and Power-to-X technologies won't come from faster computers. They'll come from faster, more reproducible experiments. Most labs still do synthesis, electrode prep and testing by hand. Slow, hard to scale, hard to turn into usable data. DIGIBAT, the UK's first fully automated energy research facility at Imperial College London, was built to change that. In our latest webinar, Facility Manager Dr. Jingyu Feng shares: • A fully automated workflow from synthesis to coin cell testing • Robotic platforms unified into one "Digital Twin Lab" • Early user case studies from the facility • A data pipeline built to feed ML tools directly Good automation isn't about replacing the scientist. It's about giving them data worth building on. Watch the full session: https://lnkd.in/egmaPVkK #Automation #BatteryResearch #Electrochemistry #LabAutomation

Weighing powders by hand is still a full-time job in most labs. Even in 2026. Solid dispensing sits at the center of reactions, formulations, analytics, and QC. Yet in many labs it's still manual work: a scientist standing at a balance, tipping powder in milligram by milligram, hoping the last pinch doesn't overshoot. It's slow, it's tedious, and it pulls your best people away from the science they were hired to do. Our overhead gravimetric solid dispensing solutions are built to take that burden off your team: • Cover the full range, from sub-milligram to gram and kilogram quantities • Choose from compact benchtop CRYSTAL systems up to larger integrated platforms, depending on your project scale • Combine dispensing with mixing, heating, refluxing, or cooling in the same run • Access more than 70 robotic tool features through our tool exchange technology • Scale the system as your workflows and throughput needs grow Precise solid handling shouldn't depend on a steady hand and a lot of patience. Learn more: https://lnkd.in/eMFDPWiJ #LabAutomation #Chemistry #RnD #ProcessDevelopment #Chemspeed

No „Roestigraben“ for lab robotics ;-) looking forward to the session with Pascal Miéville

Rare earth elements run modern life. Recovering them still runs on slow, manual chemistry. A team at Ames National Laboratory and Iowa State University set out to change that, and they published the result on ChemRxiv. The problem they tackled: developing the ligands used to extract rare earths usually means four disconnected stages. Substrate scope, process optimization, scale-up, and separation testing. Each step is run separately, by hand, and the handoffs eat weeks. So they connected the whole thing into one workflow on a Chemspeed platform. What that looked like in practice: • Continuous flow synthesis of diglycolamides in a digitized reactor with online spectroscopic monitoring • Rxn Rover software with AI supervision, cutting process optimization steps and time by over 40% • 7 structurally diverse ligands made, with yields up to 90% • Scale-up to 19 grams of DMDODGA over 48 hours of continuous operation • High-throughput liquid-liquid extraction tested against 14 lanthanides on a robotic platform The point is not any single number. It is that synthesis and application testing finally sit in one closed loop instead of separate silos. That is what moves critical materials toward smart, autonomous manufacturing. Read the full article: https://lnkd.in/eYE5AMdf Where in your ligand workflow do the handoffs cost you the most time? See how we close the loop: https://www.chemspeed.com/ #LabAutomation #RareEarthElements #FlowChemistry #CriticalMaterials #DigitalLab

Self-driving labs are reshaping chemistry - combining AI, robotics, and automation. A recent C&EN article highlights this shift. The laboratories featured in the article leverage technologies from Bruker and Chemspeed, combining ChemSpeed automation with Fourier 80 benchtop NMR to enable integrated, real-time analytical feedback and support closed-loop experimentation workflows. Want to see where autonomous experimentation is heading next? The article explores some of the technologies and concepts driving this transformation. Read more: https://lnkd.in/eNAZ_7-B #SelfDrivingLabs #Automation #NMR

Five shows. Three continents. And that was only part of our first half of 2026. Here is what stuck with us. From Boston to Singapore. The same question came up everywhere: how do I get automation that fits my chemistry, not the other way around? That question shaped every conversation at the booth. • SLAS (Society for Laboratory Automation and Screening) in Boston: The life sciences crowd wanted reproducibility they could trust, especially for sample prep feeding NMR and LC/MS. • analytica in Munich: AI and digitalization were the headline, but the real pull was connecting "make" and "analyze" in one workflow. • American Coatings Show in Indianapolis: Formulators came looking for ways to run more experiments without adding headcount. • EuroLab Live (formerly Future Labs Live Basel) in Basel: The conversation moved from single instruments to fully connected, data-driven labs. • AI4X Conference in Singapore: The research community pushed furthest, toward self-driving labs where AI runs the experiment, not just the analysis. Different industries, different continents, same thread: nobody wants automation for its own sake. They want fewer manual steps, cleaner data, and time back for the science that matters. Curious what that looks like for your lab? See how we can help: https://www.chemspeed.com/ #LabAutomation #Digitalization #RnD #Chemspeed #FutureLabs

It's summer. The lab is weirdly quiet. Half the team is off somewhere with no email signal, and honestly, good for them. The rest of us are holding down the fort, drinking iced coffee that was hot coffee twenty minutes ago, and watching the automation do its thing without complaint. There's something nice about a slower season. Fewer meetings. More room to think. The kind of quiet where the actual interesting work tends to sneak up on you. Some of the best ideas show up when the building finally calms down. Whatever you're up to, enjoy it. ☀️ #Summer #LabLife #LabAutomation #Chemspeed

A self-driving lab that works like a chemical dimmer switch? 🎛️🔬 Prof. Milad Abolhasani and his fellow researchers have developed Flex-Cat, an autonomous, AI-driven chemistry lab that discovers catalysts capable of switching between different industrial products on demand. This breakthrough could completely accelerate manufacturing for pharmaceuticals, plastics, and fuels. 🔗 Read more about the study here: https://lnkd.in/eF8Fh-sp

Your robot is fast. The gap between experiments isn't. Automation moved the bottleneck. It didn't remove it. Robots pipette, dispense, process, and read faster than any technician. But someone still pulls the results, interprets them, and designs the next run. That part never got automated. It just lands on your scientists, every cycle, every day. We studied a screening team where the robot ran for 11 hours of a 40-hour week. The other 29 hours, it sat waiting on a human decision. And when we ask lab leaders how much senior scientist time goes to coordination, the median guess is 15 to 20%. When we actually measure it, it's 35 to 45%. ArkSuite's MCP server closes that gap by connecting your AI assistant directly to the lab environment: ✅ Real-time access to inventory, instrument status, and the job queue ✅ AI proposes the next experimental set based on prior runs ✅ ArkSuite validates hardware, safety, and parameters before anything runs ✅ Scientists review and approve. Nothing executes without a human ✅ Results feed back, and the loop tightens each round The AI handles the coordination. Your scientists keep the direction. This isn't rip-and-replace. It connects to the Chemspeed hardware and AI tools you already use. If your robot only works 11 hours out of 40, the problem was never the robot. How much of your team's week goes to coordination instead of discovery? Worth measuring before you assume. Learn more: https://lnkd.in/enVZSTCM #LabAutomation #AgenticAI #RnD #MCP #DigitalLab