Why Reference Electrodes Matter in Biosensing

🔬 What’s R&D’s biggest headache? Scaling up from a 1L flask to a 10T plant. 🤯 Scale-Up is where lab theory often collides with industrial reality. 🚀 The Journey from Lab to Industry Moving from lab-scale to industrial-scale is a fascinating challenge and a critical turning point for any technology. It demands a rigorous methodology leaving little room for trial and error. The starting point must always be reproducibility. Without it, we’re scaling up variability, not the process. Once that’s secured, success depends on three core pillars: ⚙️ 1. Mastering Critical Variables Identifying and controlling scale-dependent variables is vital. At larger scales, temperature and concentration gradients impact heat and mass transfer. Recognising which variables change, and controlling them effectively, determines batch success. ⚖️ 2. Transfer Principles (Balances & Design) Material and energy balances must close at large volumes; this is non-negotiable. Proper equipment design and selection (reactors, separators) turn those balances into industrial reality. 📐 3. The Golden Rule: Dimensionless Numbers We don’t scale reactors—we scale physics. Using dimensionless numbers (Reynolds, Peclet, Damköhler, etc.) preserves dynamic similarity, ensuring predictable performance at scale. 🖥️📈 4. Leverage Process Modeling & Simulation Before scaling, use process models and simulation tools to predict behaviour, optimise conditions, and identify potential bottlenecks. Simulation reduces risk, saves resources, and bridges the gap between lab and plant. 💬 Question for you: In your experience, what’s the hardest challenge in Scale-Up—mass transfer, reaction kinetics, or equipment validation? 👇 #ScaleUp #ChemicalEngineering #ProcessEngineering #R&D #IndustrialInnovation #TechnologyTransfer #PilotPlant #ScaleUpEngineering #PilotPlantDesign #ProcessDevelopment #IndustrialChemistry

This second section on polymer characterization covers advanced analytical techniques. These tools are essential for studying material failure, stability, surface interactions, and composition at the micro and nano scales.  These specialized methods provide the critical data needed to solve material challenges and guide polymer research. #PolymerScience #MaterialsScience #MaterialCharacterization #PolymerEngineering

📢 Revolutionizing Manufacturing: Boost Efficiency with Tech! 🌟 Process intensification enhances manufacturing efficiency. It focuses on producing more with fewer resources. 🔍 Courtney Hazelton-Harrington highlights advanced analytical technologies' role. High-throughput analytical methods, like Raman spectroscopy, improve biomanufacturing processes. 📈 Real-time data from Process Analytical Technology PAT enhances control and speeds up production. Both upstream and downstream operations benefit greatly from these advancements. #BioprocessUpdates #ProcessIntensification #AnalyticalTechnologies #InnovationInManufacturing #HighThroughputAnalysis #RamanSpectroscopy #Biomanufacturing #ProcessAnalyticalTechnology #RealTimeData #ManufacturingEfficiency #AdvancedAnalytics #ProductionOptimization #SustainableManufacturing #TechInIndustry #DataDrivenDecisions #EfficiencyBoost ▷ Read the full article here: 📎 https://lnkd.in/d9S4zeaW

💡 𝗛𝗼𝘄 𝗰𝗮𝗻 𝘁𝗼𝗱𝗮𝘆’𝘀 𝗹𝗮𝗯𝘀 𝗸𝗲𝗲𝗽 𝘂𝗽 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗴𝗿𝗼𝘄𝗶𝗻𝗴 𝗱𝗲𝗺𝗮𝗻𝗱 𝗳𝗼𝗿 𝗳𝗮𝘀𝘁𝗲𝗿, 𝗺𝗼𝗿𝗲 𝗮𝗰𝗰𝘂𝗿𝗮𝘁𝗲 𝗺𝗮𝘁𝗲𝗿𝗶𝗮𝗹 𝗰𝗵𝗮𝗿𝗮𝗰𝘁𝗲𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 — 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗮𝗱𝗱𝗶𝗻𝗴 𝗰𝗼𝘀𝘁 𝗼𝗿 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆? The answer: Thermo Scientific Phenom ParticleX – an automated desktop solution that combines SEM (scanning electron microscopy) and EDX (energy-dispersive X-ray spectroscopy) in one compact, high-throughput platform. ⚡ 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: - Automated particle detection, imaging, chemical analysis & classification - Reduced turnaround times and operator workload - Same-day results & multi-sample workflows - Minimal training required 𝗧𝗮𝗶𝗹𝗼𝗿𝗲𝗱 𝗣𝗮𝗿𝘁𝗶𝗰𝗹𝗲𝗫 𝗰𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗿𝗲 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 𝗳𝗼𝗿 𝗸𝗲𝘆 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗲𝘀: - Technical cleanliness - Battery material analysis - Steel & metallurgy - Environmental particles (e.g., asbestos) - Forensic applications (e.g., gunshot residues) With automation at its core, Phenom ParticleX helps labs unlock advanced SEM/EDX capabilities exactly where they’re needed — without the barriers of traditional large-scale lab systems. 🔗 Learn more here: English article: https://lnkd.in/eQYjQ7rW German article: https://lnkd.in/e5M5Agmj #SchaeferScientific #ThermoScientific #ParticleX #Microscopy #SEM #EDX

Why the accuracy and precision distinction matters: Reliability: Both accuracy and precision are vital for reliable results in science and other fields.  Identifying Errors: Accuracy helps identify systematic errors (consistent deviations), while precision helps to understand random errors (unpredictable variations).  Calibration: Achieving accuracy often requires calibrating instruments against known standards to minimize systematic errors. 

🧪🌿 CarboVolt PEC-5™ | From hypothesis to data: how we prove a photoelectrochemical CO₂ platform No IP “spoilers.” No hype. Just scientific method applied to a PEC system that captures atmospheric CO₂ and converts energy. What we’re demonstrating (scientific scope): • Selective uptake of CO₂ from ambient air (ppm levels). • Stable photoelectrochemical conversion under simulated sunlight and controlled illumination. • Closed mass/energy balance (inputs = outputs + losses). • Operational safety and reproducibility across replicates. How we prove it (5-block didactic workflow): 1. Optics & electronics → UV–Vis/Tauc (absorption), photoluminescence (recombination), J–V under AM1.5G & controlled light; faradaic efficiency metrics. 2. Structure & surface → XRD (phase/crystallinity), XPS (surface composition/chemistry), microscopy (morphology/defects). 3. Applied electrochemistry → EIS (R_ct, diffusion), chronoamperometry/chronopotentiometry (stability), onset/overpotential in standardized electrolytes. 4. Gas analysis → GC for H₂/CO/CO₂/O₂ with traceable standards; flow/pressure metering with T/relative humidity corrections. 5. Balances & controls → blanks (no light), dark tests, reference materials, independent replicates; uncertainty budgets & confidence intervals. Good practices we enforce: • Design of Experiments (DoE) to isolate critical variables. • Traceability (parameter logs, sample versioning, calibrated instruments). • Reproducibility (≥3 replicates) and durability in continuous operation. • External audit of key datasets before any public release. • Safety: gas monitoring, seal integrity, formal risk analysis. What we do NOT disclose (to protect the project): • Formulations, synthetic routes, ratios/dopants, layer stack, fine setpoints, flow/illumination sizing. • Performance numbers not yet independently audited. Next milestones (disclosable): • Methods report focused on metrics & uncertainties. • Technical note with audited results vs. references. • Independent validation program under NDA. If you work in metrology validation, applied electrochemistry, or instrumental analysis and want to collaborate under confidentiality, let’s talk. 🤝 #science #R&D #photoelectrochemistry #CO2capture #hydrogen #electrochemistry #metrology #gaschromatography #XRD #XPS #UVVis #PL #EIS #AM15G #engineering #materials #netzero #climatetech #deeptech #innovation #safety #reproducibility #laboratory #NDA

I am pleased to share that my latest paper has been published in Computers & Chemical Engineering (Elsevier). 📖 Title: Causal discovery in industrial systems via physics-guided variational attention and probabilistic interventions. In this work, with the much-appreciated collaboration of Alireza Memarian, PhD, E.I.T and supervision of Dr. Biao Huang, we introduce a physics-guided variational attention framework for uncovering true causal relationships in complex industrial processes. The proposed method integrates log-normal variational attention, physics-informed priors derived from operator knowledge, and probabilistic interventions with uncertainty quantification to enhance fault detection and diagnosis under Industry 4.0 conditions. The article is available here: 🔗 https://lnkd.in/gz7-ka7u I look forward to connecting with others interested in causal discovery, process control, and machine learning applications in industrial systems. #CausalDiscovery #ProcessControl #FaultDiagnosis #Industry40 #MachineLearning #ChemicalEngineering #Elsevier #ResearchPublication

Excited to see this great work led by Mohammad Hossein Modirrousta now published in Computers & Chemical Engineering! It was a pleasure to collaborate on this research, where we developed a physics-guided variational attention framework to uncover causal relationships in complex industrial systems. By combining physics-informed priors, probabilistic reasoning, and machine learning, our approach enhances fault detection and diagnosis under Industry 4.0 conditions. You can read the full paper here: https://lnkd.in/gz7-ka7u If you’re interested in causal discovery, process control, or machine learning applications in industrial systems, feel free to reach out to Mohammad Hossein Modirrousta or me. We’d love to connect and discuss ideas!

Process innovation in perovskite solar-cell manufacturing demands precise ambient control. Film-formation steps such as solvent evaporation, nucleation, and crystal growth are exquisitely sensitive to humidity, solvent partial pressure, and air temperature. These ambient factors rarely vary in isolation, yet most studies vary them one at a time, ranking importance independently. In our latest pre-print https://lnkd.in/eFVrkJYA , we apply "shapiq" to capture multi-variate interactions among these variables. We observe a non-linear effect of absolute humidity and solvent partial pressure during synthesis, a finding also observed by synchrotron-based in-situ GIWAXS. This highlights the importance of "fingerprinting" the combined environmental sensitivities of a given perovskite recipe, to better define ambient tolerances during manufacturing scale-up. In support of open science, we provide information about sensors for ambient control, raw data on sensor output and current-voltage measurements. We’ve translated this sensor technology to our industrial partners. The raw data have already served as a nucleation point for one future study. Code available on GitHub. #openscience It’s my personal hope that data-science tools can continue demystifying discrepancies between lab-scale experiments and industrial-scale processing, toward more uniform, repeatable, and stable perovskite modules. This 2-year campaign wouldn’t have been possible without the ADDEPT community, including Tianran Liu, Nicky Evans, Kangyu Ji, Ronaldo Lee, Aaron Zhu, Vinn Nguyen, Jim Serdy, Elizabeth Wall, Yongli Lu, Florian Formica, Moungi Bawendi, Quinn Burlingame, Lynn Loo, and Vladimir Bulovic.

Tonio challenged me to illustrate nonlinear interactions between input variables in the data analysis. Traditional Shapley feature analysis cannot capture these interactions, so I employed the latest SHAP-IQ method and developed visualization techniques to interpret our experimental results (and to create the main text figures).