Synthetic Chemistry Innovations

Plants have been making fuel from sunlight for 500 million years. China just figured out how to copy them. A team at the Chinese Academy of Sciences built a system that takes CO₂ and water, hits it with sunlight, and produces the building blocks of synthetic gasoline. No oil wells. No drilling. No fossil carbon. Think about that. The secret was a "charge reservoir" — a material made from tungsten trioxide and tiny amounts of silver that traps solar energy like a battery and releases it precisely when needed. Previous systems failed because electrical charges disappeared instantly. This one stores them. The result: carbon monoxide — the industrial starting point for synthetic gasoline and jet fuel — at roughly 100 times the efficiency of previous catalysts. Water is the only ingredient consumed. Zero sacrificial chemicals. What stopped me: It works with existing engines. Existing pipelines. Existing infrastructure. No reinvention needed. The Multiplication Effect: 1 system proving the concept = validation that photosynthesis can be copied 10 systems producing fuel = regional energy without drilling 100 systems deployed = countries producing synthetic fuel from sunlight At scale = energy independence from fossil carbon For a century, we've drilled into the Earth for energy. This system pulls it directly from the sky. We've spent decades asking how to extract more efficiently. Maybe the better question was always: how do we copy what already works? ♻️ Follow me, Dr. Martha Boeckenfeld for innovations that reshape how we power the future. Share if you believe the next energy revolution will come from biology, not geology. 📚 Source: Chinese Academy of Sciences | Yu Huang et al. | Nature Communications, March 2026 | ECOticias — Adrian Villellas

Nature Chemical Biology paper (1 Jun 26) describes the first fully de novo discovery pipeline for membrane-permeable macrocyclic peptides targeting an intracellular protein-protein interaction (PPI), starting with zero prior structural knowledge. 🔅 The team chose Keap1-Nrf2 deliberately. Keap1 tags the transcription factor Nrf2 for proteasomal degradation; disrupting this interaction activates cytoprotective antioxidant gene expression, making it a target for oxidative stress, neurodegeneration, inflammation, and cancer. The interface spans ~650 Ų and relies on electrostatic contacts. Every known high-affinity inhibitor carries at least one negative charge, crippling membrane permeability and creating metabolic liabilities. A biologically validated, clinically relevant, and specifically resistant benchmark. 🔅 The team screened 15,360 fully random, uncharged cyclic peptides (600-800 Da) against the Keap1 Kelch domain by TR-FRET in 384-well plates, then ran three iterative design-build-test cycles to optimize both binding and permeability in parallel. The final lead, compound 30, achieved a Ki of 53 nM with only 2 hydrogen bond donors and a TPSA of 203 Ų, and showed dose-dependent activity in a Nrf2/ARE luciferase reporter assay. Crystal structure (PDB: 9QDU, 2.35 Å) confirmed compound 30 occupies the Nrf2-binding site on Keap1 but engages entirely through H-bond acceptors and hydrophobic contacts, with a backbone trajectory that has no spatial overlap with native Nrf2, confirming a truly de novo binding mode rather than a peptide mimic. The authors note that libraries of around 100K compounds will likely be needed to tackle more challenging targets with shallower or less well-defined binding pockets. 🔅 The paper also describes a covalent inhibitor branch. Exploiting Cys434 at approximately 8.8 Å from the peptide terminus, chloroacetamide compound 20-Cl achieved a kinact/Ki of 3,200 M-1 min-1, with intact MS confirming site-selective C434 modification on wild-type Keap1 but not the C434A mutant control. 🔅 The central remaining liability is active efflux. Compound 30 shows a Caco-2 apical-to-basal Papp below detection limit (less than 0.07 x 10-6 cm s-1), producing an approximately 1,000-fold in vitro-to-cell potency gap (53 nM Ki vs ~50 µM cellular IC50). Efflux pump susceptibility, not passive permeability, is the bottleneck for advancing this scaffold toward oral bioavailability. 🔅 Patent US20240279845A1 covers the core SPPS disulfide-cyclization and combinatorial diversification method used in this study. A patent directly tied to this paper has not yet surfaced. The group founded spin-off Orbis Medicines, which raised more than €90 million in Series A funding to advance oral macrocycle drugs (nCycles) against validated biologic pathways. 📑 Not Open Access: https://lnkd.in/g53Ufy5Y #MacrocyclicPeptides #DrugDiscovery

🔬NMR Spectroscopy: A Powerful Tool for Molecular Characterization 🔬 Nuclear Magnetic Resonance (NMR) spectroscopy is more than just a technique—it's a cornerstone of modern chemistry, playing a crucial role in unraveling the mysteries of molecular structures. But what exactly is NMR, and how does it help in characterizing compounds? At its core, NMR spectroscopy exploits the magnetic properties of atomic nuclei. When placed in a strong magnetic field, certain nuclei absorb and re-emit electromagnetic radiation. By analyzing these signals, we can gain profound insights into a molecule's structure and dynamics. Here’s why NMR is indispensable in characterizing compounds: Molecular Structure Determination: NMR provides detailed information about the electronic environment of atoms within a molecule, allowing us to identify functional groups and understand how atoms are connected. Spin-spin coupling and multiplet analysis are key in piecing together the molecular puzzle. Functional Group Identification: Different functional groups have characteristic chemical shifts, helping quickly identify the components of a molecule. Quantitative Analysis: The area under NMR peaks corresponds to the number of atoms in different environments, making it possible to confirm the molecular formula and purity of the compound. Stereochemistry and Dynamics: NMR helps investigate the spatial arrangement of atoms and study how molecular conformations change with temperature or during reactions. This insight is invaluable for understanding complex molecular behavior. Impurity Detection and Reaction Monitoring: NMR's sensitivity allows for the detection of impurities and the real-time monitoring of chemical reactions, providing insights into reaction mechanisms and the formation of intermediates. From identifying functional groups to confirming molecular structures and monitoring chemical reactions, NMR spectroscopy is a versatile tool that bridges the gap between molecular theory and real-world applications. As a researcher, harnessing the full potential of NMR can lead to breakthroughs in fields ranging from organic chemistry to biochemistry and materials science. Whether you're elucidating complex natural products or developing new materials, NMR is an essential part of the chemist's toolkit. #NMR #Spectroscopy #MolecularCharacterization #Chemistry #ResearchTools #ScientificDiscovery Image description: H-NMR and 13C-NMR spectra of the Green Surfactant synthesized from Cashew Nut Shell Liquid (CNSL).

A labour of love 🧡 (and many hours in the labs, and many more writing up these results 😂), Marko Ivancevic’s first of a series of papers on the photophysics of organic molecules is out!👏🏻 https://lnkd.in/gMZQaRrx This paper details the discovery of a simple process to induce ultra-long (many seconds) phosphorescence 🌈 in a wide library of organic compounds at room temperature. Colloquially termed “afterglow”🌟 because the compounds continue to glow when the excitation light source🔦 is removed, this phenomenon – especially at room temperature – was thought to be exceedingly rare.😅 A handful of compounds were reported to exhibit afterglow when they are organized, and neatly and tightly packed in crystals.💎 Yet another handful were only reported to do so when they are isolated in an inactive, plastic matrix. How do we reconcile these observations?🤔 Is there a unifying framework that can help us understand and contextualise these observations?🤨 Enter Marko’s experiments.🧩 With more than 20 compounds, he showed that he can get them to exhibit afterglow when he disperses them in a non-interactive polymer matrix, and then heating them up to soften the polymer matrix.🌡️ This softening allows the compounds to diffuse and form small aggregates that activate afterglow. Why is this so cool?😎 Scientifically, quantifying this phenomenon has allowed Marko to develop a framework that bridges past observations with his findings.🌉 Turns out it’s all about creating structures that limit intramolecular motion and intermolecular exciton diffusion to minimise non-radiative recombination that give rise to afterglow.🪱 Technologically, this phenomenon can be leveraged for bioimaging.🩻 When phosphorescence extends beyond the initial excitation, it increases signal-to-noise, making detection much easier for physicians.🧑🏻⚕️ It can also be used for authentication and anticounterfeiting purposes as it adds a more sophisticated fingerprint.🐾 So stay tuned for the next few papers where Marko demonstrates he can 3D-print🖨️ structures in almost any form factor and create stable ink formations✒️ that glow for extended periods of time! Jesse Wisch, Quinn Burlingame, Barry Rand Princeton CBE, Princeton Engineering, Andlinger Center for Energy and the Environment, Wiley

Which Detector Suits Your Analyte? In HPLC (High-Performance Liquid Chromatography), the detector you choose can make or break your analysis. With a wide range of detectors available, each type is tailored to different compound properties — making the selection a crucial step in method development. Here’s a breakdown of the main HPLC detectors and their optimal use cases: 📘 UV/Vis Detector ▪ Ideal for: Majority of organic analytes ▪ Principle: Detects based on UV absorbance 📘 PDA (Photodiode Array) ▪ Ideal for: Organic analytes with overlapping peaks ▪ Advantage: Scans multiple wavelengths simultaneously, enabling spectral purity analysis 📘 Fluorescence Detector ▪ Ideal for: Aromatic and highly conjugated compounds ▪ Notable for: High sensitivity for π→π* transitions and alicyclic carbonyl structures 📘 RID (Refractive Index Detector) ▪ Ideal for: Sugars and compounds with no UV absorbance ▪ Advantage: Universal detection (non-selective) 📘 ECD (Electrochemical Detector) ▪ Ideal for: Redox-active analytes like multivitamins ▪ Advantage: Very high sensitivity to electroactive compounds 📘 ELSD (Evaporative Light Scattering Detector) ▪ Ideal for: Non-volatile compounds or those with poor UV response ▪ Common in: Lipid, sugar, and polymer analysis 📎 Choosing the correct detector depends on your analyte’s characteristics — absorption, volatility, and chemical structure. 📊 Are you working with UV-active molecules, sugars, or redox-sensitive compounds? The right detection strategy makes all the difference in accuracy and sensitivity. 💬 What’s your go-to HPLC detector — and in what application? #HPLC #Chromatography #AnalyticalChemistry #PharmaceuticalScience #QualityControl #ResearchAndDevelopment #PDA #UVVis #Fluorescence #RID #ELSD #ECD #LabLife #ScienceCommunication

𝗔𝗔𝗦 𝘃𝘀 𝗜𝗖𝗣-𝗢𝗘𝗦 𝐄𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐥 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐢𝐧 𝐌𝐨𝐝𝐞𝐫𝐧 𝐋𝐚𝐛𝐨𝐫𝐚𝐭𝐨𝐫𝐢𝐞𝐬 Elemental analysis is a critical component of quality control, environmental monitoring, food safety, metallurgy, and pharmaceutical testing. Two widely used techniques are Atomic Absorption Spectroscopy (AAS) and ICP-OES. 🔹 𝐀𝐀𝐒 (𝐀𝐭𝐨𝐦𝐢𝐜 𝐀𝐛𝐬𝐨𝐫𝐩𝐭𝐢𝐨𝐧 𝐒𝐩𝐞𝐜𝐭𝐫𝐨𝐬𝐜𝐨𝐩𝐲) Principle: Free ground-state atoms absorb light at element-specific wavelengths. Measurement: Decrease in light intensity → Concentration Key Characteristics: Single-element analysis (one element at a time) Flame or Graphite Furnace modes Good sensitivity (ppm to low ppb with GF-AAS) Lower capital investment Best suited for: ✔ Routine testing of specific metals ✔ Small to mid-size laboratories ✔ Cost-controlled environments --- 🔹 𝐈𝐂𝐏-𝐎𝐄𝐒 (𝐈𝐧𝐝𝐮𝐜𝐭𝐢𝐯𝐞𝐥𝐲 𝐂𝐨𝐮𝐩𝐥𝐞𝐝 𝐏𝐥𝐚𝐬𝐦𝐚 – 𝐎𝐩𝐭𝐢𝐜𝐚𝐥 𝐄𝐦𝐢𝐬𝐬𝐢𝐨𝐧 𝐒𝐩𝐞𝐜𝐭𝐫𝐨𝐬𝐜𝐨𝐩𝐲) Principle: Excited atoms in plasma emit light at characteristic wavelengths. Measurement: Emission intensity → Concentration Key Characteristics: Simultaneous multi-element analysis Wide linear dynamic range Faster throughput Higher operational cost Best suited for: ✔ Multi-element screening ✔ Environmental & industrial labs ✔ High sample throughput QC labs. 🧪 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐏𝐞𝐫𝐬𝐩𝐞𝐜𝐭𝐢𝐯𝐞 𝐢𝐧 𝐐𝐂 If you need targeted metal estimation with budget control → AAS is sufficient. If you require multi-element, faster, high-volume analysis → ICP-OES is more efficient. 📌 𝐅𝐢𝐧𝐚𝐥 𝐓𝐡𝐨𝐮𝐠𝐡𝐭 • AAS is precise and economical for specific elements. • ICP-OES delivers speed and multi-element power for modern labs. #AnalyticalChemistry #QualityControl #AAS #ICPOES #ElementalAnalysis #Laboratory #QC #ISO

I'm very pleased to report two new and very different approaches to aza-S(VI) derivatives, both separately published in ChemistryEurope journal. With Dominique Deans and collaborators we report the catalytic generation of sulfonimidamides by SuFEx reaction of sulfonimidoyl fluorides with diverse functionalised amine derivatives. The reaction performed in DMSO was suitable for automation, with reaction set-up using a liquid handling robot (Opentrons Labworks Inc.). In addition we report switchable and entirely chemoselective SuFEx vs SNAr processes with 4-fluorophenylsulfonimidoyl fluorides, which enabled the generation of further complex derivatives. https://lnkd.in/ey4fsjZ8 With Tsz-Kan Ma, Peerawat Saejong, King Long Or, Callum Begg we report a copper mediated transient C-H functionalisation reaction to form sulfides which could be converted to cyclic sulfilimines and sulfoximines. Using a catalytic transient directing group strategy with benzylamine substrates gave ortho-sulfanylation, and so directly set up amine sulfides for oxidative cyclisation to form S=N bonds. Unusually sulfenamides were used as the source of sulfanyl radicals. https://lnkd.in/ea3A8__d

🔧⚗️ Understanding Key Unit Operations in Chemical Engineering — A Complete Visual Breakdown Chemical Engineering stands on four powerful pillars of Unit Operations. This visual beautifully illustrates how industries transform raw materials into valuable products through Mass Transfer, Heat Transfer, Fluid Flow, and Mechanical Operations. Let’s break it down 👇 ⚗️🌫️ 1️⃣ Mass Transfer Operations Processes where components move between phases due to concentration differences. 🔹 🏭 Distillation – Separation based on boiling points 🔹 🌬️ Absorption – Gas absorbed into a liquid phase 🔹 💨 Drying – Moisture removal from solids 🔹 🧲 Adsorption – Impurities trapped on solid surfaces 🔹 ❄️ Crystallization – Formation of pure solid crystals 🔹 💧 Membrane Separation – RO, UF, NF, energy-efficient separations 🔥🌡️ 2️⃣ Heat Transfer Operations Energy movement for heating, cooling, and phase change processes. 🔥 Heat Exchanger – Transfers heat between fluids ♨️ Evaporation – Concentrates liquids by removing solvent 💧➡️🌫️ Condensation – Vapor converted back to liquid 💨🔄 3️⃣ Fluid Flow / Momentum Transfer Operations Ensuring smooth, controlled movement of fluids in industrial pipelines. 🔸 🚰 Pumping – Moves liquids at controlled pressure 🔸 ⚙️ Compressors – Increase gas pressure 🔸 🔄 Mixing – Ensures uniform composition 🔸 🌪️ Fluidization – Solids behave like fluids (key in reactors & dryers) ⚙️📦 4️⃣ Mechanical Operations Handling and processing of solid materials. 🔹 🔍 Screening – Particle size separation 🔹 📥 Starato Feeding – Controlled solid feeding 🔹 🪨 Crushing (Size Reduction) – Breaking solids into smaller pieces 🔹 🏗️ Agglomeration (Size Enlargement) – Forming larger uniform granules 🔹 🏜️ Thickening – Increasing solids concentration in slurries 🏭💡 Why These Unit Operations Matter ✔️ Foundation of every industrial plant ✔️ Optimize efficiency & reduce cost ✔️ Improve product quality ✔️ Essential for scale-up, sustainability & innovation 🔍 Final Insight Mastering these unit operations empowers engineers to design smarter processes, optimize plant performance, and drive industrial transformation. 🔖 #ChemicalEngineering #UnitOperations #ProcessEngineering #MassTransfer #HeatTransfer #MechanicalOperations #IndustrialEngineering #EngineeringCommunity #LinkedInLearning

“The Logic of Chemical Optimization”   In the course of drug development, compounds undergo multiparameter optimization, a process by which a hit compound evolves into a lead, which, in turn, evolves into a candidate. A hit may have weak affinity for a target, while a lead likely has an increased affinity and may add additional physiochemical attributes; candidates instead add metabolic stability, efficacy, tolerability, safety.   Scientists at Sanofi looked at one of their past drug campaigns totaling 1681 molecules to assess whether one could develop an optimization scheme by working backward from a reported candidate to leads to hits, as it is commonly done in retrosynthesis. Proceeding backward identifies alternative leads and compares them with the actual lead, gauging the efficiency of various optimization paths. In the image below, it is reported the optimization path taken using minimal function group changes from the hit (1) to the actual lead (3) to the candidate (4). The actual optimization path required four matched pairs from hit to lead, and six more from lead to candidate. A search of alternative optimization paths reveals path A (shorter than the actual route taken) and path B (longer, 11 steps).   It is well-reported that lipophilic ligand efficiency correlates with clinical success and that defining the minimum pharmacophore and property-based optimization are best practices in the field of drug discovery. Still, the authors have found that three parameters taken from network analysis field, and not yet used in medicinal chemistry, could help identify the theoretical lead (2): network connectivity (how well various parts of the network connect to one another), betweenness centrality (identifies nodes that serve as bridges between other nodes in the network), edge count (number of connections) and average shortest path length (a measure of the efficiency of information on a network).   Finally, scientists sought to determine the location of functional group changes on the evolving scaffold (structural perturbation) while holding the rest of the structure constant. The structure of the hit is divided into three regions and the percentage of compounds in which a designated region has been perturbed is tracked over time. Initially, it turns out that the optimization campaign focuses on peripheral functional group modification, while later it also involves the core of the structure.   Network analysis run on a total of four projects at Sanofi indicates that while one of such projects obtained the candidate molecule after testing only 10% of the total compounds; instead, it took the other three projects testing 50-79% of the molecules before reaching the candidate.   Post n. 128. Original publication: https://lnkd.in/d3526uyj #chemistry #medicinalchemistry #drugdiscovery #drugdevelopment #pharma #science #research #synthesis

🔍 Revolutionising Petrochemical Synthesis with 𝗔𝗜 🔍 In the world of petrochemical synthesis, designing reaction pathways has always been a delicate balance—juggling conversion, selectivity, and energy efficiency. However, this balance is evolving. 𝗔𝗜 is paving the way for groundbreaking advancements in how we model, predict, and optimise complex kinetic networks, particularly in high-impact processes like alkylation, steam cracking, and aromatics production. 𝗪𝗵𝗮𝘁’𝘀 𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴? We’re moving beyond traditional first-principles and empirical models to embrace innovative approaches: 🔹𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: This enables us to iteratively discover optimal reactor trajectories under dynamic constraints, enhancing our ability to adapt. 🔹𝗕𝗮𝘆𝗲𝘀𝗶𝗮𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘀𝗮𝘁𝗶𝗼𝗻: This technique allows for fine-tuning variables, such as reactor temperature, residence time, and catalyst activity, with minimal experimentation, driving efficiency. 🔹𝗦𝘂𝗿𝗿𝗼𝗴𝗮𝘁𝗲 𝗠𝗼𝗱𝗲𝗹𝘀: These models significantly accelerate reaction simulations by approximating full kinetic schemes in real-time, making our processes faster and more responsive. At #INGENERO, our mission is not just to develop AI; it’s to deploy it meaningfully—enhancing reaction yields, reducing byproducts, and improving operability. - In alkylation, we achieve smarter control of the acid-to-hydrocarbon ratio. - In ethylene production, we implement adaptive tuning of the steam-to-hydrocarbon ratio. - In aromatics, we enable dynamic switching between feedstock cuts based on predicted yields. These tools do not replace the fundamentals of chemical engineering; they amplify them. They empower us to operationalise decades of reactor knowledge into adaptive, scalable, and intelligent decisions. The next leap in petrochemical innovation won’t solely stem from new reactor designs. Instead, it will arise from making our existing reactors far more intelligent. 💬 Engage with Us: How are you leveraging AI to optimise your reaction pathways? Let’s share insights and drive the conversation forward! #Petrochemicals #AIinEngineering #ReactionOptimization