Shehan Makani

Chemenova LLC: AI‑Enabled Sustainable Chemical Innovation Chemenova LLC (founded 2025, Newark NJ) is an AI-driven green chemistry startup focused on specialty chemicals and sustainable processes .   By combining machine‑learning formulation engines with custom manufacturing, Chemenova builds platforms that accelerate R&D and supply‑chain workflows for formulators and brands .   Its mission is to deliver “automated, sustainable chemical solutions” for SMEs in pharmaceuticals, personal care, agtech and advanced materials .   Uniquely, Chemenova bridges digital intelligence (AI tools) with real‑world production: for example its IntelliForm™ software can propose eco‑friendly blends and then send validated recipes to partnered pilot plants for scale‑up .   In short, Chemenova aims to let chemists “focus on innovation” while AI and data handle the heavy lifting of design, sourcing, and manufacturing . Industry Context: AI and Sustainability Driving 2...

  Technical Review: Green Solvents and Bio-Based Surfactants in 2025 — Chemistry, Applications & Emerging Standards The drive toward sustainability is prompting a major shift in solvent and surfactant chemistry. Green solvents are those with lower toxicity, high biodegradability, and minimal environmental impact. By contrast, many conventional solvents (e.g. benzene, carbon tetrachloride) are highly efficient but extremely hazardous (carcinogenic, ozone-depleting) . Modern green chemistry seeks alternatives: for example, water, ethanol, supercritical CO₂, and various bio-derived esters or glycol ethers are being adopted in applications from cleaning to pharmaceuticals. Likewise, bio-based surfactants (e.g. glycolipids, sugar esters) can replace petroleum-derived detergent molecules, offering similar performance with much faster biodegradation . Chemistry of Solvency and Biodegradation Solvency depends on molecular interactions: polar solvents dissolve ionic or p...

  Machine Learning for Formulation Science: A Technical Framework for Predictive, Data-Sparse Formulation Optimization Formulation science – the design of chemical mixtures like detergents, agrochemicals, or cosmetics – is traditionally empirical and labor-intensive. Recent advances in machine learning (ML) promise to accelerate formulation R&D by intelligently exploring vast composition spaces. This review outlines key ML frameworks suitable for formulation (especially when data are scarce), including Bayesian optimization, graph neural networks, and generative models, and discusses validation metrics relevant to formulation quality. Bayesian Optimization Strategies Bayesian optimization (BO) is widely adopted for optimization under scarce data . It treats the formulation-property relationship as a black box. A Gaussian process (GP) or similar surrogate models the objective(s) (e.g. stability, viscosity) as a function of ingredient ratios, along with an uncertai...

  A Critical Review of AI-Driven Chemical Sourcing Models for 2025–2030 The procurement of chemicals is becoming increasingly automated by artificial intelligence (AI) and data analytics. Modern sourcing platforms claim to use “AI, data science, and machine learning to automate supplier discovery, qualification, and engagement” . In practice, this means integrating procurement workflows – from searching for suppliers and parsing technical documents to managing compliance checks – into AI-driven pipelines. Automation tools now ingest catalogs, safety data sheets (SDS), and certificates of analysis (COA) to accelerate approval and ordering. Real-time market intelligence can even rank suppliers dynamically (e.g. “GenAI-enhanced supplier negotiations” featuring retrieval-augmented data) . Compared to legacy systems, AI can continuously update rankings and “flag suppliers before they impact the supply chain” via risk-scoring models . In the following, we critically exam...