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Blog by Pandora Industries
Market Trends & Emerging Insights
Explore emerging technologies, shifting consumer demands, sustainability movements and digital transformation in chemical manufacturing - so you can align your business strategy with tomorrow's opportunities rather than yesterday's benchmarks.


Battery Chemicals and New Energy Materials: A Sourcing and Manufacturing Guide for India's Supply Chain
Key Takeaways India imports effectively all of its lithium-ion cell and cathode active material (CAM) requirement today — estimated to exceed 400,000 tonnes/year of CAM by 2030 — overwhelmingly from China, Japan and South Korea, making battery chemicals one of the most import-dependent categories in the Indian new energy stack. India's flagship Advanced Chemistry Cell (ACC) PLI scheme, a Rs 18,100 crore programme targeting 50 GWh of domestic cell capacity, had commissioned on
7 days ago9 min read


Decarbonization and Carbon Capture in Chemicals
Key Takeaways Decarbonization in chemicals has moved from a sustainability slide to a line item: EU CBAM entered its definitive, certificate-purchasing phase on 1 January 2026, and India's Carbon Credit Trading Scheme (CCTS) already carries binding emission-intensity targets for petrochemicals and other energy-intensive sectors, with first compliance filings due 31 July 2026. Carbon capture is not one technology. Post-combustion, pre-combustion and oxy-fuel capture suit diffe
Sep 114 min read


Chemical Imports in 2026: How Geopolitics, Carbon Borders, and AI Are Reshaping Global Procurement
Key Takeaways Geopolitical chokepoints are redefining supply security: The February 2026 closure of the Strait of Hormuz exposed Asia's structural vulnerability—over 60% of its seaborne naphtha transits through this single route, forcing crackers offline and triggering force majeure declarations across Singapore, South Korea, and Indonesia. EU Carbon Border Adjustment Mechanism (CBAM) is now live: From January 1, 2026, chemical imports into the EU carry embedded carbon costs,
Jul 289 min read


AI in Chemical R&D and Materials Discovery
Key Takeaways AI models, particularly graph neural networks (GNNs) and generative approaches, predict material properties with >90% accuracy in targeted domains like thermoelectrics, slashing virtual screening time from weeks to hours. DeepMind’s GNoME project identified 380,000 new stable materials, demonstrating how large-scale ML on databases like Materials Project accelerates discovery by 10–100×. Autonomous laboratories combining AI planning with robotics achieve ~71% sy
Jun 305 min read
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