Trends that are Catalyzing Chemical Innovations

A look at the evolving landscape shaping the chemicals industry

The Coronavirus outbreak is catalyzing innovations in the global chemicals industry. Customers are witnessing the influx of antiviral and antimicrobial materials, machine learning for better testing of chemicals and 3D printing amongst other trends. Manufacturers are increasing efforts to strike the right balance between innovation and the future for a circular economy to boost their credibility credentials.

Circular economy goals

Chemical companies are taking cues from the European Green Deal of 2019 to tackle climate change and environmental degradation. Growing e-waste from smartphones is suggested to be recycled. Since recycling also contributes for a large carbon footprint during production process, smartphone companies are bullish on increasing the lifespan of mobile devices. Repairable and upgradeable smartphones are becoming the new rage in forming a circular economy.

Deep Dive in Chemicals for Oil & Gas Industry

Production chemists are gaining cognizance about key aspects like improved oil recovery and enhanced oil recovery. However, the issue of greenhouse gas emissions is stifling market growth. Since increasing number of chemical companies are keen on adding value to their business operations, they are joining forces with researchers and technicians to adopt a proactive approach at knowing emission inventories, the emission sources and the parameters required to control individual emissions.

Hydrogen economy and the rising customer expectations

Hydrogen technology is gaining prominence in automobiles and the overall mobility sector. Proton exchange membrane cells are paving the way for emission-free vehicles with a long range and the advantage of having a quick refueling. But high implemention cost in the current early stages of fuel cell technology is inhibiting market growth. Thus, companies should maximize production of hydrogen using high temperature solid oxide electrolysis (SOEC), proton exchange membrane electrolysis (PEM) and alkaline electrolysis (AWE) to lower its cost during the coming years.

Increasing number of customers are willing to associate with environmental-conscious companies. Chemical companies are using strategies like improved waste recovery, water management and power to X technologies, where the latter is a method that uses CO2 to convert it into useful chemicals via biomass combustion & waste combustion techniques. Biotechnology holds potentials to produce useful industrial enzymes and remediate environmental pollution.

In all aspects, investment in R&D by partnering with think-tanks and technicians will help chemical companies to build a sustainable future for customers in the value chain.

AI to find use to Improve Performance of Fuel Cells

In a new development, AI may find use to explore possible designs for the microstructure of lithium-ion batteries and fuel cells. This may help prevent researchers run 3-D simulations to make changes for improved performance.

The improvements in fuel cells and lithium-ion batteries using AI could help smartphones charge faster, increase the power of hydrogen fuel cells that run data centers, and increase the time interval between charging electric vehicles.

The findings of the study is published in npj Computational Materials.

AI to help Examine Microstructure of Fuel Cells

Meanwhile, fuel cells use renewable hydrogen fuel, which is generated using solar and wind energy, to generate heat and electricity. And, lithium-ion batteries used in laptops, smartphones, and electric cars are a popular form of energy storage. The performance of fuel cells and lithium-ion batteries is significantly related to their microstructure. This is closely related to how pores inside the electrodes are shaped and the arrangement can affect power that fuel cells generate, and how quickly charging and discharging of battery happens.

Nonetheless, due to extremely small size of micrometer-scale pores, their specific sizes and shapes can be difficult to examine at resolution high enough to relate them to overall cell performance.

To present a solution to this, researchers at Imperial have machine learning techniques. This helps them to explore the pores virtually and perform 3-D simulations to foretell cell performance based on their microstructure.

For this, the researchers employed a novel machine learning technique “deep convolutional generative adversarial networks. The algorithms of this novel machine learning technique gains insights to generate 3-D image data of the microstructure. And, the image generated using algorithms of novel machine learning technique are based on training data of synchrotrons obtained from nano-scale imaging.