Which technologies will propel industry forward and give companies that competitive advantage? According to digital consultancy McKinsey Digital’s Tech Trends Index, several technologies will have a profound and disruptive impact on industries including the chemical sector. So, which ones will have the biggest effect on the way you work in the coming decade?

1: Automation

By 2025, more than 50 billion devices around the world will be connected to the Industrial Internet of Things (IIOT) and about 600,000 industrial robots a year will be in place from 2022. The combination of these, along with industrial processes such as 3D and 4D printing, will speed up processing and improve operational efficiency.

According to McKinsey, 50% of today’s work practices could be automated by 2022 as ever more intelligent robots (in physical and software form) increase production and reduce lead times. So, how does this change look in the real world?

According to the McKinsey Tech Trends Index, 10% of today’s manufacturing processes will be replaced by additive manufacturing by 2030.

According to the Tech Trends Index, one large manufacturer has used collaborative robots mounted on automatic guided vehicles to load pallets without human involvement, while an automotive manufacturer has used IIOT to connect 122 factories and 500 warehouses around the world to optimise manufacturing and logistics, consolidate real-time data, and boost machine learning throughput.

2: Next generation computing

An almost incredible 368,000 patents were granted in next generation computing in 2020. Advanced computing will speed up the processing of reams of data to optimise research and cut development times for those in the chemicals and pharmaceuticals industries, accelerate the use of autonomous vehicles, and reduce the barriers to industry for many eager entrants.

‘Next-generation computing enables further democratisation of AI-driven services, radically fast development cycles, and lower barriers of entry across industries,’ the index notes. ‘It promises to disrupt parts of the value chain and reshape the skills needed (such as automated trading replacing traders and chemical simulations, reducing the need for experiments).’

According to McKinsey, AI will also be applied to molecule-level simulation to reduce the empirical expertise and testing needed. This could disrupt the materials, chemicals, and pharmaceuticals industries and lead to highly personalised products, especially in medicine.

3: The Bio-revolution

It doesn’t take much investigation before you realise that the bio-revolution has already begun. Targeted drug delivery and smart watches that analyse your sweat are just two ways we’re seeing significant change.

The Tech Trends Index claims the confluence of biological science and the rapid development of AI and automation are giving rise to a revolution that will lead to significant change in agriculture, health, energy and other industries.

In the health industry, it seems we are entering the age of hyper-personalisation. The Index notes that: ‘New markets may emerge, such as genetics-based recommendations for nutrition, even as rapid innovation in DNA sequencing leads ever further into hyper personalised medicine.’ One example of this at work in the agri-food industry is Trace Genomics’ profiling of soil microbiomes to interpret health and disease-risk indicators in farming.

4: Advanced materials

It’s no secret that we will need to develop lighter materials for transport, and others that have a lighter footprint on our planet. According to McKinsey, next generation materials will enhance the performance of products in pharma, energy, transportation, health, and manufacturing.

For example, molybdenum disulfide nanoparticles are being used in flexible electronics, and graphene is driving the development of 2D semiconductors. Computational materials science is another area of extraordinary potential. McKinsey explains: ‘More new materials are on the way as computational-materials science combines computing power and associated machine-learning methods and applies them to materials-related problems and opportunities.’

5G networks will help take autonomous vehicles from tentative - to widespread use.

So, which sorts of advanced materials are we talking about? These include nanomaterials that enable more efficient energy storage, lighter materials for the aerospace industry, and biodegradable nanoparticles as drug carriers within the human body.

These are just four of the 10 areas explored in the fascinating McKinsey Digital’s Tech Trends report. To read more about the rest, visit: https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/the-top-trends-in-tech

Every day, there are subtle signs that machine learning is making our lives easier. It could be as simple as a Netflix series recommendation or your phone camera automatically adjusting to the light – or it could be something even more profound. In the case of two recent machine-learning developments, these advances could make a tangible difference to both microscopy, cancer treatment, and our health.

The first is an artificial intelligence (AI) tool that improves the information gleaned from microscopic images. Researchers at the University of Gothenburg have used this deep machine learning to enhance the accuracy and speed of analysis.

The tool uses deep learning to extract as much information as possible from data-packed images. The neural networks retrieve exactly what a scientist wants by looking through a huge trove of images (known as training data). These networks can process tens of thousands of images an hour whereas some manual methods deliver about a hundred a month.

Machine learning can be used to follow infections in a cell.

In practice, this algorithm makes it easier for researchers to count and classify cells and focus on specific material characteristics. For example, it can be used by companies to reduce emissions by showing workers in real time whether unwanted particles have been filtered out.

“This makes it possible to quickly extract more details from microscope images without needing to create a complicated analysis with traditional methods,” says Benjamin Midtvedt, a doctoral student in physics and the main author of the study. “In addition, the results are reproducible, and customised. Specific information can be retrieved for a specific purpose."

The University of Gothenburg tool could also be used in health care applications. The researchers believe it could be used to follow infections in a cell and map cellular defense mechanisms to aid the development of new medicines and treatments.

Machine learning by colour

On a similar thread, machine learning has been used to detect cancer by researchers from the National University of Singapore. The researchers have used a special dye to colour cells by pH and a machine learning algorithm to detect the changes in colour caused by cancer.

The researchers explain in their APL Bioengineering study that the pH (acidity level) of a cancerous cell is not the same as that of a healthy cell. So, you can tell if a cell is cancerous if you know its pH.

With this in mind, the researchers have treated cells with a pH-sensitive dye called bromothymol blue that changes colour depending on how acidic the solution is. Once dyed, each cell exudes its unique red, green, and blue fingerprint.

By isolating a cell’s pH, researchers can detect the presence of cancer.

The authors have also trained a machine learning algorithm to map combinations of colours to assess the state of cells and detect any worrying shifts. Once a sample of the cells is taken, medical professionals can use this non-invasive method to get a clearer picture of what is going on inside the body. And all they need to do all of this is an inverted microscope and a colour camera.

“Our method allowed us to classify single cells of various human tissues, both normal and cancerous, by focusing solely on the inherent acidity levels that each cell type tends to exhibit, and using simple and inexpensive equipment,” said Chwee Teck Lim, one of the study’s authors.

“One potential application of this technique would be in liquid biopsy, where tumour cells that escaped from the primary tumour can be isolated in a minimally invasive fashion from bodily fluids.”

The encouraging sign for all of us is that these two technologies are but two dots on a broad canvas, and machine learning will enhance analysis. There are certainly troubling elements to machine learning but anything that helps hinder disease is to be welcomed.

Machine Learning-Based Approach to pH Imaging and Classification of Single Cancer Cells:
https://aip.scitation.org/doi/10.1063/5.0031615

Quantitative Digital Microscopy with Deep Learning:
https://aip.scitation.org/doi/10.1063/5.0034891