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Nanomaterials May 2018 Viewpoints

Technology Analyst: Marianne Monteforte

Machine Learning to Speed Up Nanomaterial Innovation

Why is this topic significant?

A new US research initiative seeks to advance material scientists' use of data analytics, machine learning, and human-machine interfaces. Such computational tools can help companies' R&D divisions to drive informed nanomaterial design and innovation on an industrial scale.

Description

Lehigh University (Bethlehem, Philadelphia) has invested nearly $3 million in a new Nano/Human Interface Presidential Engineering Research initiative. The multidisciplinary initiative has several objectives, including improving scientists' ability to visualize and interact with complex multidimensional datasets, integrating computational simulation with experimentation, finding correlations in large data sets, and acquiring data using in situ characterization methods.

To attain these objectives, the center is seeding out multiple research projects, and recently released the first round of results of one of these projects. The results appear in a January 2018 Nature Research journal and reveal new computational tools to map material properties and extract useful relationships between these properties. The research combines machine learning with data-visualization techniques to help scientists infer important insights about material properties from the vast amounts of materials datasets generated by scientific research. This data-analysis approach can find patterns in multidimensional datasets from both experimental characterization techniques and computational simulations of materials behavior.

Implications

To develop materials and understand materials' properties, scientists employ a range of experimental characterization techniques, using tools such as electron microscopes and synchrotrons. These techniques generate large volumes of data, often terabytes in size. In addition, materials-property data, by nature, are highly multidimensional, making it difficult for researchers to decipher relationships between properties or to identify any outliers in the dataset.

The Nano/Human Interface Presidential Engineering Research initiative's new data-analytics and visualization tool removes any nonrelevant dimensions in the materials data, enabling researchers to clearly "see" and analyze complex nanomaterials-property relationships. Advances in the commercial application of such data-analytics tools could help R&D divisions in nanomaterials companies en route to developing complex nanomaterials systems. Also, the initiative is collaborating with cognitive scientists to investigate limitations in human mental processing and how researchers interpret and present data. The outcomes of such active research are worth monitoring because of the potential to reveal new ways to integrate artificial intelligence and humans in nanomaterials data processing and innovation.

Impacts/Disruptions

Data science is emerging as an important field that can facilitate nanomaterials developers in their design of materials (including nanomaterials). It will also help to optimize industrial-scale manufacturing processes. Today, many companies are likely sitting on vast amounts of materials data, which they lack the tools to make full use of—materials data are only as good as the human's ability to use them and interpret them. The emergence of advanced machine-learning and data-mining tools and algorithms (such as the new quantum algorithms) in combination with advances in computing power can significantly facilitate scientists' deciphering insights from materials data. Already many materials-informatics companies exist as a result of the rise in cloud computing, government support, and data-analytic capabilities—and industry players are using their services to infer materials property trends or relationships hidden within their vast existing materials datasets. Companies that employ methods to analyze both existing data and new data efficiently will most likely gain a competitive advantage by enabling efficient approaches to improving or optimizing their product offerings.

Scale of Impact

  • Low
  • Medium
  • High
The scale of impact for this topic is: Medium to High

Time of Impact

  • Now
  • 5 Years
  • 10 Years
  • 15 Years
The time of impact for this topic is: 5 Years to 10 Years

Opportunities in the following industry areas:

Machine learning, human-machine interfaces, materials data, data analytics, artificial intelligence, nanomaterial R&D, manufacturing, big data

Relevant to the following Explorer Technology Areas:

Graphene's New Application: Hair Dye?

Why is this topic significant?

New research reveals a graphene-based hair dye that uses nontoxic chemicals and works just as well as commercial permanent dyes. However, instead of chemically altering the hair, it simply coats it. Despite the potential commercial benefits of this technology, possible safety and environment implications of adopting graphene-based products for cosmetic applications will likely hinder its uptake.

Description

In April 2018, researchers at Northwestern University (Evanston, Illinois) and at the Engineering Laboratory for Functionalized Carbon Materials at Tsinghua University (Shenzhen, China) released the results of their study, "Multifunctional Graphene Hair Dye." The research team demonstrated that graphene oxide (GO) and reduced GO can create multiple shades of brown to black hair dyes. A user applies the dye onto hair by spraying, brushing, and drying.

According to the research team's study, the graphene-based hair dye has the same durability that commercial permanent hair dyes have but contains no hazardous organic solvents or toxic ingredients. Furthermore, instead of chemically altering the hair, the graphene forms a smooth coating on it. In its study, the team claims that the dye can "bestow a broad range of new optical, electrical, thermal, and biochemical properties to hair, enhancing comfort level and cosmetic performance." The team shows that the GO-based dye remained in the hair even after 30 washes—long enough to classify it as "permanent."

Implications

Typically, commercial hair dyes require the use of harsh chemicals, such as ammonia and bleach, to force open the cuticle scales of the hair and allow diffusion of the colorant molecules inside, which then triggers a reaction for the hair to produce more dye of the same color. This conventional process of dying damages the hair, whereas the GO-based dye does not because it is amphiphilic and adheres to many types of surfaces to form a smooth coating. The GO sheets in the dye are inexpensive to manufacture and are usually made by the chemical exfoliation of graphite powders using strong oxidizing agents.

The scientists say the graphene-based dye is a safer alternative to particle-based dyes, which are easily inhalable or skin permeable and claim that graphene is much less likely to enter the skin barrier. Although a large body of existing research suggests that graphene is not particularly dangerous to use, the safety of the cosmetic use of graphene still requires investigation. Considering that hair dye will wash down the drain and into the environment, prospective developers of GO-based hair dyes will need to undergo due diligence to show consumers and investors that graphene does not cause damage to the environment or human health.

Impacts/Disruptions

The personal-care industry has been a keen adopter of nanotechnology, using it to develop products with improved properties—such as better transparency, longer-lasting color, antioxidant properties, greater ultraviolet protection, and improved smoothness. Players continue to demonstrate the performance and commercial feasibility of graphene in many new applications. Although the use of graphene in a hair dye triggers environmental concerns that need addressing, the coating technology may be more likely to see development for use in other products such as electronic textile wearables or responsive skins for use in humanoid robots.

Scale of Impact

  • Low
  • Medium
  • High
The scale of impact for this topic is: Medium

Time of Impact

  • Now
  • 5 Years
  • 10 Years
  • 15 Years
The time of impact for this topic is: 5 Years to 10 Years

Opportunities in the following industry areas:

Fashion, cosmetics, wearables, graphene oxide, robotics, electronic tattoos

Relevant to the following Explorer Technology Areas: