The Inefficiencies of Nanomaterial Production

Nanomanufacturing processes have the potential to greatly improve the properties of materials, resulting in nanomaterials that are stronger, lighter, more durable, among other traits. Current applications of nanomaterials that already take advantage of these properties include tools with ultrasensitive detection and identification of toxins, highly efficient and low-cost solar cells, nanomedicine, and many more.

While innovation in the nanomaterial industry is growing, the development is complicated by manufacturing problems. Since trial-and-error scaling and quality assurance is expensive and tedious, nanomaterial manufacturing is more complex than traditional manufacturing. As a result, nanotechnology companies cannot fully utilize their resources and optimize their production.

This post will be breaking down the current problems in nanomaterial production including:

  1. Production is slow
  2. Production is expensive
  3. Harsh environmental impact

Production is Slow

There are two approaches to nanomaterial production: top-down and bottom-up methods. The top-down method starts with a large piece of material and “carves” it down to the nanoscale, while bottom-up fabrication builds products by assembling them up from the atomic and molecular scale. Both methods are time-consuming and in the case of the top-down method, a waste of excess material.

The current approach to improve nanoparticle synthesis involves analyzing high-resolution microscope images and identifying each particle. Either a free software that is unreliable for samples with complex geometries and compositions is used or graduate students have to spend hours manually counting the particles in each image. As a result, valuable resources are being wasted on slow and inaccurate processes.

Production is Expensive

Because nanotechnology is difficult to manufacture, it is also very expensive. Nanotechnology R&D and nanomanufacturing requires very advanced and expensive facilities and equipment. The current manufacturing process involves painstaking batch by batch labor that is frequently bottlenecked, creating these high costs of production. For example, gold nanoparticle production costs USD $80,000/gram while raw gold costs USD $50/gram. The gold is not making it expensive. The problem lies with mass-producing them cheaply.

Environmental Impact

Inefficient nanomaterial production has negative long-term environmental effects. Stricter requirements for material purity, lower tolerances for defects, and lower manufacturing yields also lead to greater harm to the environment than traditional manufacturing processes. For example, bacteriostatic silver nanoparticles used in socks can pollute the wastewater stream and destroy bacteria that are essential to the ecosystem. Another study completed at Ohio State University found the life-cycle environmental impact of carbon nanofibers was about 100 times greater per unit than traditional materials. Therefore, improved efficiency in nanoscale manufacturing is necessary to reduce energy use, emissions, waste, and toxic input and output materials.

Main Takeaways

Inefficient nanomaterial production is slow, costly, and damaging to the environment. Therefore, nanotechnology companies will greatly benefit if their nanomaterial manufacturing processes can be optimized. In turn, the time and money previously devoted to production can instead be used to improve other aspects of their business. To learn more about how to improve nanomaterial production, contact us on our website.

What is Nanoinformatics?

Nanomaterials combine the complexities of molecules and materials. However, dealing with the challenges of low manufacturing yields and defects of individual samples have been secondary to searching for new properties and applications of nanomaterials. For science to become technology, machine learning will need to play a big role in navigating the large amount of data associated with these complexities. Applying data-driven methods will be crucial in the discovery and development of new nanomaterials that can improve the efficiency of many processes that span multiple industries. In this post, we’ll give a comprehensive overview of nanoinformatics and show how it’s used in industry.

What is Nanoinformatics?

Nanoinformatics is the use of informatics techniques to collect, process, and store information about the characteristics of nanoparticles, their environments, and applications. Traditional analysis looks at data sources individually, but informatics effectively brings multiple data streams together. This emerging field working at the intersection of informatics, nanotechnology, and traditional science develops tools to process nanoparticle data more efficiently.

Why is Nanoinformatics Important?

Automation has made it possible to synthesize and characterize materials faster, leading to the growth of both new materials and massive data sets. Nanomaterial data sets typically suffer from the same data challenges as other domains like variability, bias, and sparsity. However, these problems are further exacerbated by the greater complexity of the information collected. As a result, computational methods for analyzing these large, multidimensional data sets are rising in significance, sophistication, and utility.

Examples of Nanoinformatics in Action


Current informatics technology is typically focused on collecting, representing, and linking information together, but this isn’t enough to obtain greater insights into the complexities of environmental and multi-gene interactions in human diseases. Through using nanoinformatics, the growing amount of -omics data can be translated into personalized medical diagnoses and therapies for patients. For example, nanoinformatics can contribute to a new wave of nanodevices like nano-based screening tests and bio-sensing systems that are designed to diagnose and treat children with cancer and genetic diseases.


Researchers are also using nanoinformatics to develop models for classifying the ecotoxicity of nanomaterials. Through building a single model for multiple species and individual species-specific models, machine learning can generate across-species and species-specific models to predict toxicity with significant predictive power. The models can also provide insight into which nanoparticle compositions and descriptors are creating the most adverse biological effects on the environment.

Key Takeaways

More inventive uses of machine learning and statistical modeling will develop as nanoinformatics becomes more advanced. By effectively incorporating data science into nanotechnology, the discovery of nanomaterials will accelerate through more efficient data analysis and characterization. Therefore, nanoinformatics will be essential to solving a variety of problems across all industries.

Contact us today to see how AtomicAI can help your company optimize your nanomaterial production using data science.