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Artificial Intelligence (AI) can be utilized increasing in the nanotechnology and chemical industries. While the majority of this attention has been on designing, synthesizing and scaling-up aspects of small-scale pharmaceutical, fine chemical as well as novel nanomaterials there is a second aspect in which AI could benefit these industries as well as the scientific community. This is through helping develop safe nanomaterials.
The Importance of Nanomaterial Safety
Like all chemical substances or materials nanomaterials should be secure. Every chemical in the market is scrutinized and certified safe for human consumption or handling, even in products that humans could be in contact. Nanomaterials aren’t any different. The safety of several nanomaterials may be more crucial than the standard chemical.
The majority of chemicals have been in use for quite a while, and their dangers are well-documented. In addition, many of the new chemicals are built on previous chemicals, which means the risks are typically simpler to determine and quantify. Nanomaterials, in the overall scope of the universe, tend to be more recent. Because of their tiny dimensions and their compositional variations and their unique properties, nanomaterials’ behavior are quite different from traditional chemicals and more bulky materials. Thus the properties and safety of nanomaterials should be assessed on a individual basis in great detail since there are many more variables to consider and they often get to the surface, interact with, and even penetrate areas that conventional chemicals cannot, such as biological barriers and cell systems.
Does this mean that nanomaterials aren’t safe? Not at all. Nanomaterials are subject to rigorous tests where numerous researchers and regulatory agencies collaborate to categorize them and make sure their safety. When there is a risk of harm, the various bodies define the boundaries at which the nanomaterial could be harmful (or dangerous) and provide steps on how to best produce or use it, as well as integrate it into various products, without creating harm.
However, there is another aspect that contributes to the perception of the safety of nanomaterials. Through the years there has been inadequate coverage from media about the so-called dangers of nanomaterials. For instance, “carbon nanotubes are the new asbestos.” While we should be aware and be attentive to every nanomaterial that are used, people without a technological background are unaware the fact that nanomaterials are found in our daily lives (including in foods that are ingested) and are safe.
This isn’t to suggest that we should ignore nanomaterials’ rules and regulations for safety. In fact, the opposite is true. As with all materials there are nanomaterials that can be harmful, which is why we don’t hear about them being discussed or utilized. Alternatives are discovered which are more secure. It is this strict security procedure — which involves a number of chemical and physical tests and studies of various scenarios that have led to the nanomaterials we’ve studied to be commercially available.
Therefore, although the majority of nanomaterials are safe to use, we can only be sure that they’re safe due to the diverse processes being used. This must continue, and the more precise the information about an individual material is precise the predictions will be about the security of the nanomaterial at different conditions and amounts. Although it is different with each material the process of testing can take a long time and involve both computational and physical methods. AI is currently becoming a possible option to assist scientists and regulators make more informed decisions regarding the safety of various nanomaterials.
Using AI to Compliment Nanosafety Protocols
AI algorithms, and in particular machine learning, are able help scientists provide the most precise information to regulators and standard agencies. The process known as “Safe By Design’ can be employed for many chemicals to ensure whether they’re safe for occupational handling as well as final products. In recent years it has been discovered that the Safe by Design approach has been applied to nanomaterials. Secure by Design involves a lot of information. This is where AI can help scientists make sure that nanomaterials are secure.
It’s important to remember that the application of AI to determine nanosafety is a relatively new concept. Since nanomaterials are harder to model and predict in comparison to bulk material, more research needs to be completed on the algorithmic side before we can see the application of AI in the field of nanosafety often and at a global level. But, there’s lots of potential for it for different fields.
Safe by Design
The Safe by Design is a concept that includes a process, a method, and a collection of tools that create nanomaterials safe, as well as all other materials, in general. The idea is straightforward. Develop the production process keeping safety at the forefront at all times (using various safety tools) instead of developing the system first, and then focusing on safety aspects later.
There are three components to Secure by Design. They are secure products and use and the production of safe industrial products. Therefore, by analyzing information of risk assessment, the various aspects of manufacturing pertaining to occupational exposure in addition to exposure factors and waste and you will have many details about the safety of various nanomaterials in various environments. In addition, data is taken on the safety of the nanomaterials (which requires a thorough analysis of the nanomaterials and their toxicity profile the nanomaterial and the in vitro testing) and the degree of safety it has in the product intended for use in the end.
Instead of being able to count on the safety inherent in the nanomaterial as a result of various tests, Safe by Design enables manufacturers and users of nanomaterials to use them safely and integrate the nanomaterials in their product. That means that even if the initial tests of nanomaterials can be a little risky it is possible to take steps during the manufacturing and integration phases to ensure that they aren’t a threat to the employees in the manufacturing industry as well as to all of the people customers who buy products made of the material.
Then, where is AI play a role? As mentioned previously there’s an enormous quantity of information collected about the creation of nanomaterials, their secure integration and details about the nanomaterials themselves. AI is not just employed to predict the properties of nanomaterialsbut is also able to analyse all the data concerning the safety of nanomaterials in general.
AI is widely utilized to predict the properties and structure of various nanomaterials. Machine learning algorithms are able to utilize all the data scientists have on the various characteristics, compositions and behavior that nanomaterials exhibit (and any other molecule, in general) to predict the characteristics the nanomaterial is likely to exhibit within itself and in other situations. Utilizing historical data and then analyzing the most relevant nanomaterial, AI algorithms can make precise forecasts that could be applied along with the Physical characterization findings.
In the area of profiling, AI can be extremely useful to determine the safety and toxicity of nanomaterials within biological environments because of the numerous elements at play, as well as the numerous biological environments that can be affected. Through AI techniques, each of of these elements can be encoded as specific units of operation and the information from these operations points can be used to create an idea of the way in which the material is likely to behave the the in-vivoand the in-vitro environments.
It is usually done by comparing chemical structure with those with known properties and toxicological consequences. Being able to access all the data associated with all the various chemical profiles is almost impossible for a person, however AI algorithms are able to be able to access this information (and comprehend it) by using it to build a model from the literature of science. The AI can then predict the toxicity profiles of the nanoparticle/nanomaterial of interest directly from their structure and physiochemical properties.
This is a great base for determining what clinical research should be focused on (that will result in a more accurate output of the experiments) in the sense that they are essential for finding out the toxic profile (and hence the security) for the particular nanomaterial. For instance, if AI studies show that certain nanomaterials can be harmful to a specific kind of tissue or cell Researchers can study this (especially in cases where it may not initially be a issue). This is an important aspect where AI can enhance nanosafety efforts and may be used to decrease the amount of resources needed as well as the cost of clinical trials.
Incorporating AI to Safe by Design
While identifying a nanomaterial’s individual properties is the mainstay of an Safe by Design approach, the entire ecosystem of nanomaterial production and usage is equally important and AI can be utilized within the larger Safe by Design aspects as well. The Safe by Design approaches not only directly benefit the producers and consumer, but the outcomes of these protocols could also be used to create standards and regulations, so when AI could make the process more precise and precise, it can also benefit the scientific community in general, not only those directly involved in the research.
In the manufacturing process, AI algorithms can provide more precise information on the amount of exposure to nanomaterials in various occupational environment. Sensor data from the production line as well as from the environment around it can be analysed using algorithmic machine learning. By recording the particular characteristics of the nanomaterials and non-nanomaterials in the working environment the AI algorithm can give a more accurate assessment of exposure levels and the potential occupational dangers. This will allow more precise security procedures to be put into the case of every nanomaterial (if needed) in the process of production.
Another element in the production process is in the automation of nanomaterials’ production process itself. Although this isn’t directly related to nanosafety or Secure by Design, the ability to monitor the levels of exposure of nanomaterials, and also automate the process itself can reduce the need for workers to be present in the vicinity of the manufacturing line, reducing the risk for occupational exposure, creating a safer work area for those working with nanomaterials.
Other areas in which AI could aid to improve the general Safe by Design approach is in the refinement and analysis of all the data and using it to identify patterns that indicate risks to safety (be it in the nanomaterial or in its usage). In general, lots of information is required to support a claim that a nanomaterial is secure and AI can be utilized to improve the quality of this data, so that only the pertinent information is made available (without having to spend a lot of time making it a manual process of correlating it). Furthermore some of the issues about the safety and characterisation tests for each nanomaterial–as well as the advantages of making use of AI algorithms can be applied to the final product they’ll be utilized in, making sure that the user and people in general are protected.
In general, the areas in which AI can have the greatest importance is in the prediction analysis of nanomaterials within biological environments, allowing their toxic profiles to be determined. But, AI automation and monitoring techniques could be able to create the environment for production of nanomaterials that is safe also.
Conclusion
Nanomaterials receive a lot of publicity as dangerous due to their small dimensions. However, a lot of them are completely safe and in commercial settings they must be tested thoroughly before being made available to the public. In recent years, Safe by Design protocols have been adapted for nanomaterials to ensure that they are safe for anyone who handles them, as well as anyone who buys a nanomaterial-enhanced product (and to take appropriate steps if they are hazardous).
These protocols and tests generate an abundance of data that needs to be collected, analyzed and then presented. We are all aware that AI algorithms can analyze and sort data more precisely and efficiently than humans. So, while it is currently in its infancy and more work needs to be done before they are implemented regularly, there is the potential for AI to be used in Safe by Design process for nanomaterials (and other materials) to speed up the process and improve the safety of nanomaterial-enhanced products and raw nanomaterial products.
Integrating AI Nanomaterial Analysis
So, where does AI come in? As previously mentioned, there is a vast amount of data collected regarding the production of the nanomaterials, their safe integration, as well as information about the nanomaterials themselves. AI can not only be used to predict the properties of nanomaterials, it can also be used to analyze all the data regarding a nanomaterial’s overall safety.
AI is being widely used to model the properties and structures of different nanomaterials. The machine learning algorithms can take all the data that science knows about the different compositions, properties, and behaviors of nanomaterials (and any molecule for that matter) to predict what characteristics the nanomaterial will have in itself as well as in other scenarios. By taking the historical data and analyzing the current nanomaterial of interest, AI algorithms can make highly accurate predictions that can be used alongside physical characterization results.
On the profiling side, AI could become particularly useful for determining the toxicity and safety of nanomaterials in biological environments because there are many different factors in play—as well as the many different biological environments that could be affected. With AI algorithms, all these factors can be encoded as individual units of operations, and the data from each of these operation points can be used to build a model of how the nanomaterial will behave in both in-vivo and in-vitro environments.
This is typically done by comparing the structural similarities with chemicals that have known properties and toxicological effects. Having all the data related to all the different chemical profiles in existence is a near impossible task for a human, but AI algorithms can have access to this data (and understand it) by extrapolating it from the scientific literature. The AI can then predict the toxicity profiles of the nanoparticle/nanomaterial of interest directly from their structure and physiochemical properties.
This can give a good starting point as to where clinical studies should focus on (that will lead to a more accurate experimental output), as these are crucial for determining the toxicity profile (and therefore the safety) of the nanomaterial. For example, If AI studies are showing that specific nanomaterials may affect a certain type of cell or tissue, the researchers can specifically look at this (especially if it might not have been an initial concern). This is a crucial area where AI could improve nanosafety efforts and could be used to reduce the resources required and the costs of clinical trials.
Integrating AI into Safe by Design
Although characterizing a nanomaterial’s own properties is the cornerstone of a Safe by Design approach, the whole ecosystem of nanomaterial production and its use is also important, and AI could find use in the wider Safe by Design aspects as well. Safe by Design approaches not only directly benefit the producer and the consumer, the results of these protocols can also be used to develop regulations and standards, so if AI can make the process more accurate, then it could also help the wider scientific community—and not just those who are directly doing the analyses.
On the production side, AI algorithms can provide more accurate results on the level of nanomaterial exposure in different occupational environments. Sensor data from inside the production line and from the surrounding environments can be analyzed using machine learning algorithms. By encoding the specific characteristics of the potential nanomaterial and non-nanomaterial species in the working environment, the AI algorithm could provide a more accurate analysis of the actual exposure levels and potential occupational hazards. This could enable more accurate safety measures to be put in place for each nanomaterial (if necessary) at the production stage.
Another aspect on the production side is in the automation of the nanomaterial production itself. While this is not directly linked with nanosafety and Safe by Design, the ability to monitor the exposure levels of nanomaterials and simultaneously automate the production itself could lead to less manpower needing to be in the general vicinity of the production line—reducing the potential for occupational exposure and leading to a safer working environment for nanomaterials.
Other areas where AI could help in the overall Safe by Design approach is in refining and analyzing all the data, using it to pick out any trends that point to safety hazards (be it from the nanomaterial itself, or its use). In general, a lot of data is needed to put forward a case that a nanomaterial is safe, and AI could be used to refine this data so that only the relevant data is put forward (without spending a long time correlating it manually). Additionally, many of the aspects regarding the safety and characterization tests of the individual nanomaterial—and the benefits of using AI algorithms—can also be extended to the final products they are going to be used in, ensuring that the end-user and the general public are also safe.
Overall, a lot of the areas where AI could provide the most significance is in the predictive analysis of nanomaterials in biological environments, enabling their toxicity profile to be determined. However, AI automation and monitoring approaches could help to provide a safer nanomaterial production environment as well.
Conclusion
Nanomaterials get a lot of coverage as being unsafe because of their small size. However, many of them are inherently safe, and to be used in a commercial setting, they need to pass rigorous testing before they reach the public. In recent years, Safe by Design protocols have been adapted for nanomaterials to ensure that they are safe for anyone who handles them, as well as anyone who buys a nanomaterial-enhanced product (and to take appropriate steps if they are hazardous).
These tests and protocols generate a lot of data that must be collated, analyzed and presented. We all know that AI algorithms can sort and analyze data much more accurately and quicker than humans can. So, while it is currently in its infancy and more work needs to be done before they are implemented regularly, there is the potential for AI to be used in Safe by Design process for nanomaterials (and other materials) to speed up the process and improve the safety of nanomaterial-enhanced products and raw nanomaterial products.
