Lithium-ion batteries (LIBs) are everywhere in our lives. LIB application fields are even growing into military missions and advanced exploration, which require resistance to severe cold in the polar region of the Earth and in outer space. But even state-of-the-art LIBs suffer from huge performance degradation as temperatures decrease, and they stop operating at all in extreme cold.

We need to design and engineer novel electrolytes with tailored chemical, electrochemical and physical properties to address the challenge. Our Vilas Pol Energy Research (ViPER) group introduced a low-melting-point ether solvent to do just that: maintain the liquid state within a lithium-ion battery in extreme cold.

Guinness World Records™ took note. During a demonstration in a specially designed ultralow temperature lab, we charged and discharged multiple lithium-ion batteries at -100°C. Accordingly, we hold the Guinness World Records title for the “lowest temperature to charge a lithium-ion battery.”

The award certificate is more than a novelty. This discovery goes a long way toward overcoming the absence of reliable power storage in extreme conditions. This limitation poses a major roadblock to strengthening national security, by hindering our increased presence in those environments. For example, for successful space exploration of Mars and the moon, the first important thing we need to have is the energy conversion (solar) and storage (batteries) sources working together to provide the requisite energy supply to the mission.

The traditional approach to battery uses in extreme cold conditions is a secondary heating system installation to maintain the battery’s proper internal temperatures. This method cannot resolve the intrinsic limitation of the LIB; the power sources to operate the heating system also must come from somewhere. Therefore, we must incur additional cost, energy and mass to generate heat for thermal insulation. Our idea was to remove those hurdles and make the lithium-ion batteries function without add-on heating or cooling systems.

Electrolyte-related issues, such as electrolyte freezing and inefficient lithium-ion (Li+) transport, have been identified as a primary factor for the poor performance of LIBs at cold temperatures. The currently used high-melting-point carbonate-based solvents freeze around -10°C and show high Li+ desolvation energy, dramatically decreasing ionic conductivity at cold temperatures.

Our low-melting-point ether solvent (cyclopentyl methyl ether, or CPME) overcomes that constraint. Although ether solvents have been avoided for LIB electrolytes because of limited stability, we adjusted Li+ solvation in the CPME-based electrolyte to use it for an LIB electrolyte.

Apart from the electrolyte side, simulating the extreme cold temperature conditions for the battery tests was confined by commercially available battery cyclers and temperature chambers. Therefore, we also devised a battery testing system to efficiently measure performance of our batteries at extreme cold temperatures (from room temperature to 185°C).

While electrolyte research has enhanced LIB performance in cold climate conditions, the fundamental underpinning principles for Li+ transport in the electrolytes remain unknown, presenting a colossal knowledge gap. Because we cannot directly observe how Li+ moves in the batteries due to the operational nature of the LIBs, mathematical, computational and experimental techniques are necessary to design and optimize LIBs for extreme cold temperature usage.

Our research was supported by the Office of Naval Research (ONR), with assistance from the Purdue Research Foundation in filing patents and the Purdue Office of Technology Commercialization in identifying licensing opportunities. Our team collaborates closely with Thomas Adams, PhD, a research engineer at the Naval Surface Warfare Center Crane (NSWC Crane), to materialize the successful creation, demonstration and utilization of advanced LIB batteries in submarines, high-altitude air vehicles, and the polar regions of Earth.

LIBs functioning at lower temperatures have a great future in the U.S. Space Force and other military and space applications. Achieving a Guinness World Record is a first step toward demonstrating that developing such complex battery technologies is possible. This evidence provides enthusiasm, encouragement and passion for young researchers to think of novel solutions and go beyond the current state of the art.

A Purdue graduate student researcher, Soohwan Kim, played a significant role in achieving the Guinness World Record. He designed an instrument able to reach the extreme low temperature, as well as developed the electrolyte that makes the battery operational without freezing the electrolyte at that ultralow temperature — leading to numerous tangential inventions and published papers.

Advanced lithium-ion batteries, emanating from a Purdue discovery, could be the next “giant leap.”

After all, Purdue Engineering graduate Neil Armstrong was the first person to step on the moon, on July 20, 1969.

Vilas G. Pol

Professor, Davidson School of Chemical Engineering

Head, Vilas Pol Energy Research (ViPER) Group

College of Engineering

Purdue University

Related Links

Purdue’s ViPER group sets Guinness World Records title for the lowest temperature, -100 degrees Celsius, to charge a lithium-ion battery

Professor Vilas Pol receives DOD grant to test Li-ion batteries at extreme temperatures

DOD supports ViPER group’s research and development leading to safer quasi-solid state battery prototyping equipment

ViPER group website

Professor Vilas Pol receives Trask Innovation funds to further novel research in sustainable, low-cost sodium-ion batteries

Charging a lithium-ion battery at -100°C to set Guinness World Record was originally published in Purdue Engineering Review on Medium, where people are continuing the conversation by highlighting and responding to this story.

It’s the stuff of science fiction — or it was until recently. Fantastic Voyage, a 1966 movie and related book, featured miniaturized submarine crew members traversing an injured scientist’s body to repair brain damage, and the 2014 animated film Big Hero 6 presented swarming healthcare microrobots.

For years, biomedical engineers have dreamed of bringing such technologies to life. For example, what if robots smaller than a grain of rice could move through the human body to deliver medications, and eventually also conduct diagnostic tests, clear blood clots and even perform surgeries — all with greater precision, fewer side effects and less trauma than conventional medicine allows?

These visions are becoming realities through research at Purdue’s College of Engineering in collaboration with the Indiana University School of Medicine (IUSM). Our ultimate goal is to design and implement an integrated microrobotics system for targeted delivery of custom therapeutics inside the human body for various diseases.

Unmet need — and opportunity

Currently, we’re focusing on colorectal disease — in particular, finding better ways to treat inflammatory bowel disease (IBD). Several factors point to an unmet clinical need, and opportunity, for more efficacious and less toxic drug treatments for this disease. IBD patients face significantly increased risk of colorectal cancer. However, today’s IBD pharmaceutical options are either topical and inconvenient or systemic with limited effectiveness and risk for serious adverse effects. A powerful solution for delivering medications locally and precisely would represent a major breakthrough.

Our team is creating magnetic microrobots and designing systems to help them provide targeted, localized drug delivery in the colon. Compared with conventional techniques, actively guided microrobots hold promise to be more effective, lower the risk of side effects and trauma, and offer higher drug retention rates. Benefiting from continuing design and fabrication advances, microrobots can be wirelessly controlled and steered to navigate complex topographies in the human body, such as those in the colon, to reach specific locations.

In 2020, using the colon of an anesthetized live mouse, we successfully demonstrated the first microscale robot tumbling through a biological system in vivo. Now, supported by a $1.11 million National Institutes of Health (NIH) grant, we are working to prove that a combined actuation/imaging system with high resolution, cross compatibility, a small footprint, and tissue penetration capabilities can be developed to actively guide minimally-invasive in vivo magnetic microrobots for the on-demand local delivery of compounds to treat IBD.

Engineering-medicine collaboration

Our research is part of the Purdue Institute for Cancer Research, and it aligns with Purdue Engineering Initiatives in Autonomous and Connected Systems and Engineering-Medicine.

The NIH-funded project has three aims, each led by a different Purdue team member. David Cappelleri is responsible for designing and building microrobots that can tumble (think back flips and side flips) through a live colon using external magnetic fields. The microrobots are made — inexpensively of polymer and metal, and nontoxic and biocompatible — at Purdue’s Birck Nanotechnology Center. Luis Solorio is leading efforts to design a drug loading and release system to enable microrobots to deliver a therapeutic payload. Craig Goergen is heading up designing a focused ultrasound heating system that the microrobots can use for active in vivo targeting and delivery of a therapeutic payload, as well as using traditional ultrasound for real time in vivo imaging of the microrobots.

Through the Engineering-Medicine initiative, Dr. Thomas F. Imperiale, at IUSM, is the clinical expert, advising on all aspects of the work. Trained in clinical epidemiology and gastroenterology, he directly treats many IBD patients in his clinical practice.

Challenges surmounted

We’ve overcome key challenges.

First, we achieved mobility and control of microrobots in various terrains and environments such as those in the body, including wet, sticky and non-smooth surfaces. We designed the microrobot to rotate with the rotating magnetic field, as cars and trucks traverse rough topography — with wheels that can roll over the magnetic field. The rectangular microrobot essentially functions as a rotating wheel driving over diverse terrains. We can speed it up or slow the magnetic field down by changing the frequency of the magnetic field. We also can modulate the direction of the field in the plane to steer the robot to different locations.

Next, we investigated how we could attach a drug payload to the robot for precise delivery to a targeted location. We developed a unique electrospraying technique to coat the robot with a mock drug payload and demonstrate delivery capabilities.

Of course, it’s necessary to know that the microrobot is at the targeted location. To visualize a microrobot, we developed an integrated actuation/imaging system consisting of a rotating permanent magnet and an ultrasound imaging device. The system was designed for small animal research so we could evaluate the entire system with in vivo studies. This is the first step toward translating our technology to humans for precision medicine applications.

What’s next?

Ability to directly access parts of the body that human medical providers can’t reach opens the possibility of more effective targeted treatment, ultimately throughout the body. Among important advantages, without the risk of systemic effects, drug concentrations can be more potent and diagnostic sampling can be more precise.

In the near future, we envision deploying teams and/or swarms of tumbling microrobots for diverse targeted drug delivery and diagnostic procedures — from chemotherapy treatments to biopsies to colonoscopies — in the gastrointestinal tract. Minimally invasive surgeries are a longer-term prospect.

New versions of microrobots can be created for additional areas of the body. For example, we’re developing a class of swimming microrobots that could work in arteries and other fluid-filled locations, clearing blood clots, delivering drugs and taking samples. Other classes of microrobots may arise to repair cells or wounds in vivo. Patients with cancers and neurological diseases are among those who could be helped.

We expect microrobots’ capabilities to expand as well. New opportunities are emerging with the advent of 3D and 4D printing. 3D printing of smart materials can give us 4D-printed microrobots, which can change their shape and/or material properties with different stimuli, enabling higher versatility and complexity in advanced applications.

Microrobots hold nearly unlimited potential for improving medical therapies — better treating and diagnosing disease, sparing patients side effects and trauma, and ultimately saving lives.

David J. Cappelleri, PhD

Assistant Vice President for Research Innovation, Office of Research

B.F.S. Schaefer Scholar and Professor, School of Mechanical Engineering
Professor, Weldon School of Biomedical Engineering (by Courtesy)
College of Engineering

Purdue University

Craig J. Goergen, PhD

Leslie A. Geddes Professor of Biomedical Engineering
Director of Clinical Programs, Weldon School of Biomedical Engineering
Faculty Council Co-Director, Purdue Engineering Initiative in Engineering-Medicine
College of Engineering, Purdue University

Adjunct Professor of Surgery
Indiana University School of Medicine

Luis Solorio, PhD

Associate Professor of Biomedical Engineering
College of Engineering, Purdue University

Thomas F. Imperiale, MD

Distinguished Professor, Indiana University
Lawrence Lumeng Professor of Gastroenterology and Hepatology
Professor of Medicine
Indiana University School of Medicine

Research Scientist, Roudebush VA Medical Center and Regenstrief Institute, Inc.

Adjunct Professor, School of Public Health

Medical Director, Margaret Mary Health

Related Links

Tackling inflammatory bowel disease with tumbling microrobots

All-terrain microrobot flips through a live colon

Swimming microrobots achieve record speeds, thanks to microscale 3D printing

Soft microrobots demonstrate potential for targeted drug delivery

‘World’s smallest drum’ expands horizons for microrobotics research

Tumbling microrobots tackle inflammatory bowel disease — and that’s only the beginning was originally published in Purdue Engineering Review on Medium, where people are continuing the conversation by highlighting and responding to this story.

Promise is one thing, execution another. The promise of digital agriculture is data-driven precision leading to higher crop yields and sustainable resource utilization. But the execution of any plan to realize the promise is fraught with failure, because the wealth of data and insight is not easily accessible and transferable.

One problem is silos: not the ones that store the farmer’s grain, but the silos that hem in the data. The data-limiting silos deny farmers the timely content on field and crop status and yield-improving strategies they need to fully exploit their new, sensor- and compute-pervasive, digitally-rich world. The silos often result in data being separated from its context.

The Open Ag Technology and Systems (OATS) Center at Purdue is working to open things up. OATS represents a collaborative effort between Purdue’s colleges of Engineering and Agriculture. It operates around the “farmer-first, farmer-centric” principle: that the digital agriculture revolution can only be fully realized through an open-source development paradigm, in which some of the software behind tools farmers use to make their data valuable and to keep their data private are exchanged freely between people, systems and projects, with the highest levels of interoperability, automation and trust. The automation and trust are important with regard to adoption.

In agriculture today, data either is a hassle or has unclear value. Everyone wants automation, but they don’t want, or don’t know how, to deal with the data — they just want answers. The information technologies used in the food/ag industry and in university research lag behind the state of the art in other industries, due in part to the absence of the thriving open-source communities that have propelled progress in the broader technology sector, where data can flow freely between devices and systems.

This drawback keeps agriculture from taking advantage of many of the most promising avenues for sustainable agriculture system improvements — from novel applications of sensing, networking, computation, and wireless communications to big data science, visualization, and analytics. Powerful datasets and models are researched and developed at the individual-discipline and regional levels, but fundamental issues prevent their translation into practice.

For example, the full value in data is realized only when the data can be integrated from multiple, context-rich sources in ways that adhere to the trust requirements of its owners. The data and algorithms produced by publicly funded food/ag research are not easily obtainable for verification, extension, or translation to practice. The rapid rise of data-driven agriculture also has left many stakeholders short of the proper data analytics, software development, and computational thinking skills necessary to thrive.

These issues are solvable by open-source software enabling data and algorithm exchange. For example, our open-source edge computing framework called Avena addresses this shortcoming; it includes standardized packages of code operating on an open-source platform to remove barriers of vendor lock-in and lack of interoperability. Avena — developed through Purdue’s participation in the National Science Foundation (NSF) Engineering Research Center for Internet of Things for Precision Agriculture (IoT4Ag) — lets vendors safeguard their intellectual property while allowing farmers to run compatible third-party software of their choice. This creates a Google Play Store-like market for agricultural apps.

Our work has been released as open source because we believe that is needed in agriculture to encourage a more innovative environment.

We also believe expertise in computer engineering, electrical engineering, and computer science is critical to progress in digital agriculture.

To this end, Purdue has a Data-Driven Agriculture minor to help students and practitioners leverage advances in sensing, communications, and computation technologies in farming.

We also held a 10-week, USDA/NIFA-funded summer data science program focused on agriculture. Over four years, we hosted 45 students from 22 institutions. Participants learned about data science, coding, and other IT tools. They applied new knowledge gained in independent projects around the agricultural data pipeline, data wrangling, and decision making. In addition, participants were trained to operate state-of-the-art agricultural equipment.

Helping bring this open-source culture to agriculture and democratize innovation is the mission and focus of OATS. The center has several initiatives in research, education and outreach to further open systems and sustainable digital agriculture. It produces targeted online training materials, and shares its work via OATS-sponsored conferences, industry/agricultural associations, academic publications, GitHub, the web, and social media.

For instance: OATS members were part of a winning team at an international hackathon, building an open-source framework and apps for the Dutch swine industry. Our Open Ag Data Alliance project team helped enhance produce-safety data, creating an automated data exchange between trading partners and blockchain platforms. We also conducted webinars and talks on topics like agricultural data pipelines, Python, blockchain, and rural communication and networking.

OATS developments helped propel agricultural technology deployment within the Wabash Heartland Innovation Network (WHIN) project. Moreover, the NSF-funded Aerial Experimentation Research Platform for Advanced Wireless (AERPAW) testbed project, led by North Carolina State University, is using our ISOBlue Avena platform, an open-source system for connecting agricultural machines to the cloud to enable real-time data sharing.

Data is key to the future of agriculture, and open-source cooperation is the way forward. Open source built the internet. Now we’re working to better capitalize on that same kind of value in agriculture.

Dennis R. Buckmaster, PhD

Dean’s Fellow for Digital Agriculture
Professor, Department of Agricultural and Biological Engineering
College of Engineering

Co-Director, The Open Ag Technology and Systems (OATS) Center

Purdue University

Agricultural Response Systems Co-Lead, NSF Engineering Research Center for the Internet of Things for Precision Agriculture (IoT4Ag)

James V. Krogmeier, PhD

Professor, Elmore Family School of Electrical and Computer Engineering, and Department of Agricultural and Biological Engineering (by Courtesy)
Associate Dean for Facilities and Planning
College of Engineering

Co-Director, The Open Ag Technology and Systems (OATS) Center

Purdue University

Research and Executive Committee Co-Lead, NSF Engineering Research Center for the Internet of Things for Precision Agriculture (IoT4Ag)

David J. Love, PhD

Nick Trbovich Professor of Electrical and Computer Engineering, Elmore Family School of Electrical and Computer Engineering
College of Engineering

Director, neXt G (XGC) Center for Communications and Sensing

Team Member, The Open Ag Technology and Systems (OATS) Center

Purdue University

Communication and Energy Systems Research Co-Lead, NSF Engineering Research Center for the Internet of Things for Precision Agriculture (IoT4Ag)

Fellow, National Academy of Inventors

Aaron Ault

Senior Research Engineer, The Open Ag Technology and Systems (OATS) Center
Elmore Family School of Electrical and Computer Engineering
College of Engineering

Purdue University

Related Links

The Open Ag Technology and Systems (OATS) Center

OATS leads the way to the future of agriculture through open-source development

The Internet of Things for Precision Agriculture (IoT4Ag): An NSF Engineering Research Center

Purdue partners to build NSF-funded Aerial Experimentation Research Platform for Advanced Wireless (AERPAW)

Purdue Engineering Review: ‘Data science tills the fields’

Realizing the promise of digital agriculture was originally published in Purdue Engineering Review on Medium, where people are continuing the conversation by highlighting and responding to this story.

Anything that can drive costs down and efficiency up is a welcome development in solar energy, as the world turns inexorably toward more sustainable energy. Part of the solution may be a non-silicon, light-absorbing material that could reduce solar cell costs by more than 75%.

Perovskite — discovered in the Ural Mountains, and named after the Russian mineralogist Lev Perovski — is a light-absorbing semiconducting material that can be used to make solar cells in a simple process at very low cost. The resultant cells have a power conversion efficiency comparable to that for traditional silicon cells (~26% for the best perovskite solar cell, versus ~27% for the top silicon cell).

Another plus: The fabrication process can be as simple as printing newspapers. In our lab, a sophomore student can make 25% efficiency devices in two days. If translated into large-scale production, the solar energy price can drop to less than one-quarter of the price of an existing silicon solar module.

Still, challenges remain. Perovskite solar cells are less stable than silicon cells, as the perovskite material can react with moisture and oxygen and decompose. The long-term stability at elevated temperatures also is inadequate. Further, the interface of the perovskite light absorber and charge-transporting (charge-collecting) layers can degrade — meaning current perovskite solar cells typically last only a few months while silicon cells can operate for more than 25 years.

Our lab is propelling efforts to overcome these limitations.

To address the stability issue, we invented a molecularly tailored “glue” that can neutralize the defects on the perovskite surface and improve the interface between perovskite and other charge-collecting layers. These molecules, called conjugated ligands, are a type of molecular semiconductor with multiple-ring structures that help conduct the charge. Conjugated ligands also interact with perovskite to remove surface imperfections.

The benefits are substantial. We improve the overall power conversion efficiency by reducing defect density on perovskite materials. We facilitate charge extraction by enhancing the contact with other charge-transporting materials. And we increase device stability by blocking moisture penetration and ion migration, as well as lessening defects and undesired charge accumulation.

We’re using advanced spectroscopy to investigate the electronic structures of materials, which includes understanding the energy of electrons and how the electrons move at the interfaces. We also use advanced microscopes to explore material morphologies, to understand how crystalline materials grow, and to improve the physical contact between different materials.

In addition, we’re changing the role of organic molecules in the perovskite structure. Conventionally, the organic molecules are considered structural framework, without contributing to the electronic structure. We regard this organic molecule as both a framework and a semiconducting bridge that can manipulate the electronic structure of 2D perovskite.

Another breakthrough is enabling what are called polymeric hole-transporting materials to be used in high-efficiency devices. This type of material helps generate stable perovskite solar cells, but it sacrifices efficiency due to its poor contact with perovskite. We used the molecular engineering technique to solve the interface contact issue, a long-standing problem in this field. This strategy will pave the way for building more efficient and stable devices.

Our funding comes primarily from the Department of Energy; we also are discussing collaborations with the private sector to commercialize our technology. Through the Purdue Office of Technology Commercialization (OTC), we have filed several patents, now available for licensing.

Perovskite solar cells are developing extremely quickly these days. Within 15 years of development, their power conversion efficiency already is comparable to that of the Si-based solar cell. Although the stability of perovskite devices still lags, the research field is turning the “steering wheel” from investigating high efficiency to exploring high stability.

And based on how device efficiency of perovskite has been improved, we believe the stability of this material also will advance tremendously in the next few years. We like to compare it to organic light-emitting diodes (OLEDs) — the first OLED research papers demonstrated stability of only a few seconds, but after 30 years of effort OLED technology was commercialized with great success.

There already are many perovskite startup companies all over the world; some major solar companies additionally are investigating the possibility of perovskites. What also looks promising is combining perovskite cells with silicon solar cells to make “tandem” solar cells that can help boost the efficiency from ~26% to ~35% without a major increase in production cost.

Overall, the future appears promising. The next five to 10 years will be an exciting time for perovskite solar technology to make the next giant leap — from lab to fab.

Letian Dou, PhD

Charles Davidson Associate Professor of Chemical Engineering
Davidson School of Chemical Engineering
College of Engineering

Associate Professor of Chemistry (by courtesy)
James Tarpo Jr. and Margaret Tarpo Department of Chemistry
College of Science

Purdue University

Ke Ma, PhD
Lillian Gilbreth Postdoctoral Fellow
Davidson School of Chemical Engineering
College of Engineering
Purdue University

Jiaonan Sun, PhD
Postdoctoral Research Assistant
Davidson School of Chemical Engineering
College of Engineering
Purdue University

Related Links

Professor Letian Dou’s team discovers methods to improve stability, efficiency of perovskite solar cells

Researchers explore fabrication, optoelectric characterization of multilayered perovskites

Professor Dou receives Humboldt Research Fellowship

Postdoctoral researcher Ke Ma awarded Lillian Gilbreth Fellowship

Alternate material could cut solar cell costs by more than 75% was originally published in Purdue Engineering Review on Medium, where people are continuing the conversation by highlighting and responding to this story.

AI is taking the world by storm, popping up in every nook and cranny of human endeavor through its ability to crunch huge amounts of data and come to speedy conclusions. Materials science and engineering is no exception, as many of its next-leap advances depend on sifting through large datasets to understand material structure, properties and applications. Arun Mannodi Kanakkithodi, assistant professor in the School of Materials Engineering, shares his thoughts on the subject in a Q&A with Purdue Engineering Review.

What is AI?

Artificial intelligence (AI) is a branch of computer science that aims to replicate or simulate human intelligence in machines. The origins of AI lie in Alan Turing’s “thinking machines,” which gave way to the “Turing test” for determining whether somebody (or something) is a computer or a human. Machine learning (ML) and data science are components, or sub-fields, of AI. The former deals with the study of computer algorithms that improve automatically with experience and by the use of data, while the latter concerns extracting knowledge from structured or unstructured data using scientific methods, processes, and algorithms.

How is AI used overall?

AI and ML are now indispensable components of all facets of society and the global economy, affecting everything from banking to healthcare to food to the fundamental way modern offices and businesses operate. Examples of functions in which AI is playing a crucial role — with or without people realizing — include determining housing prices based on region and population demographics, making complicated weather forecasts based on existing conditions and historical data, screening resumes for a job opening, issuing credit cards, and discovering new pharmaceutical drugs. Not every such application is ethical or necessary, and businesses still depend on people training, executing and deploying AI/ML models to make the right calls in their specific industries. At the end of the day, AI (if used correctly) generally helps save time by providing a way to navigate highly complex problems involving tons of data and high-dimensional spaces that the human mind cannot easily visualize.

How do AI and materials science and engineering mix?

AI has been a vital part of research and education in materials science and engineering (MSE) for almost two decades. However, using data science or correlations between inputs and outputs to help understand materials behavior has been around in some form or another for centuries. In one 20th-century example, the famous Hall-Petch equation relating grain size to the strength of a material was discovered in the 1950s by analyzing a collected dataset and uncovering a simple relationship. With the advent of massive computing power, and methods ranging from Gaussian processes to neural networks to generative models, AI/ML now can be performed on enormous datasets amassed over decades. This AI/ML can speed the discovery of new materials and capabilities in the laboratory much faster than before, when brute-force Edisonian approaches — repeated trial and error — were the norm. The emerging field of materials informatics studies the structure, behavior and interactions of natural and engineered computational systems to improve the understanding, use, selection, development and discovery of materials.

Can you cite use cases in materials engineering?

AI is performing autonomous robotic experiments in the lab much faster than can be done manually; an “active learning” scheme then determines the next experiment to conduct in order to reach the best solutions in the fewest experiments. AI is generating new material structures with desired properties for a variety of applications, such as batteries and electronics. It is using large language models to navigate through historical text-based datasets in efforts to accelerate discovery of new materials. And AI is training models to accurately predict atomic forces and energies (referred to as ML-based interatomic potentials) to hasten the simulation of materials dynamics.

What about your research?

I am a computational materials scientist applying quantum mechanics-based density functional theory (DFT) simulations and AI/ML to drive materials discovery. My research group has developed some of the largest computational datasets of materials with applications in solar cells, electronics, and quantum computing. These datasets have helped train ML models to predict materials properties on demand, accelerating real-world materials discovery by several orders of magnitude. The breakthroughs I am hoping for will make it even easier to learn and perform high-throughput DFT computations using shared computing resources. Quantum computing may provide the ultimate solution in this regard, performing first-principles simulations in a fraction of the time required with classical computing. All data and associated models are available open-source via the Materials Data Facility and nanoHUB, a nanotechnology repository housed at Purdue. Our research has major consequences for semiconductor R&D, ties in well with the U.S. government’s 2022 CHIPS and Science Act as well as with the long-standing Materials Genome Initiative, and contributes to achieving the Semiconductors@Purdue goal of educating the next generation of workforce leaders in semiconductors and microelectronics.

What challenges remain?

The primary challenge is making high-quality and high-accuracy materials data readily available. Funding agencies and professional societies are emphasizing the need to make materials data more “FAIR” — findable, accessible, interoperable and reusable. However, many researchers are reluctant to share their data publicly, especially when it contains failed experiments or computations (which provide huge opportunities for learning); this leads to unnecessary repeated efforts.

What’s next for AI in materials engineering?

AI/ML in materials science is going to be of paramount importance going forward as we deal with the ever-increasing quantities of data and computing power, as well as the needs of a world trying to tackle climate change, energy crises, and a host of social and economic problems. AI in materials engineering will be transformative for renewable energy, hydrogen generation and storage, CO2 removal, and other important approaches to deal with climate change. AI will help reduce duplicated efforts of materials researchers, and will break barriers between domain experts and data scientists. My vision is that all MSE undergraduate students take courses on elementary coding/programming, data science and analytics, statistics, machine learning, and materials informatics, early on in their curriculum. No matter what the students go on to do, this knowledge will prepare them well for the future.

Arun Mannodi Kanakkithodi, PhD

Assistant Professor

Principal Investigator, Mannodi Research Group: Data-Driven Materials Design

School of Materials Engineering

College of Engineering

Purdue University

Related Links

Mannodi Research Group: Data-Driven Materials Design

Professor Mannodi receives DoE grant for research on solar cells

TMS - The Minerals, Metals & Materials Society names Professor Mannodi a 2023 Young Leader

AI/ML tutorials/webinars by Professor Mannodi for Materials Research Society

AI puts on its thinking cap for materials engineering was originally published in Purdue Engineering Review on Medium, where people are continuing the conversation by highlighting and responding to this story.

The value of AI lies in its ability to assist humans in making decisions by speeding information gathering and pattern recognition. AI outputs need to be explainable in order to be trusted and carefully considered. This requirement calls for a decision-oriented definition of explainable AI, comprised of three key elements: the decision task; the AI-generated explanation; and an often-overlooked aspect, the “hidden” human knowledge unavailable to the AI system.

The decision task is any task in which a human must choose an action (or no action).

The explanation could be any AI output, such as the model’s prediction, a visualization of key factors affecting its prediction, or a natural language justification. Importantly, the explanation must be made available to a human in some form, usually visualized on a computer screen.

The last element, human knowledge unavailable to the AI system, is a critical component of explainable AI. It means there must be something that a human knows or understands about the world or the current situation that enables the person to make a better decision than a completely automated system could make. If there is no hidden human knowledge, the decision could be completely automated, rendering AI explanations unnecessary.

A good example is medical diagnosis. The decision task is to diagnose a patient’s disease correctly. The explanation could be a list of most likely diagnoses, along with symptoms that point to each of these diagnoses. The human knowledge is the doctor’s understanding of medical conditions, and realization if a symptom does not make sense for the diagnosis. Incorporating this medical understanding, the doctor either accepts the predictions or recommendations or rejects them if they don’t align with the practitioner’s medical knowledge.

When AI goes wrong

Most people intuitively recognize that entirely automated decisions likely could go wrong, which is particularly dangerous in situations with significant real-world consequences. Ultimately, then, an explainable AI system somehow must integrate a human to enable better decisions.

The main applications of explainable AI’s ability to mitigate any potential problems caused by automated decision-making fall under the categories of robustness and social concerns.

As for robustness, one major issue with current AI systems is that they can fail in unexpected and catastrophic ways when the environment or context changes even slightly. Thus, explainable AI helps a human recognize and correct these failures to enable better decisions than those made using AI alone.

The other major issues are social matters, such as discrimination, lack of fairness, or lack of accountability. In these cases, either social norms (such as fairness) or legal factors (such as anti-discrimination laws) imply constraints on the system that can be difficult to formalize or enforce, as they inherently are social constructs.

In these cases, a human decision-maker or judge can carefully consider both the AI-generated explanation and the social understanding — which is difficult to formalize mathematically — and aid the person in making decisions that align with societal or legal values.

To trust or not to trust

Most AI methods are black boxes that are too complicated even for experts to fully understand — perhaps similar to the challenge of truly comprehending the human mind.

Without explainability, people may fall into one of two extremes when using AI systems. On the one side, without explanations, humans may never trust AI systems so decide to completely ignore them. On the other hand, also without explanations, people may begin to implicitly trust AI systems more than they should, expecting AI systems to magically understand everything about the world and speak the truth.

For low-risk situations, such as those involving writing help or creativity assistance, the first problem can be mitigated with simple explanations. For high-risk situations, like needs for medical diagnoses or bail determinations, explainability can be critical for making correct decisions, and failing to analyze in such instances could result in severe consequences, such as medical fatalities or unfair judicial verdicts.

Ultimately, explanations allow people to combine their knowledge and reasoning with the reasoning of the AI system for a better overall outcome. Relying exclusively on either the human or AI often is suboptimal in many circumstances.

It’s thus vital that we do better at systematically defining what is meant by explainability, and how to measure progress toward this goal. The key lies in answering two questions: 1. What does the human have that the AI system lacks? 2. How can we effectively combine human and AI information sources for better decisions?

I do not think we can ever fully explain AI technology, nor do I think that should be the aim — just as I expect we will never be able to completely explain the inner workings of the human mind. Even so, by explaining some aspects of how humans process information, we can better understand people’s decisions and behaviors — knowing when to trust or not trust them.

This is analogous to explainable AI. Most AI systems will always be too complex to entirely explain their behavior. However, by understanding them better, we can know their limitations and abilities.

Data shifts, uncertainty looms

Our current research focus is on explaining distribution shifts and uncertainty quantification to better explain AI.

In the first case, we are seeking to provide machine learning (ML) operators with an explanation of how two datasets differ from each other (i.e., the distribution shift), to aid in identifying potential problems in the ML pipeline.

Distribution shift occurs in ML when the data used to train a model varies significantly from the data the model encounters in real-world applications. This mismatch can lead to poor performance, as the model’s assumptions about the data no longer hold true. For example, a self-driving car trained on sunny California roads may struggle in snowy Michigan due to a change in the distribution of weather conditions.

Addressing distribution shift is crucial to building robust and reliable ML models that perform well across diverse environments. Our explanations can help an ML practitioner better understand these distribution shifts and select the appropriate course of action.

We also are investigating ways to explain an AI system prediction to an operator by quantifying the uncertainty due to missing values. In many applications, values may be absent because of privacy concerns, data errors, or measurement mistakes.

For example, in a sensor network that is monitoring a remote location, sensors may fail due to power limits or harsh weather, which would result in missing sensor values. In a security application whose goal is to protect a certain region from intrusion, a security officer would need to decide if a detected movement is a real threat (e.g., a thief or person intent on vandalism) or if the movement merely involves a wildcat moving through the area.

While an AI system could predict its best guess, missing values may significantly impair ability to predict accurately. Therefore, we aim to also give the security officer an estimate of the uncertainty due to missing values.

If there is no uncertainty, the security officer can trust the prediction and act accordingly (e.g., send someone to investigate). If there is high uncertainty, the security officer may choose to deploy a new sensor (e.g., a drone) to check further, in order to reduce the unsureness before deciding what to do. Thus, the explanation can help the officer make more optimal decisions.

Looking ahead

In the future, I envision we will gain a much better handle on what AI can do and what humans can do as the two enhance their interactions. Explainability then will be the main interface between AI and people to enable seamless collaboration.

The dichotomy of what AI and humans provide to a task is not primarily a question of feasibility but rather of cost. While it is far easier for a computer to analyze thousands of data sources in seconds, it is much simpler for humans to quickly validate the results using their understanding about the context and the world. We already are seeing this with the explosion of large language models like ChatGPT, which produces immediate results that are not necessarily truthful, while a person can easily validate such results because the model is in natural language.

My contention is that only humans can define a task or problem based on human values. In the past, people defined more narrow tasks for computers to complete. Humans are now defining increasingly broader and more complex tasks for large language models to handle.

Ultimately, the task or goal definition will be provided by the person — potentially at higher and higher levels of abstraction as AI improves its task-solving abilities.

David I. Inouye, PhD

Assistant Professor, Elmore Family School of Electrical and Computer Engineering
Probabilistic and Understandable Machine Learning Lab
Faculty Member, Institute for Control, Optimization and Networks (ICON)
College of Engineering

Faculty Member, Center for Resilient Infrastructures, Systems, and Processes (CRISP)
Faculty Member, Center for Education and Research in Information Assurance and Security (CERIAS)

Purdue University

Related Links

More information on Professor David Inouye’s research on explainable AI

Professor Inouye wins CRISP’s Rising Star Award for submission on ‘Improving resilience of AI systems via explanation’

Team including Professor Inouye awarded grant to collaborate with Army Artificial Intelligence Innovation Institute (A2I2)

Purdue Engineering Review: ‘When data shifts: Getting your ducks in a row’

Explainable AI, Explained was originally published in Purdue Engineering Review on Medium, where people are continuing the conversation by highlighting and responding to this story.

NASA made history when its Double Asteroid Redirection Test (DART) spacecraft, launched with the Light Italian CubeSat for Imaging of Asteroids (LICIACube), successfully crashed into the asteroid Dimorphos on Sept. 26, 2022. Recent findings provide new detail on how the collision changed the asteroid’s shape and orbit in the world’s first planetary defense technology demonstration. Andrea Capannolo, now Purdue assistant professor of aeronautics and astronautics, was mission analyst for the LICIACube mission while serving as a research fellow at Politecnico di Milano. He shares his experience and insights with Purdue Engineering Review.

From the beginning, I have been very proud of my contributions to the DART-LICIACube mission. The concept of a kinetic impactor to deflect asteroids was well known before the mission, but this was the first time this concept was tested on a real asteroid.

I remember the day of DART’s impact very well. I was in France, it was late at night, and the LICIACube team participated in a group call to view real-time images coming from DART during the last minutes before it hit the asteroid.

How in awe all of us were to finally see the system of the asteroid Didymos (with the smaller Dimorphos orbiting around it) and its real shape! It differed significantly from what we were expecting based on the shape models we had seen on our monitors for about four years.

This was the first hint of how unpredictable and uncertain the parameters related to these celestial bodies can be.

A few days later, we started to receive images of the impact from LICIACube. The shape of the ejected material was extremely different from the uniform cloud of dust I was expecting, with clumps and filaments expanding in all directions.

The true surprise came with publication of the first measurements of the impact and Dimorphos’ orbit around Didymos. The impact caused a reduction of about 30 minutes in Dimorphos’ orbit, instead of the predicted six to seven minutes. This difference alone proved the success of the mission, and the extreme effectiveness of the kinetic impactor concept.

DART’s impact raised our hope for the future of planetary defense.

Additionally, it made us think about how much care should be put into the preliminary design of this kind of mission.

For example, during the first stages of development, I needed to understand how close LICIACube could fly next to Dimorphos, considering the ejected particle cloud that the spacecraft needed to avoid to survive the flyby.

To set safety margins, my colleagues and I assumed a “monolithic basalt” composition of Dimorphos (as it was a single, large block of very hard material). According to empirical models, we expected this kind of material would cause the ejected particles from the impact to travel the fastest, about 300 meters per second.

The latest findings from the Italian scientific team of the LICIACube mission, published by Nature, instead show how the irregularly ejected material displayed large variations in particles’ velocity, from a few tens of meters per second (m/s) to almost 500 m/s. This means that, given the designed trajectory and distance from Dimorphos, some parts of the particles cloud could have easily reached and maybe destroyed the spacecraft, if they had developed toward the trajectory path — revelations that are fascinating and scary at the same time.

Another interesting result, covered in the Nature article, deals with the change in the spectrum of the observed ejected particles, moving from red to blue, when observing the surface particles and the inner ejected material respectively. Among various possible explanations for this phenomenon, one theory suggests that the outer material “aged” differently due to exposure to the outer space weather.

The growing value of the LICIACube data demonstrates why small spacecraft, such as CubeSats, are a fundamental element of asteroid-exploration missions. Given their light weight, lesser complexity and lower costs, we can afford these kinds of risks. We can try to fly them closer to asteroids and maximize the amount of scientific data we can obtain, while keeping larger spacecraft — the most expensive assets — at a safer distance.

I predict this trend will become a standard for this type of missions, as indicated by Hera, the next mission toward the Didymos system. The European Space Agency’s Hera is scheduled to launch in October 2024 and, by December 2026, to begin additional study of the asteroids’ properties and long-term effects of the DART impact.

As scientists continue to analyze data from the DART-LICIACube mission and build on its success, we are sharpening our ability to protect the Earth from an asteroid threat. I am thrilled to have contributed to the pioneering mission and look forward to witnessing and hopefully contributing to the next advancements that will enable asteroid deflection on a larger scale.

Andrea Capannolo, PhD

Assistant Professor

Member, Area Committee on Astrodynamics and Space Applications

School of Aeronautics and Astronautics

College of Engineering

Purdue University

Related Links

NASA Jet Propulsion Laboratory: ‘NASA Study: Asteroid’s orbit, shape changed after DART impact’

Ars Technica: ‘Close-up images of DART’s asteroid smashup reveal complex debris’

NASA: ‘NASA’s DART data validates kinetic impact as planetary defense method’

Planetary and Space Science: ‘LICIACube — The Light Italian Cubesat for Imaging of Asteroids in support of the NASA DART mission towards asteroid (65803) Didymos’

Aerogram: ‘New faculty’ profile of Andrea Capannolo

The world’s first successful deflection of an asteroid — an inside view of recent findings and… was originally published in Purdue Engineering Review on Medium, where people are continuing the conversation by highlighting and responding to this story.

Anybody who has gotten food poisoning knows how bad it is. It’s also rampant — according to the Centers for Disease Control (CDC), each year 1 in 6 Americans becomes sick from contaminated food or beverages, and 3,000 die from some foodborne illness. The U.S. Department of Agriculture (USDA) estimates that foodborne illnesses cost the nation more than $15 billion annually in medical expenses, lost business productivity, lawsuits, and compromised branding.

Time is vital in tracking down foodborne pathogens. Traditional approaches like culturing and plating are the most reliable techniques, using selective enrichment of food or environmental samples so the pathogenic cells multiply to detectable levels. Other benefits include ease of interpretation, recovery of live cells for further analysis, and minimal specialized equipment.

The drawback is how long it takes to obtain results — more than five days in some cases, which is problematic for the food industry and regulatory agencies that need to identify and control outbreaks. Products with short shelf lives also may spoil before results become available.

Portable testing is crucial. On-site tests ensure precision in critical situations, such as identifying water contamination or outbreaks of foodborne pathogens associated with processing plants, restaurants, or retail stores. The ability to conduct tests on the go ensures that results are promptly accessible and the location is geo-coded, leaving no room for doubt about the site.

Portability is especially valuable for food inspectors and border inspectors who analyze imported food or food products that are frequently misrepresented, mislabeled, or tampered with. In such circumstances, the ability to conduct tests on location can significantly enhance the accuracy of inspections and protect consumers.

An ideal platform for detecting pathogens should work for diverse food matrices, offer fast results, be user-friendly and cost-effective, and integrate with hand-held devices like cellphones. The goal is to reduce physical size by compacting all the components — easier to do with today’s newer-generation microelectronics, small lasers, power supplies, and batteries.

Several portability solutions

Our research team is focused on developing multiple portable approaches. For example, we have proposed a quartz crystal microbalance (QCM) system that combines a smartphone, an in-situ fluorescence imaging component, and a flow injection component. This system enables real-time frequency data to be received by a smartphone via Bluetooth, while the camera verifies the presence of bacteria on the quartz crystal surface via a fluorescence-tagged antibody.

We also employ a fluorescence-based, loop-mediated isothermal amplification (LAMP) assay to detect mycotoxins — harmful molecules produced by fungi that can cause health problems when consumed by humans and enter the food chain through food crops or animal feed.

Typically, mycotoxin detection is done after harvesting and processing, as concentrations can increase during these steps. If farmers could detect the fungi that produce toxins before harvesting, they could prevent cross-contamination, save resources, and improve food safety.

For example, the fungus Fusarium graminearum is a major problem for cereal crops, producing a mycotoxin called vomitoxin (also known as deoxynivalenol, or DON). We designed a low-cost, portable, microfluidic device to extract the fungal DNA from wheat samples and perform a LAMP assay. The prototype detects the fungus at concentrations relevant to U.S. limits for DON.

Combating food fraud

We also use portable devices for food authentication. While not strictly a contamination issue, food fraud often is associated with contamination or adulteration. The primary techniques we employ are laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. We focused initially on products like European Alpine-style cheeses, balsamic vinegar, coffee, spices, and vanilla to demonstrate the technology.

LIBS is a promising analytical technique that uses high-energy laser pulses to create a plasma plume at the surface of a material and measure the optical emission of the elements, ions and molecules that comprise the sample. Our current efforts aim at combining Raman and LIBS into a single, portable, hand-held device to detect food fraud, adulteration and contamination. It would not only address the mislabeling and misattribution of product origin but also detect the presence of harmful substances, including pesticides, metals and microplastics.

Collaboration among experts in fields such as food science, chemistry, microbiology, statistics, data science, and engineering is essential for this interdisciplinary research. Our data science expertise has allowed us to develop predictive models to identify potential food fraud. Our studies also involve analytical chemistry techniques, molecular biology methods, and optoelectronic detection tools. In addition, we have developed immunoassays and biosensors that can detect specific toxins and pathogens in food matrices or cultures.

Making the smartphone an analytical tool

We want to transform the smartphone into an analytical instrument suitable for many use cases. So far, we’ve reported smartphone-based spectrometers, colorimetric devices, lateral-flow analysis devices, and bioluminescence detection — examples of numerous potential applications that smartphone-based instruments can provide. While these measurement modalities have been tested in food-related research to date, they are perfectly applicable to any life science area involving analysis of bacteria, viruses and/or toxins.

The majority of our research is sponsored by USDA Agricultural Research Service (ARS) programs; we also get funding from the Center of Food Safety Engineering at Purdue. In addition, we receive assistance from companies through their instruments and devices; for example, SciAps Inc. let us access its LIBS system until we could buy our own. We’re also working with the Purdue Research Foundation and seeking patents for several technologies.

We hope these ideas will be developed further by partnering companies. Our goal is to implement these technologies on the upstream side with producers and manufacturers so contamination can be detected earlier and dealt with before causing too many problems downstream in society.

J. Paul Robinson, PhD

Distinguished Professor of Cytometry and The SVM Professor of Cytomics, Department of Basic Medical Sciences, College of Veterinary Medicine

Professor of Biomedical Engineering, Weldon School of Biomedical Engineering, College of Engineering

Director, Purdue University Cytometry Laboratories (PUCL)

Purdue University

Fellow, National Academy of Inventors (NAI)

Fellow, American Association for the Advancement of Science (AAAS)

Fellow, American Institute for Medical and Biological Engineering

Honorary Fellow, Royal Microscopical Society

Euiwon Bae, PhD

Senior Research Scientist/Continuing Lecturer

School of Mechanical Engineering, College of Engineering, Purdue University

Bartek Rajwa, PhD

Research Professor of Computational Life Sciences

Bindley Bioscience Center, Purdue University

Kennedy Okeyo, PhD

Visiting Scholar, Purdue University

Xi Wu, Sharath Iyengar, Sungho Shin, Iyll-Joon Doh

Postdoctoral Research Scientists

Department of Basic Medical Sciences

College of Veterinary Medicine, Purdue University

Related Links

New technique ferrets out food fraud

The Washington Post: ‘Stopping knockoff knockwurst and phony fromage: How the food industry is stepping up anti-fraud technology’

Foods: ‘Rapid food authentication using a portable laser-induced breakdown spectroscopy system’

New technology for pathogen detection driven by lasers

Purdue University Cytometry Laboratories (PUCL)

Germs get around — testing must get mobile, too was originally published in Purdue Engineering Review on Medium, where people are continuing the conversation by highlighting and responding to this story.

Humankind has been drying food to preserve it since prehistoric times. Food dehydration removes the moisture from food to inhibit the growth of bacteria and mold in order to increase its shelf life. Food typically is dehydrated by using heated air in an electric or gas-fired oven; drying in a solar dryer, which concentrates the heat; employing a desiccant; leaving a product to dry in the open air; or sun drying in the open air.

The use of heated air is the most common method in developed countries and can be quite expensive. Globally, the majority of farmers today use open-air sun drying to preserve the crops they grow. Sun drying is fairly inexpensive (zero energy input) and carbon neutral. However, it has a daunting limitation in a food-insecure world: It can only work effectively during the daytime when sunshine is plentiful and it doesn’t rain.

Technologies for drying horticultural produce (specialty crops) at a scale for small and midsize farmers are some of the least developed in the industry. We’re creating such technology for a multipurpose solar dryer. As its name suggests, a multipurpose solar dryer can dry several types of crops, and is designed for high-moisture horticultural produce, including fruits, vegetables, spices and medicinal plants.

It works by capturing solar energy in the form of heated air and transferring the heated air to a drying chamber where the crops are presented. Rather than using open-air sun drying, which can result in foods being contaminated with things like dust, insects, birds and livestock, the heated chamber provides a more hygienic drying environment.

The innovation originated when I worked with USAID’s Feed the Future Innovation Lab for Food Processing and Post-Harvest Handling. The goal was to develop a solar dryer for maize (corn) suitable for smallholder farmers in Senegal and Kenya. Rather than design a crop dryer to focus on just drying maize, I pivoted to designing a multipurpose crop dryer. As we interacted with farmers and stakeholders in the region about the crop dryer for maize, they said they would like a dryer that also could dry vegetables, fruits, root crops, and so forth.

USAID encourages technologies developed in the lab to be transferred to farmers in the field via commercialization. In keeping with that aim, I worked with the Purdue Office of Technology Commercialization (OTC) to patent two different solar dryers and launch a company, JUA Technologies International, Inc. (JTI), to sell them.

The smaller, portable dryer is manufactured and sold by JTI under the tradename Dehytray™; the larger unit is still under R&D by JTI. Our goal is to make both dryers hygienic and efficient drying platforms to meet the needs of home, small and midsize farms and businesses, which are key to local food growers and urban agriculturalists.

In emerging countries, growers can experience postharvest losses of as much as 50% of vitally important produce. Multipurpose solar dryers can not only increase growers’ food security but also enable the growers to raise revenue by more successfully processing their harvests.

Solar is critical. With access to affordable electricity still in short supply, and out of reach for some 2 billion people, the use of electric dehydrators is out of the question. The use of fossil fuel (such as natural gas and fuel oil) is simply out of reach for most rural dwellers in developing countries. Due to climate change, the current global trend is to pursue energy sources such as renewables that decarbonize society, which makes solar energy a viable option.

Solar energy, on the other hand, is abundant, but it unfortunately has not been harnessed effectively for drying crops. While an enormous amount of research literature on solar drying has been generated, very few designs have been commercialized at scale. In fact, it is easier to buy a Tesla car than a solar dryer.

Solar dryers capable of drying fruits and vegetables that are high in crucial micronutrients are essential to meet global food demand and will have a net positive impact on the environment. JTI is working to make its solar dehydrators accessible to anyone around the world who needs them.

Klein E. Ileleji, PhD

Professor and Extension Engineer

School of Agricultural and Biological Engineering

College of Engineering

Purdue University

Co-founder and CEO, JUA Technologies International, Inc. (JTI)

Related Links

USDA provides $600,000 grant to develop solar-powered crop dehydrator

SBA honors Professor Klein Ileleji for business venture

Video: Using the Dehytray

The Dehytray reviewed by the IFT Food Science for Relief and Development

Purdue Engineering Review: Take 6 with Klein Ileleji

Solar powers novel dryer for preserving crops was originally published in Purdue Engineering Review on Medium, where people are continuing the conversation by highlighting and responding to this story.

Most people think of research and innovation as the precursor to large-scale production, and that often is the case. A breakthrough idea, through follow-on refinement, can lead to a well-received product that warrants mass manufacturing.

But it’s also true that production scaling — the ability to efficiently manufacture large quantities of a product or component — itself leads to research and innovation. This is especially important to consider as the U.S. has outsourced much of its scaled production over the past 40 years — which, tellingly, has led to a surge of research and innovation in those countries that have happily welcomed the opportunity to produce at scale.

Production at scale is a continuous learning opportunity. When you manufacture large quantities of a good, you invariably get new ideas as you debug and optimize the manufacturing process steps for maximum efficiency. These light-bulb moments can lead to substantial value-added improvements to a product or to entirely new products.

The accessibility of preexisting scaled manufacturing capacity as an economic given is also important to entrepreneurs in that they can envision a way to capitalize on their research and innovation by manufacturing in the quantities necessary to make their early-stage startup investment in time and money pay off.

Seeing a quick path to commercial success through scaled manufacturing is crucial to venture investors and other sources of capital. As the U.S. rebuilds its manufacturing capacity and production processes, reversing some of the excesses of its lengthy overreliance on outsourcing, investors of capital will view more favorably entrepreneurs who come to them with new product ideas, as the investors can see proven ways to realize their return on investment through commercialization at scale. More manufacturing output means more value added across the nation’s economy — and more profits that widen the pool of capital for further investment.

A nationwide network of manufacturing at scale also helps create and nurture a new generation of innovators. These “makers” progress from their prior roles in the value chain as engineers and floor workers to entrepreneurs, as their experience and natural curiosity enable them to see avenues for product innovation and commercialization.

Additive manufacturing (aka 3D printing) plays a significant role here. It already has led to an enormous community of pioneering makers across the country, each toying in prototyping and small-lot production with their own bright ideas, many of which could come to fruition more easily in a country with scaled manufacturing capabilities as part of its newly refreshed DNA.

Also critical to scaled manufacturing is the digitalization of business. The ability to quickly and easily transfer design models and manufacturing process instructions from one production node in the value chain to another enables manufacturing-at-scale facilities to accept the various inputs from researchers and innovators and turn that code into humming production lines. Digitalization and the free flow of data also mean manufacturing at scale can either be centralized at large enterprises or operate in a distributed model across the nation in a digitally connected value chain. Digitally grounded applications like manufacturing execution systems and operations management systems provide the means to continuously collect and analyze data to make manufacturing at scale ever more efficient and profitable.

And if the innovation in manufacturing is based on large sets of data or created through digital manufacturing innovation unleashing the power of ever-more-powerful artificial intelligence, it becomes even more important to scale up manufacturing in an environment with access to all data generated throughout the production process and opportunities to use the data to develop new digital manufacturing software.

The societal benefits are immeasurable. Production-at-scale capacities across the nation mean more jobs, more opportunities to draw the disadvantaged into the workforce pool, and economically strengthened communities. And it leads to resiliency in the face of inevitable supply chain crises and shocks. It buttresses security and national defense, by way of the always-on capability to produce to need. The multiplier effects are enormous, cutting across all sectors of the economy, from supporting suppliers to restaurant owners to house builders, all providing necessary goods and services to nourish vast manufacturing networks.

It also helps create a deeper knowledge base of manufacturing across the country. Consultancies, academia, and manufacturing-focused organizations like Purdue’s eXcellence in Manufacturing and Operations (XMO) initiative that already have planted deep roots will see those roots sprout as they tinker with, refine and disseminate cloud-based, open-source manufacturing best practices and processes to small, midsize and large enterprises — lending vital support to ongoing generations of researchers and innovators eager for ways to optimize their businesses.

The ability to manufacture at scale has been fundamental for this nation since its inception. The founding fathers, most notably Alexander Hamilton in his 1791 Report on Manufactures, realized that, as strong as the country was through agriculture and artisans, the ability to mass-produce everything from farm implements to textile products to defense arms would ensure the fledgling nation’s independence by reducing its reliance on imports.

Production at scale not only satisfies a nation’s need for essential goods, resiliency and a strong national defense. It also leads to a virtuous, self-repeating cycle of production improvements and research innovation in a sustainable advance toward a brighter and more inclusive future.

Stephan Biller is the Harold T. Amrine Distinguished Professor in the School of Industrial Engineering and the Mitchell E. Daniels, Jr. School of Business at Purdue University. He is a co-founder and co-chair of Purdue’s national eXcellence in Manufacturing and Operations initiative and leads Purdue’s Dauch Center for Management of Manufacturing Enterprises. Previously, he served as founder and CEO of Advanced Manufacturing International, Vice President of Product Management for AI Applications & Watson IoT at IBM, Chief Manufacturing Scientist & Manufacturing Technology Director at General Electric, and Tech Fellow & Global Group Manager for Manufacturing Systems at General Motors. He is an IEEE Fellow and an elected member of the National Academy of Engineering.

Related Links

eXcellence in Manufacturing and Operations Purdue Engineering Initiative (XMO PEI)

Dauch Center draws record attendance at conference on digital transformation in manufacturing and supply chain

Purdue Engineering Review: ‘Supply chain resilience: Think of the U.S. as a ship at sea’

Purdue Engineering Review: ‘Digital twins: Smart manufacturing’s DNA for a bright future’

Manufacturing and Materials Research Laboratories (MMRL) to bolster U.S. manufacturing

Production scaling drives research and innovation was originally published in Purdue Engineering Review on Medium, where people are continuing the conversation by highlighting and responding to this story.