After spending years in academia studying carbon nanomaterials and wearable biosensors, materials scientist Hongbian Li transitioned into industry to work closer to real-world applications. Now a Scientific Principal at Henkel, the company behind major brands such as Loctite and Schwarzkopf, Li leads research on adhesives for advanced semiconductor packaging. Speaking with Advanced Science News, she reflects on the evolving relationship between academia, industry, and artificial intelligence.
How does the pace and structure of research differ between academia and industry, particularly in a company like Henkel?
In industry, the research direction is usually much clearer. We typically start with a specific gap between customer needs and what the market can currently offer, and our goal is to develop materials that can bridge that gap. Since the market evolves very quickly, the pace of research in industry is also faster in order to keep up with those changes. Large companies like Henkel also have strong technical knowledge and accumulated expertise, but much of that effort is closely aligned with market demands.
In academia, the focus is more on exploring new scientific areas and building fundamental knowledge. It offers greater freedom, but there’s also more uncertainty in the outcomes. In addition, it usually takes longer for academic research to be translated into real-world products.
Much of your academic research focused on health-monitoring devices. What initially drew you to that field?
My academic research started in carbon nanomaterial synthesis, including carbon nanotubes and graphene. We found that these materials were ideal for biosensing due to their good conductivity, high surface area, and chemical stability, so we started using them as sensing layers for wearable electronics, including strain sensors for motion tracking or pressure sensors for pulse tracking. Later, we found that these materials had good biocompatibility when properly treated — they can even be implanted into the brain to monitor neural activity without hurting the surrounding neurons.
What do you see as the biggest remaining challenges in translating health-monitoring research into widely adopted technologies?
In my opinion, the biggest challenge lies in the integration of these research-stage health-monitoring sensors with other units — including signal processing units, communication units, data storage units, and power units — into wearable products that can provide long-term, stable signals in practical applications.
How are advances in materials science shaping the future of wearable or personalized healthcare technologies?
Materials are the foundation for wearable electronics. Developing new materials with novel properties can significantly advance current technologies and open up new directions for wearable and personalized devices.
For example, ultralow-modulus yet robust materials can create seamless neural interfaces, enabling stable, long-term neural recordings. In another case, biocompatible, self-adhesive materials with good electrochemical performance can be directly painted or printed onto the scalp — like an “electronic tattoo” — allowing for stable, long-term brain-signal monitoring in an imperceptible way.
As a reviewer for several major journals, what makes a manuscript stand out during peer review?
The novelty of the work is the most important. High-quality figures and writing are also very important.
Do you think the peer-review system is changing, particularly with increasing publication pressure and AI-assisted writing tools?
Yes, I do think the peer-review system is changing. With increasing publication pressure, reviewers are getting more requests. I personally receive several invitations from different journals in the same week, but I can usually only accept one or two to finish them on time. This can make it harder for editors to find available reviewers.
AI-assisted writing tools are definitely helping improve the readability of manuscripts, especially for language polishing. But sometimes the writing feels a bit too polished, and the paper can lose the straightforward, scientific tone you’d expect.
There are also some concerns about misuse. For example, I heard about a professor who received a rejection letter for a manuscript he never submitted — and didn’t even recognize. Cases like this suggest that AI tools might be misused in ways that create extra work for editors, especially when it comes to verifying where a manuscript actually comes from.
What does your current work as a Scientific Principal at Henkel involve, and how does it differ from your earlier academic research?
As a Scientific Principal, I lead a team of scientists working on adhesives for advanced semiconductor packaging. Compared with academia, the research direction is usually much clearer and more time-driven. We’re really focused on solving specific problems for customers and delivering practical solutions.
By contrast, when I do academic research, it’s more exploratory. It’s about looking into new areas and bringing in new scientific ideas or technologies that could eventually benefit the field.
Has your experience as a reviewer influenced the way you approach your own research and writing?
Yes, definitely. Reading a high-quality manuscript can give me new ideas and inspiration for my own research. It also helps me learn how to structure my papers more clearly and present my work in a more organized way.
Do you think academia and industry are becoming more interconnected? In what ways?
Yes, I think academia and industry are more interconnected than ever. With the rapid development of AI, knowledge in industry is evolving much faster, which also drives the need for new research and scientific understanding. Because of that, collaboration between academia and industry has become much more common — through joint programs, research centers, and industry-sponsored projects that support product development.
When I was working at the National Center for Nanoscience and Technology in Beijing, for example, there were regular programs from industry looking for collaboration to help solve specific technical problems. On the other hand, many professors are now starting their own companies to translate their research into real products — and some of them have been very successful.
What advice would you give to early-career researchers navigating today’s scientific landscape?
For one, choose a research area that you’re very interested in. When you truly enjoy the field, it’s easier to stay motivated and appreciate the exciting discoveries — even after going through many failures before finding the real “diamond.”
Second, collaboration is very important. Knowledge today is highly interconnected, so it’s hard to achieve big results alone. You need a team of people with diverse backgrounds and, by working together, you can leverage everyone’s strengths to accomplish larger goals.