Field Choice
Zhongguo Zhang
Wei Su
Yan Yan
Hui Cao
Kailiang Ren
Choosing a project is usually not a single turning point, but the product of many factors converging over time.
Several professors talked about how their undergraduate and graduate courses sparked their interest. For example, early exposure to chemical engineering, electronic materials, or resource-oriented process design gave them the ideas they needed for their research, and graduate studies also opened doors to subfields like dielectrics, thin-film capacitors, or bioprocess engineering. For example, Professor Cao enjoyed working with enzymes and microbes throughout his graduate studies, which helped him decide his research direction.
At the same time, personal experiences are also very important. Professor Su said that his concern for the environment goes back to when he was a student and smelled the industrial pollutants near campus every day. These sensory reminders turned into his interest in strategies to remove desulfurization and low-emission pathways.
Sometimes, policies can also guide one’s research. When the country made plans for biomanufacturing, Professor Zhang changed the focus of his research to match the long-term problems that the country needed to solve.
To put it simply, these scientists didn't just happen to end up in their fields of research. They combined what they studied with real-world difficulties until they identified a direction that made sense and was possible. It is important for us to pay attention to real-world challenges and find things that make us curious. When these factors align, I believe this research field can become your lasting home.
Research Spirit & Challenges
Zhongguo Zhang
Wei Su
Yan Yan
Hui Cao
Kailiang Ren
The ideas conveyed by these scientists are mostly the same: perseverance, breadth, the daring to explore new areas, and consideration of practical application.
It normally takes a long time to come up with useful ideas or advancements, and being able to persist during this time means being able to handle failure without losing desire. Breadth also provides background for research, as Professor Ren said that in the domains of piezoelectricity or materials chemistry, researchers can only make real progress when they can think about physics, chemistry, and materials science all at once. To be brave enough to explore new frontiers, you have to be willing to fail. This isn't because the ideas themselves are weak, but because researching new areas is hard and often leads to diversions and setbacks. The hardest part is figuring out how to use the results in real life. There are many initiatives that become stuck because they aren't economically feasible, stable, or easy to run, even when they are technically sound. Cost is a constant problem: if a step is energy or labor intensive, or needs a specific investment, it will be hard for actual adoption, no matter how ingenious the mechanism is. Most of the time, breakthroughs don't come from improving theories, but occur from improving traditional operations or changing methods to reduce the cost.
In summary, the research spirit is a combination of the ability to keep going even when encountering failure and finding practical solutions. The scientists did not sugarcoat the difficulties they encountered; instead, they talked about how they dealt with each problem as it came up. This is exactly the kind of spirit that makes science last.
Signature Work & Translation
Zhongguo Zhang
Hui Cao
Kailiang Ren
Professor Zhang worked for twenty years on membrane technology, moving it from research papers and patents to use in factories and industry standards. Professor Yan sees mining by-products as a new resource. By changing techniques to avoid energy-intensive high-temperature steps that are common, he has isolated silicon and aluminum at a lower cost and added them to existing industrial operations. Professor Cao solved the old problem of how to make soft materials stronger and more flexible at the same time by building several interpenetrating networks. This has opened up new possibilities for tissue engineering now and for alternative protein scaffolds in the future. Lastly, Professor Ren's piezoelectric materials and low-power sensors made their way into consumer electronics and wearable gadgets, also introducing a clear possibility toward self-powered implants.
The difficulties that come with these triumphs remind us that in scientific research, performance on a plot is not enough. We need to deal with the problems that come up in industrial manufacture and the problems that come up with long-term use, such as durability and maintenance. Cost control is something to think about from the start, which is why teams who start doing research on technology costs and reliability testing early on tend to progress the fastest. This shows that the best scientific judgments are made at the architectural level, when researchers decide what to integrate, what to throw away, and how to make research results fit with real world limits.
AI in Research
Yan Yan
Hui Cao
Wei Su
Kailiang Ren
Artificial intelligence has a lot of uses in scientific study. Professor Su employs extensive language models to accelerate literature review and produce summaries of recent advancements, thereby allocating more time for experimentation and design. But he is careful about letting existing models take over the main job of interpreting data or figuring out how things work, as he sees these tools as helpful aides rather than trustworthy analysts. Professor Yan thinks that AI is the future for industrial automation. Due to safety, labor, and cost concerns, robot technology for factory patrols and anomaly detection has been added to the plans for the near future.
In the domains of biological and material design, computation has been integrated with experiments. In Professor Cao's study, optimizing metabolic pathways is no longer based only on theory and intuitions, but now involves searches for flux distributions under energy and toxicity constraints. The redesign of proteins and enzymes is increasingly using structural prediction and directed evolution together to make the experimental search space smaller. Professor Ren's team is also utilizing machine learning to extract gait traits and physiological rhythms from big, noisy datasets in the fields of electronic materials and health sensing. They are then employing algorithms to make apps for chronic disease monitoring and elderly care.
This points to the same idea: AI will not be involved in decision making. People still have to decide whether to relate datasets to mechanisms or mechanisms to restrictions since AI still has trouble with large-scale causal reasoning. The current strength of artificial intelligence is its capacity to read, connect, and suggest candidates quickly on a large scale. Therefore, one practical use of AI is to combine it with good data and back up its results with experiments.
Beyond Short-Termism
Yan Yan
Wei Su
Hui Cao
The Professors frankly said that researchers' current motivations are biased. The current research system frequently favors novel signals, including new materials and trending subjects, due to their correlation with citation metrics and rapid outcomes. But in many sectors of engineering, the most useful innovations are frequently the most boring ones. These changes don't often make the news, but when they are used on a wide basis, they can make a big difference.
The second distortion is that groups tend to gather together in hot fields. When a lot of money and attention go into a specific topic, it becomes easier to get in, more papers are written, but real breakthroughs may be scarce. Professors here say that people should maintain independent thinking and patience, choose a path that fits their skills and really serves social requirements, and then keep doing research instead of constantly changing directions to follow trends. They believe that the evaluation of research should take into account how hard it is to put into practice and how useful the system is. This means considering cost, operability, stability, and standardization along with innovation. It also involves rewarding to structures and standards that a lot of factories or clinics can use, not just a few prototypes that are written about in papers. This doesn't mean that high-risk, high-reward exploration isn't important, but that it should be balanced with the steady advancement of industry. A better rating system would make research in less popular areas more visible and valuable.
The professors all agree that science should make long-term choices for society. Researchers would pursue the signals of the times instead of building solutions if the assessment system favors short-term surface benefits over long-term usefulness. Only by aligning incentives with impact through stressing translational work and system integration, researchers can be attracted to research that endures after the paper cycle closes.