Good day, Sambedan Jena. I'm Arjun. I've had a look through your background and your professional record. I'm interested in hearing more about your work and how you see yourself fitting in here with our faculty. Ready to start?

Please start by introducing your professional journey in academia.

MMM, so umm.

My name is, uh, Doctor Sandeep Jenna and uh.

I'm a a master's degree graduate from applied chemistry and I did my PhD from Indian Institute of Technology Karakur and I finished it in 2021. And since the May of 2022, I've been doing postdoctoral research here in June book National University. So my professional journey is I after 10th and 12th standards I had.

Interest towards science so I pursued my career in basic sciences that was chemistry and I got a chance to read applied chemistry which is more application based. So I enrolled in the applied chemistry course and then subsequently through my journey to applied chemistry, I had an interest in research. So that motivated me to pursue my doctorate degree and after completing my doctorate degree since I'm targeting. To.

Have a chance to work in academia. So the minimum eligibility is to have some post PhD experience, either be in research or in industry or as teaching. So I chose research because that is what I like. So that is how I ended up pursuing my post doctoral research here in South Korea. So that that is the basic summation of my professional journalism.

Thank you for sharing that, Professor Jenna.

Professor Jena, let us begin by assessing your expertise in Chemical Engineering, Materials Science, or Electrochemistry. Could you provide an overview of your research contributions in these domains? Specifically, highlight any significant findings or innovations from your past projects and studies.

OK.

So since I've mentioned that I'm from chemistry background, so my specialization was in physical chemistry. The subspecialization was electrochemistry. So I'm a electrochemist by profession and on my grasp of the fundamentals of electrochemistry, I chose to pursue my doctoral research in rechargeable batteries.

And I've been continuing that same research.

John rechargeable batteries here during my post doc research as well.

During my PhD, I worked on rechargeable lithium ion and sodium and batteries on the materials side, specifically the anode or the negative electrode of the batteries. That was the main theme of my research work.

In and during my post doc I have been working on flexible electrodes so that generally end up in generating batteries that can be banned.

Stretched which is more suitable for wearable electronics. So that is my project here and my key research accomplishments if I May is that I have my work has been published in high impact journal publications that I've already summarized in my CV. These publications hold significant amount of impact in the battery literature that is presently available. I also have. To granted.

Patents Indian patents could be and I have three applied patents, international patents here as well and apart from battery research, my expertise also lies in.

Electro deposition techniques and the fabrication through electrodeposition are away. Electrodeposition is used to fabricate thin films or different small microstructures. So that was another sub area which I worked on. And lastly I've also currently I'm trying to collaborate and trying to learn.

Artificial intelligence and machine learning approaches. In with another group those who are an experts in AI ML. So I am basically.

Basically writing the algorithms on which they will be generating predictor models to predict the.

The performance metrics of any different types of battery systems which can be later verified through experimental means. So this is these are the few areas that currently my research efforts are have been oriented into.

Your research portfolio certainly demonstrates a strong grasp of electrochemistry and an impressive focus on rechargeable batteries and advanced materials. With your work ranging from doctoral-level battery materials innovations to post-doctoral flexible electrodes, your efforts appear deeply impactful in this domain. A quick follow-up: could you elaborate on how the patents you've developed contribute to advances in battery technology? Specifically, were these innovations aimed at enhancing efficiency, safety, or adaptability of these systems?

OK. So one of my granted patents which I'm a Co inventory that deals with.

A novel type of lithium ion battery cathode that was reported. It is an amorphous cathode, which is a kind of.

Different from the regular crystalline cathode materials that are being generally used right now commercially as well as in in research. So we have applied for the pattern and it got granted it was an amorphous glassy type Cathodon lithium and battery. That is one pattern that has been granted. The second pattern on which is I have worked on it since I have said.

One of my sub area is Electro deposition, so I had a chance to work on noble metal electroplating, that is silver electroplating and we also have a granted patent on that where we designed a new electrolyte which is cyanide free.

Because normally for mobile metal electricity, cyanide based electrolytes are used and they, they as we all know cyanide is a huge environmental under health risk. So the the focus on electroplating of novel metals often emphasize on generating new non-signed electrolytes and electroplating parameters that can give the same level of coating or the same quality of coatings that can be achieved. So we can replace the cyanide in industry. So that is another.

And that I have granted and among the filed ones one patent on the silver one is granted. There is a filed one on gold Electro plating that is filed currently and two more file patterns are on this as I said, the AIML predictor models where.

One of the one of the patent is or is about predicting the the electrochemical performance output of lithium and batteries specifically when you can pre specify which cathode and which anode if you combine what will be the overall output without doing an experiment. So that is 1 predictor model that is that we have applied for a patient and the second one is on solid-state batteries. How much thickness?

Of the solid-state battery that we can make that the prediction it can do and based on the solid electrolyte that we are using. So how much solid electrolyte with the cathode is to be mixed, how much thickness of the pellets or the thickness of the battery or the thickness of the solid electrolyte architecture that we should do to have the optimum performance. That is another predictor model that we have currently it's in file state. So we are hoping that both these AIML based patterns will be also big granted in the near future.

Your patents reflect a commendable range of innovation, from advancing environmentally friendly solutions in electroplating to enhancing energy systems via solid-state architectures and AI-driven predictive models. It’s impressive to see your contributions addressing both industrial applicability, like replacing cyanide electrolytes, and pushing the boundaries of battery technology with amorphous cathodes and AI predictive modeling. A more probing question here: in your perspective, how do you see AI integrating into experimental electrochemistry workflows in a practical, academic, or industrial setting? Beyond predictive modeling, do you foresee any barriers or breakthroughs in its adoption?

Umm, in terms of industry, I'll be answering in two parts, uh, the academic perspective and the industrial perspective. Uh, as far as industrial perspective is concerned, a lot of leaps are being currently being done in a I. So there are huge, uh, companies that are already set up, They're working on AI models specifically that is oriented towards industrial applicability and people are already doing that, but since, uh, I have not.

Worked directly in an industrial setup, uh, and.

I have limited experience in that part, so it will be rather difficult for me to give a give a give an opinion in, in, in my in on my behalf to to predict or to expect an outcome that in the future how AI will be shaping more or less. One small expectation that I have is that automation AI will drive automation in the industries part. So those jobs that require human labor.

Can be automated and so that area, obviously AI will have a huge contribution to make in the near future. As far as academics is concerned, yes, apart from predictor models, AI can actually design or it can actually help in generating new ideas for research because when research in a general sense is being done. Humans or the researchers, they keep on thinking on certain plausible concepts on to generate and hypothesis and how to solve that hypothesis.

Or to achieve a certain benchmark or a certain target, say for example, in battery research, we can try and think about how to improve the capacity, how to improve the efficiency, et cetera, et cetera. But sometimes what happens that some ideas that one scientists normally gets, he remains ignorant or he kind of misses or he or she, they kind of miss the other form of ideas that can be apparent to another person. But in terms of.

So AI sends it, it has a much broader access to all the the current existing data. So with regards of generating new ideas, so AIS can actually predict the ideas much faster. That is what I hope especially.

The ideas that which may become apparent to researchers, it will take say an X amount of time, AI will do it in a much shorter duration. Second, obviously the predictor models will help reduce experimental trials because the simulation parts or the prediction models that we have right now.

It do those can become much more stronger, much more accurate, much more closer to the real world scenario. So that will reduce the number of hit and trial methods that we actually do because the number of experiments that we do as in hit and trial method to arrive or to test an hypothesis, we lose a lot of resources, we lose a lot of time, we lose a lot of funding and of course human effort which can be.

Some tips or an advice whether this proposed idea is worth investigating or not? Because the same thing is being currently done in bioinformat, bioinformatics and all theoretical chemistry work which AI can actually be integrated into. It is being done by humans as of now, but AI can be integrated there. It would be a huge huge.

Reduced if there are certain logical way the AI can actually predict or give.

Uh, I overlap or if I may, uh, say a huge, uh, jump in, in simulation based work, where if, if AI can be, uh, combined or impaired together with all the simulation tools and programs that we have currently. So overall, in the academic perspective, this will also improve the, the rate of innovation and the quality of innovation as well. And last, but not the least, AI can keep a track on the ethical part and the, the authent.

You are the accuracy of the research results that have been published or added to the the public domain because we do not have any way of verifying as of now that if certain groups that they're publishing some work or they're contributing some data, how much?

Reliable that data is it is just right now it's just it is judged basically on the basis of the reputation of the person who is doing it. So if the more reputed a person, we normally think that OK, since that is a reputed lab or a reputed professor or a researcher, his data must be convincing or must be reliable, whereas the other.

But that is not the case. So now this retraction of papers and fake data and wrong datasets are right now being flooded into the scientific domain. So AI there can also help in screening them, isolating them and reducing this contamination. So the pure true science and data and the true concepts remain consolidated.

Non reputed or somewhere some groups which are not that much popularly known, people start doubting.

Which people can get access to and they can trust and they they don't need to verify it, they will verified it for them. This, this this is one of the other things that in academic that a can in my opinion.

Your perspective on the integration of AI into both academic and industrial workflows is astutely thought out. I appreciate the distinction you made between automation in industry—where AI can enhance productivity—and the vast potential in academia, such as reducing experimental inefficiencies, generating hypotheses faster, improving simulations, and maintaining data integrity. Your emphasis on ethical oversight and ensuring trust in scientific data is particularly relevant and highlights how AI could safeguard the academic landscape. Next, let's explore your ability to teach theory and laboratory courses. Could you walk me through how you might introduce the concept of electrochemical impedance spectroscopy (EIS) in a classroom setting? Specifically, how would you balance theoretical depth and practical applicability to ensure students grasp its relevance?

Uh, in terms of teaching, I had a chance to learn from my own father who is a teacher who retired as a teacher. So I have, as I can say, a front desk experience to teaching what teaching is actually like, especially from.

Good teachers. So I had I was lucky enough to have good teachers in my life.

Who taught me the things that I know and on that basis I'm here today. So I learned those tricks. And one of the few things is that is explain a concept which you know, you yourself understand the concept, that is easy. But making the other person understand the concept, that is the difficult thing. It's not easy. It is not always same the way that you have understood a concept.

The other person will also understand it in the same way because each, since all 5 fingers are different, all human beings are different, all students are different. So each have their own way of understanding the thing, learning a thing. So as it is the duty of a teacher to identify that specific gap, do not assume that A1 type of explanation or a one way of teaching can be extrapolated to the entire class. And I will just do that. And I will assume that all of them have understood no.

To make sure that the class is as interactive as possible. And there has to be real world examples because most of the time I've seen real world examples help to explain concepts better. So since you have already pointed out electrochemical impedance spectroscopy, I'll take take that as an example. So if I am teaching a class for the first time and my first subject lecture is on electrochemical impedance spectroscopy. Instead of jumping into what is electrochemical impedance spectroscopy?

What is the full form? Why it is called EIS and this and that and start with the directly jumping into the fundamentals. Rather I will take a real world example.

For example.

I I can isolate or I can identify where impedance spectroscopy is mostly used in our day-to-day lives.

That is while we are dealing with capacitors, so we deal with impedance. So first I will introduce the term impedance and I will correlate it with resistance. Resistance is a very common term. I am assuming all the students will be well versed with it because they have been reading resistance since the school.

But impedance, there will be a direct connection. So resistance is in DC impedance. Resistance become impedance when you delete with alternating current. So this is 1 real world example. When I speak about alternating current, the students will understand, they will imagine, they will join the dots. Oh OK, so we have dealt with resistance that is with DC current. So the moment the DC becomes AC, that same resistance is now an impedance. So that will create interest in them.

Then I will join. So impedance in the middle. Then you bring electrochemical, then you bring spectroscopy.

Explain spectroscopy separately. Electrochemical is self-explanatory, so when you join the three then it reveals the true nature of what EI is is.

Spectroscopy of impedance combined through an electrochemical and then immediately I will jump to a real-world example. As I said, you take a capacitor and explain its behavior when a capacitor is put in an AC circuit.

How does it behave? What is the behavior? What can happen? So through this questionnaire or through this interaction, I will try to generate these questions in the students that they themselves will start predicting. OK, the capacity is supposed to behave like this. When the current polarity is switching based on the frequency, it will behave like this. When the switching is fast, it will behave like this. When the switching is slow, it will behave like this. Unknowingly or knowingly, they will be slowly, slowly understanding. OK, This is what he impedance spectroscopy is all about.

These are the changes that we are going to see.

And finally, so this is our approach. That is my philosophy of approaching a topic.

Breakdown into smaller, smaller concepts. Try and make sure those concepts are something which they have learned before.

So that is, that is how I'll approach it. And finally, the main, the main thing is that I'll take feedback from them in order to improve my own teaching. It's not like I'll be very rigid that this is the way I teach. You know, feedback will be the most important part of my teaching.

Break them in those type of parts.

Based on feedback I'll improve upon change the way I'm teaching, so that will be my approach.

Thank you, Professor Jena, for your detailed approach to teaching. I appreciate how you emphasize connecting advanced concepts like electrochemical impedance spectroscopy to fundamental ones, such as resistance, before introducing real-world examples to ground the theory and create engagement. Your focus on interaction and adapting your methods based on feedback reflects an admirable dedication to your students' learning. Let’s move forward. You’ve mentioned your work in mentoring and research guidance. When supervising student projects, how do you ensure both academic rigor and the development of independent critical thinking skills? Could you share any specific approaches or strategies you've used?

One of the strategies that I like to implement when I'm mentoring my students is.

I don't. So one of the strategies that I implement is when I'm teaching them something or on an experiment or I'm giving them the idea, I don't give them the idea right away.

I give the idea or I pitch the idea in such a way that they also continue to that idea.

So by doing this, what happens is that.

I know they have to arrive at this point or this conclusion, but I don't tell them directly. I try to generate that conclusion from themselves. They should be the they should be the one to tell me what the conclusion of that discussion is. That is how I approach it. So by doing so, it generates self-confidence. It motivates them. They also understand that, OK, we are also capable of thinking, of thinking beyond.

The boundaries and thinking beyond the innovation limits that we have rather than depending on me as a, as a, as a guide to tell them each and everything, OK, you do this, do this, do this and they will just follow it like a robot, you know?

That motivation is very important because if they don't have that motivation, they lack that self-confidence that whether if there is no support, if I'm not there, whether they're capable enough.

In doing the things on their own or not. So this is the most critical part for me during my mentoring service. I make sure their motivation is there because I know I can do that. That is why I'm guiding them.

But they should also know that even if there is nobody or they should know that they have the confidence that OK, they can also do it. With little bit of help they can also do it.

This thing has to be instilled in them. This is what I ensured during my mentoring.

Of students.

This approach of fostering independent thinking and building confidence in your students is commendable, Professor Jena. By encouraging them to arrive at conclusions themselves, you not only ensure academic rigor but also nurture their ability to tackle challenges autonomously—a crucial skill for researchers and professionals alike. I now want to move to your experience with student evaluation and exam duties. How do you design assessments or examinations to accurately measure both theoretical understanding and practical competencies?

My approach would be to design the examination papers, specifically the questions that are there. There should be a 5050 balance between fundamental theory and practical oriented. So if there are 10 questions to be set, 5 questions to deal with the core fundamental of that topic, and the rest of the five questions should be.

Real-world example based where the student will apply those core fundamentals, solve the problem, arrive at the answer and answer it. So it has to be a 5050 balance. So in this way they never get swayed into one or the other like sometimes students.

Should know both the fundamentals and know how to apply it as well. So the examination time is the main is the critical point where this thing can be instilled or this thing can be.

Can be told to them that they have to have a balance between 2:00.

Application and theory. So it has to be a 5050 share.

Your method of balancing theoretical and practical components in assessment design is very well-considered, Professor Jena. By splitting the focus equally between fundamentals and real-world application, you ensure students gain a holistic understanding of the subject while honing problem-solving skills. Moving forward, communication and structured teaching play a key role in effective education. How do you ensure clarity and engagement when delivering complex content to diverse student audiences, possibly from different academic levels?

Our scheduled time for the interview is now up. Before we conclude, do you have any questions related to the role or the company?

Umm, I do not have any questions related to the role uh, because umm, I.

I know everything I need to know about this position as an assistant professor, so I specifically do not have any questions on that aspect.

And regarding company, reality is already such a well known company and so there's nothing much to ask about. So that's it.

Understood, Professor Jena. Would you like me to formally conclude this interview?

Yes, please.