A small roller coaster ride as I submitted my first preprint

(I originally wrote this on June 29, 2022 but never posted it)

Roughly 2 years into my PhD program, I finally finished and submitted my first paper manuscript. If you are interested, the preprint is available here on arXiv. As I picked this blog post up again in December 2023, the paper is finally accepted by Ultramicroscopy and is available here as well: https://doi.org/10.1016/j.ultramic.2023.113902.

Rather than talking much about what the paper is about, which you can read yourself, here I would actually prefer to talk about all the events leading to this paper. I looked back and realized yesterday that the thoughts leading to the idea presented in this paper could really have been left in a corner of my desk last November and never be touched again.

(1)

Back in August 2022. The detector I used has the functionality of filtering incoming electrons (or any high-energy particles) based on their energy by applying a threshold. However, I noted that when I set the threshold higher than the primary electron energy of the microscope, the detector still register events, lots of them. I thought the energy calibration is off (turns our this is simply because of pile-up, so many electrons are registered as a single event but with sum of their energies).

The manufacturer of the detector told me to do an energy spectra collection, which requires another data readout mode optimized for sparse events, and typical STEM-in-SEM beam currents are too high to give the desierd “sparsity”. So this readout mode does not work. Had it work, we could do spectra imaging with electrons and evelate our experiments. Though at that time I am not aware of this limitation, and thus I had some discussions with the manufacturer of the detector.

To show them my confusion, I collected the patterns using both the conventional readout and the new readout, the former gives the diffraction pattern and the latter doesn’t. I also captured the patterns at different exposure times (this eventually becomes FIgure 3(a) and (b) in the paper), and tried to compare total event counts for the two modes. Eventually, another detector expert was able to answer my questions.

(1)

In late November, when I had pretty much gave up on the spectra imaging prospect, my supervisor had a look at the slides I made for the manufacturer again, and mentioned something vaguely about the prospect of combining these patterns for a better one. The sentence started from “I am sure there is something you could do re……”.

Now that the paper is finalized, I could not quite remember how lost I was hearing these instructions. But I knew I had no idea how to make it happen. So roughly a week later, I showed up in my supervisor’s office with no prior notice, and asked him to explain his idea. Eventually, he made 3 sketches on paper.

Still, at that time my focus was on detector characterization, so if anything this is a secondary objective. However, our SEM broke down on December 7th and repair schedule is unknown, probably after the Christmas break.

So, I had to find things to do, and dug out the 3 pieces of paper my supervisor made sketches on, and started programming in MATLAB. It took me a while to get everything going and at its very beginning stage, the code is quite primitive, no loops, no indices, and I keep forgetting it’s “~=” rather than “!=” in MATLAB. After a few days, a final pattern was produced according to our initial plans.

Feedback? “That’s beautiful”. This makes me realize that we might be onto something. After the turn of the year, I decided to talk about this routine on our research group meeting, which is the first time I present my own results since I joined (if not counting the 2 exam rehersals in March and August 2021).

Since my diffraction experiments usually requires a long SEM session, I work a lot on Saturdays. One of these Saturdays when I tried to collect some data with software masking of the detector (masked pixels are turned off during data collection) using an aluminum sample, I collected an interesting dataset and processed it with the code I wrote, indexed the resulting pattern and showed to my supervisor again.

Feedback? “Woah – that is super pretty!!!!!!” (all 6 exclamation marks reproduced here). So we decided to write the first draft paper you see today. The “super pretty” pattern is now the demonstration pattern in Figures 4-6. The first draft was ready as early as in March and my supervisor said “it reads”. But this is still not the full story.

(2)

At this stage, we thought that the theoretical framework of pattern processing and the demonstrative aluminum pattern alone might not be enough for a full-length paper, so it would be better to see if the same method works on other materials, because it should. With this in mind, I borrowed some samples from a colleague who deemed the samples no longer useful to her. The sample is interesting, and we know the candidate phas but also different. A lot of time was spent on phase identification on what seems to be an unknown structure.

In addition, I started to prioritize “other project”. Until a few weeks ago, when I received an alert on a new paper, which basically did the “other project” I have been doing. This is only a few days after one of the milestones of my detector characterization experiments. I panicked a lot and panic-wrote my own version of the detector paper.

While I panicked, my supervisor was preparing for a workshop talk and asked me to provide a few diffraction patterns. Afterwards, according to him, one of the attendees, who seems to be a tough audience, was impressed with my processing method and the final result.

It was in that instant that I asked if we can submit the paper without the extra work on the borrowed samples, or any other sample. I reopened that Word document which I haven’t opened in 3 months, and started editing. Since this is a panic reaction and also important thing for my PhD program, I also work on the file on Saturdays and tried to put together everything needed for the submission.

My supervisor, on the other hand, wants to add a bit more to the paper now that the planned content on the extra samples are gone. He wanted to work on something different with my pattern, something hasn’t been done in a while by him. This code alone took more than a week for him to complete, while he was attending another conference and trying to promote my work to a number of microscopists. For this I am quite thankful as we both realize that the paper will be even better with these extra work. This piece of “extra work” becomes the reprojection and fully experimental stereogram, i.e. Figure 7 and 8.

(3)

At this point, most of our discussions and collaborations are about the manuscript and figures. An very important aspect of the work, my code, was not touched or checked by my supervisor yet. As he tried to merge my code into the AstroEBSD repository, he “function-ized” a lot of the routines in my code and had frequently asked me to see if the new version works. Indeed, the new version is concise and, agree to disagree, probably more elegant.

As he travelled to Europe yet again, so in order to catch up as quickly as possible thanks to the time difference, I check my messages and emails immediately after I wake up, and make edits accordingly. Everything went according to the plan, and we plan to submit to a journal and arXiv on Friday, June 23th.

Friday morning when I woke up, this message shows up:

“Need you to check / verify something // I have updated the matlab code for the paper release”

I thought it would just be testing, so despite it was 6 AM, I started debugging in my bed. But the edited code had a multiplicity of errors when I ran them. When I managed to make the program run, what could finish in 7 seconds now finishes in 7 minutes, and I had no clue about what I could do. The issue was related to generating FIgure 3(e). The anxiety quickly climbed to a level where I was almost blinded by rage.

I replied at least twice “I don’t know how to fix it” and even thought about not being able to submit the paper on my bus ride to the university. Luckily, it was in June, so the bus ride was otherwise pleasant…

6 hours later, sitting in my office, I realized that there is an alternative way, and after some crunching the code works again at the intended speed and produces the result I wanted. Of course, due to time zone issues, it is already a bit late to submit the paper on Friday.

The wait did not last too long, as I got confirmation from him Sunday at 1:21AM that everything is submitted. I saw the messages and emails after I woke up and made a long, long exhale.

(4)

So yes, the paper started from a dataset not originally collected for this purpose. I had two panic attacks and lots of rage and frustration. However, if things did not unfold in this way, the sketch papers with my supervisor’s idea would still sat on my bookshelves, collect dust and might never be picked up by me ever.

Personal thoughts on undergrad research experience

In a program like materials engineering, which is both theory- and industry-oriented, undergraduates can be quite bilateral about their future. Some look forward to industry experience, while others may be very enthusiastic about research opportunities. As a former undergraduate researcher and a grad student (and someone who doesn’t like the industry very much), I have been often asked on research opportunities for undergraduates. I only have positives about my own research experience, but that’s often not the case, as I have heard quite a bit of complaints, too. Perhaps I can share my own ideas about undergraduate research opportunities from a few different perspectives.

What do undergraduates want from such experience?

For undergraduates, participating in some research activities is a great opportunity to learn something new and jump out of the textbooks, assignments, lectures which pretty much constitute the “ideal, theoretical” world. It is at least an aspiration.

There are a few pragmatic or vanity motivations though. I will list a few and dissect them in later sections.

One of them is obviously showing off. I still remember someone walking into FF319 (computer lab) in lab coats, with a thong in his hand holding a crucible, violating multiple building/lab safety rules. Or things like “I work in AMPEL”, “I touched the SEM”. They do make you feel good when you are a rookie. I am not totally against this, because the first step of every journey is worth celebrating.

SEM is so commonly used in both academic and commercial research that it should be a staple part of the undergraduate curriculum of materials science and engineering.

Make use of long holidays, so you wouldn’t stay at home for 2-4 months doing nothing. This was one of my motivations of doing summer research as an undergrad.
Accumulating experience (i.e. things that could turn into entries on your résumé). With research experience ubiquitous among undergraduates, it might me a decided disadvantage if you don’t have it. (Then what separates people if everyone does?)
Connections & friends.
Or even more pragmatic, publication opportunities. Quality research experience is a gateway to grad school, and we want to get something out of it. Comparing to understanding of topics, ability to analyze…, a publication entry on your résumé is more tangible (in some communities, publications are an important, if not the only.metric of research ability, which is not true at all). Some supervisors may be kind enough to give you an opportunity (I was a beneficiary), but you should be aware of and be honest about your contribution. You might also get asked about your research in interviews, then having a paper means nothing if you don’t have enough understanding of what you did. If your supervisor don’t want to publish your research , do not push it. There must be reasons behind it. Never attempt to publish without consulting your supervisor first. Your research is likely a subset of a grad student’s work or a patent and that may result in predatory.
Gain deeper understanding on specific topics. This is not limited to instruments or experimental techniques, but also theories. For example, I worked in a hydrometallurgy research lab and gained quite a lot of knowledge on electrochemistry through reading and guided experiments.

What should you expect as an undergraduate researcher?

The key thing is how we define “research” in this sense.

What we do as research as grad students is probably much broader than undergraduates think. Undergraduates tend to think that research is just working in a lab. Indeed, lab works tend to consist the majority of undergraduate research projects. But it is not the full picture. From my own experience, I summarized the key components of conducting a research project below:

Understand the background of the topic/project. This include reading literature (i.e. get an idea of what is known, what has been done and what is yet to be solved) and, for example, listening to what senior grad students talk during group meetings. This is often omitted or only briefly introduced in undergrad research projects. First, not a lot of undergraduates have the patience to read through the literature (especially as a part-time researcher), and second, the time scope of their project often does not allow them to do so either.

Designing experiments (or models). Again, this sometimes involve learning from the literature. For students with no previous research experience, this can be quite difficult. Sometimes this is taken care of by the supervisor.

Doing experiments (or running models). This might be what undergraduates often define as research and are assigned as their main tasks. It is indeed an integral part of any research project, where you get to touch some cool equipment, and also where the time-consuming, repetitive works tend to stem.

Data processing, interpretation and validation. This is where raw information from the cool equipment is processed (e.g. data, images), transferred to visual elements like plots and charts. Moreover, it should tell you something about the problem you want to solve. One also will validate the results against common sense, established theories and previous works.

Iterations. What if the experiments did not give the desired outcome? Grad students will turn to their supervisors, colleagues and the literature for help, then design new experiments and try again. It is a tough and often inevitable part of grad school. Luckily, most undergraduates don’t have to experience them in their projects.

So as an undergraduate researcher, your task is often simple: experiments and data processing. Sometimes you will also be asked to learn some background knowledge. The most difficult parts are often taken care of by the grad student or the supervisor (professor, research associate, post-doc…). Don’t be disappointed, as these are transferrable skills should you not end up in the research community. Always let the supervisor/grad student know if you desire more challenges. But you should be aware what the bigger, complete picture is.

Thinking experiments as the only component of research is a frequent misunderstanding among undergraduates.

There are, however, certain cases that the grad student ask the student to review the literature (reading papers, textbooks, etc.), and that is where things deviates from the ideal. I am strongly against this type of undergraduate research. It takes a certain level of background knowledge to understand research papers, and undergraduates (especially first- or second-years) tend not to fulfill that requirement. If it becomes merely searching of key words, then the research project is way too menial. Graduate students should actively do these tasks on their own because it’s your own project, your own knowledge network and your own understanding that you are developing. At least at UBC, literature review is a key part of your thesis.

How can we improve the environment for undergraduate research enthusiasts as grad students, etc.?

Try to encourage the students as much as possible. Don’t objectively scare them off.

Try to assign projects to undergraduates with a clear direction.

If possible, try to integrate experiments (or modelling), data processing and interpretation in the tasks.

Be patient and don’t push the undergrad. Be kind to them and try to teach everything you know. It is natural that they will face quite a lot of unfamiliar topics.

Give students chances to present or showcase their work in the form of routine group meetings, etc. An even better idea is to hold a poster/presentation session for all undergraduate research students in the scope of research group or even the department. For example, Faculty of Pharmaceutical Sciences of UBC holds annual poster & presentation sessions for summer students. The students are asked to prepare a poster and an abstract about their research, and then present in front of a crowd. The 2020 edition can be found here: https://pharmsci.ubc.ca/research/summer-student-research-program/2020-virtual-summer-student-research-program-poster

In addition to student direct contacting the supervisor, which should help those with better grades and looking for challenges; there can be such mechanisms organized by student council or the department to pair students with research projects. Such mechanism also exists in UBC MTRL, which was initiated in 2015 and has been implemented for 2 years.

Design and improve courses on characterization techniques, data processing and statistics.

General advice to prospect undergraduate researchers?

Research is not the only career route for STEM major. It is totally fine if you are not interested in research. Don’t force yourself into it.

Ignore the peer pressure. Your friend is working on a more inetresting project or working with a “better” professor? You are jealous of someone? I understand the desire of pursuing higher goals, but at the start of your research career, accumulation of experience and forming your own ideas and plans are more important.

Topic wise, (1) there is no bad topic. People influenced by e.g. The Big Bang Theory may learned the “discrimination” of research subjects. Remember that as an undergraduate, the key is to gain transferrable skills and (primitive) understanding of certain topics. It can be extra motivating to work on an interesting topic, but don’t be too disappointed if you don’t, because you can still learn a lot. Even at the graduate stage and further, remember that all reseach fields are equally important and the ultimate goal is to advance our understanding of the world, and improve our lives. (2) Try to explore more topics at your early stage. Don’t limit yourself a specific one when you are a first- or second-year student (especially if you don’t have a clear favourite topic). Stay curious. You might be interested in topic A now, then change to topic B or C a few months later.

Mineral-related metallurgical research are sometimes not welcomed by undergraduate students because they are “not cool”, despite being an integral part of metal production.

Try to work with different people and groups. People of a research group tends to share similar skill sets, mindsets and knowledge background. It is a vital in the research world to broaden your horizon and get new ideas. Moreover, you should demonstrate to your future supervisor/employer that you are capable of working with a variety of people. This is very important if you want a career in academia.

Asking for opportunities to present your work on group meetings. It will help you practice your presentation, expression and organizational skills. Also a good opportunity to assess the level of your research work.

When possible, read introductory-level textbook of the general topic/experimental technique you are working on. For almost every topic in materials science, there is a classic textbook. Your supervisor should know some of them. Self-learning is a crucial part of grad school and if you want to have a nice transition from undergrad to grad school, you should try it and even form a habit. If you think you have extra energy to burn, you can read more!

Take lab/test notes. Also, take notes when you are doing a new type of experiment, using a new equipment or learning something new in general.

Know your limits. We often hear things like “going outside of the comfort zone”, which can be true. What I want to say here is not to force yourself into doing a lot of works that are beyond your capabilities, because this is only the beginning of a journey, not the final sprint. Further, with limited knowledge it is often not helpful to attending research seminars (if that is a thing in your research group) or lectures that are too advanced. You can easily get lost, waste some time and possibly get discouraged.

Prof. Dallas Trinkle giving a seminar at UBC Materials Engineering in November 2019. Many research seminars are designated for graduate students only and that is with good reasons. Without at least superficial understanding of the topic, one can easily get lost in a convoluted seminar.

Keep a good work ethic. Your grad student supervisor usually don’t expect miracles from you. But you should at least finish the basics and commit a reasonable amount of time and effort.

If your research topic is more or less systematic (e.g.combination background, experiment, data interpretation), try to summarize your research into a scientific report, mimicking the structure of published works. It may not be publishable, but it is a great practice of your scientific writing and definitely transcendents the level of lab reports in your courses.

A Short Navigation of UBC MTRL courses (200 and 300-level)

This article is out-dated as I graduated many years ago. You should consult more recent graduates for up-to-date information.

Disclaimer: please notice that all the words in this article are based on my own experience and are only references. It should not be used as your sole determinant for judging the quality of a course.

MTRL 201, 392/398, 489: Technical Communication

No comment.

MTRL 250: Principles and Calculations of Materials Engineering

(I have no idea who came up with this weird new name for the course.)

An introductory level course on thermodynamics. Covers most basic thermodynamics such as 0th, 1st and 2nd Laws, enthalpy, entropy and Free Energy. The course was redesigned along with MTRL 252 so that more focus was put on materials (mass) and heat balance in 250.

Some derivations in the second part of the course when thermodynamics comes into play (derivations are an integral part of thermodynamics) that can make you asleep. Be sure to understand at least the thermodynamics part, which is one of the foundations of the materials program. Some assignments require a lot of work (not necessarily hard). Pay attention to the structure and train of thoughts when you solve the problem.

Exams are open book as of 2019W. The suggested textbook can be useful. Rumours are that this class will be redesigned in fall 2020, but thermodynamics should remain there.

MTRL 252:

This class used to be “pyrometallurgy”, which means extracting metals from ores using high temperature methods. The class was focused on unit operations and mass/heat balance and I am not sure about the new materials yet. Maybe that was not so attractive after all.

It was renamed “thermodynamics of materials”. It seems some contents on solution thermodynamics such as activity have been added. The class is somewhat calculation intensive and thermodynamics from 250 is frequently used.

Unfortunately, if you want to know things about the practical side of pyrometallurgy (i.e. how metals are made from ores), you will have to read books or watch videos. It could be useful for co-op based on some feedbacks. Principles of Extractive Metallurgy by Terkel Rosenqvist is a good reference. I believe a hard copy is available at UBC Library because I borrowed it in 2016.

MTRL 263: Fluid Mechanics

This might not be the fluid mechanics (e.g. aerodynamics) you imagined. This is a chemical engineering version of fluid mechanics but the very basic concepts should be the same. Again, this is (or at least, should be) a math-intensive class. Be sure to pick up your calculus and differential equation knowledge before or during this class. Also, use the Gaskell textbook.

This class is seldom used in later stages of the MTRL program, but combining with heat transfer, it can be quite useful in many applications.

MTRL 264: Heat Transport

Very important topic for engineering in general.

Unfortunately, this class does not teach PDE and I guess only touches some superficial stuff on finite difference methods. I believe there are better ways to teach the class (than the prof did in 2016) but one way to exercise is to dip yourself into problems (lots of textbooks around) and familiarize yourself with the scenarios and the equations.

This is also the easiest way to study for courses without trying to understand the course materials. (not recommended if you aim for grad school)

APSC 278: Engineering Materials

A very important course for MTRL students, yet many found it confusing, including me. The course has too many materials which are scattered, and there is usually only one instructor who will definitely encounter unfamiliar topics. Imagine a guy whose research is pouring liquid in a cavity try to explain energy band theory – the lectures will inevitably lean towards some powerpoint-reading. The new instructor, based on feedbacks I received, has been an significant improvement.

Some topics are rather convoluted and I don’t think I fully understood the class materials until I finished my 3rd year courses. This is what usually happens when lots of topics are covered but not to depth.

I believe the department is trying to reduce the difficulty of this class and add some real life experience, but I don’t fully support the idea. There has to be some theoretical stuff.

For MTRL students, be sure to get a copy of the textbook (Callister). It is a fabulous reference for many future classes.

APSC 279: Engineering Materials Lab

A fun class and a good opportunity to see some primitive materials testing in action. Easy marking (online quizzes) and no lab report required but pre-reading is vital.

MTRL 280: Materials in Design

A flip-classroom course. The instructor is very effective but he is also a very harsh marker. Report writing is needed. Be sure to read the exam questions very carefully so that you won’t easily fall into a potential well. Good practice on conceptual thinking.

MTRL 340: Manufacturing of Materials

Fancy name but terrible materials. If there is a learderboard for waste of money MTRL course, 340 has to be on top.

An unfortunate combination of a few courses in the MTRL program. This course focuses on metal casting, but does not teach heat transfer or fluid mechanics, or how to apply them. Lots of things being taught are experience based. Also many repeats of solution thermodynamics. My most recent experience suggested that this is a memory-intensive class with little to do with casting practice.

I guess the purpose for having this course is that every research group gets to teach a course. This course has to be changed.

MTRL 350: Metallurgical Thermodynamics II

To quote Arnold Sommerfeld, “The first time you go through [thermodynamics], you don’t understand it at all. The second time you go through it, you think you understand it, except for one or two small points. The third time you go through it, you know you don’t understand it.” This course will review what you learn in 250 and 252, and go towards electrochemistry, solution chemistry and some phase equilibria.

It is concept intensive as any other thermodynamics course. But remember that thermodynamics is phenomenological and the theories are not very hard to understand. It is work-intensive as well but very much managable. 250 and 350 used to be one single class and I believe in science (e.g. chemistry), these materials are taught in a single course too.

MTRL 358: Hydrometallurgy I

Hydrometallurgy means extracting metals from ores using water-based techniques.

As I said, our department has a rich tradition of extractive metallurgy, especially hydrometallurgy. You may find this class boring but the instructor is very effective, helpful and can explain everything clearly. Course outline is well structured. Some chemistry knowledge is required. Many claimed that they passed AP chemistry (I didn’t take AP) still struggled through this class. These chemistry are taught in this class. The workload may be a little bit high and good time management will be your friend. Be patient and read the notes!

MTRL 359: Hydrometallurgy Lab

This is in fact some kind of inorganic chemistry lab and will be the most time-consuming 1-credit course you will take in MTRL. The lab reports will also take considerable efforts. Again, develop good time management for yourself and don’t be crybabies like the Class of 2019. Labs are well designed but the standards are quite high.

One challenging point is that the lab material might not be co-current as 358 (depend on your schedule). Ask question when you have one as the instructor is very helpful! Try to do everything (read lab manual, process data and write report) on time.

MTRL 361: Modelling of Materials Processes

I’m not sure if this is still the name of the course. The class includes some basic data processing techniques with some math involved. Programming is needed. Some numerical approximation methods will be discussed.

But the most apparent drawback is the programming language this course uses: VBA. Like what?

The course is greatly simplified with the new instructor in 2017. Harold Cohen’s book Numerical Approximation Methods can be quite useful. Try to understand the math behind the code and the formulas. If you want to be a decent engineer, write the codes by yourself!!!

MTRL 363: Mass Transport

It sounds like a chemical engineering course but it’s a bit tailored to materials students. More attention is paid on solid-solid diffusion which is vital in metallurgy and nanofabrication. Some math required. The instructor likes to give tricky questions. Tutorials are quite useful. Later you may find that mass transport problem is actually everywhere.

MTRL 365: Mechanical Behaviour of Materials

One of the best yet most difficult MTRL courses. Extensive materials and schedule, tricky exams. Focus on both conceptual and computational skills. Many practices are available. Try to follow the proposed course schedule and ask questions when you have one!

MTRL 378: Phase Transformation

Course materials might focus on metal processing but they are rather transferable. One of the best yet most difficult MTRL courses. Both 365 and 378 tend to fail 1/4 of the students in the class every year according to historic data. Well-designed lectures and notes. Extensive practice available from the instructor. Tutorials are also useful. Ask questions when you have one! Get the textbook too.

MTRL 381: Structural and Properties Labrotary

Focus on metals and metallography. Many connections to 365 and 378. Extensive report writing. Hopefully you will also get a touch on sample preparation, microscopy and data interpretation.

Adjust your mindset from “materials of one course should not be tested or used elsewhere”. Try to build a knowledge network and think out of the labs themselves.

MTRL 382: Ceramics

Many don’t like the FYI nature of this course while many like the repeated pattern of the exams of this class……

It’s not fair to test 3rd year students with little knowledge on solid state physics, crystallography or the likes. In my opinion the curriculum is reasonably designed. Otherwise many would fail this class too. Projects (“guided designs”) are fair and involve some data processing and report writing. Labs are not very fun.

(I was told that there was a MTRL course on crystallography decades ago. Apparently I was born perhaps 10 years too late)

MTRL 394: Polymers and Polymer-based Composites

One of the best MTRL courses. A good blend of concepts in characterization, engineering, chemistry and physics. First and probably the only MTRL class that touches statistical mechanics.

Assignments, quizzes and the tests can be tough, but the historical average of 394 is higher than 365 and 378.