Tag: research

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Mistakes That Follow You Around

Sometimes, things go wrong in your life that, no matter how much you try, you just cannot hide them.

Especially if you work with smelly chemicals in your lab.

You spill one of those and your biggest wish would be to bury the evidence of your clumsiness. It’s all well. You clean up, no big damages. No one saw you. It should be fine. Only if it was this simple.

The smell of the chemical will not let it be so. It haunts you and follows you around everywhere you go.

And people start asking questions. Questions that should not have been raised in the first place, that are best left unanswered.

And then, you have to admit that yes, it is you. This is something that you have done. And it is definitely you who smells like that chemical.

But in this adversity lies a masked opportunity. An opportunity to develop your own line of perfumes that smell like chemicals in your lab. Then you can wear them all the time and get the people in your lab accustomed to those smells.

So that next time, they won’t even know (plus you generate revenue. Win-win.)

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Square Tomatoes

Some days ago, I found out about the square tomato.

In short, the “cultivar VF-145” was developed at UC Davis to get to a more sturdy kind of tomato that wouldn’t squish so easily and wouldn’t roll of conveyor belts. It is not really square, but just “less round” than a more round tomato.

Anyway, many different ways to look at, and solve, a single problem (and all the subsequent problems that arise because of that). Less manpower – Get in a machine to do it. The tomatoes roll off and get crushed by machines – Change the tomatoes.

Or perhaps it depends on the person you bring the problem to. Changing the tomato would probably be the first thing that comes to the mind of a plant breeder – but not necessarily to the mind of a solar cell researcher (not that solar cell researchers are the best people to solve tomato problems… It’s probably a good idea to leave them to tomato experts). But it does make one wonder: would the solution be still the same had someone else been on this task? Someone with a slightly different background and technical expertise?

I have had times in research when I was stumped by a mundane, and most of the times non-scientific, problem (which is, of course, mundane only in retrospect – no problem is too stupid, too mundane, or too small when it is in the phase of being a problem). I only had to move around, sometimes up and down some stairs, throwing the question at people I knew – and one of them would reply with an obvious solution that would leave you in a speechless why-didn’t-I-think-of-this state.

Times like these are when you find out who your real friends are – They are the ones who will not hesitate to break you out of your tunnel vision and bring you play-doh to seal your glassware airtight when you need it.

However, my biggest concern is completely unrelated to this now: Have I been eating square tomatoes all my life, thinking they were round when there were probably rounder tomatoes out there? Have I even seen a truly round tomato ever?

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Machine Learning

I would like to, first, apologize for the misleading title.

Wikipedia defines “machine learning” as:

Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to learn (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed.

But that is not what this post is about.

You see, as a junior scientist about to embark on your career path, one thing you should do is learn to use different machines. Some machines will make something for you, whereas others will tell you that those machines are probably not working, and despite all your efforts, it will be some time before you start getting good samples or results out of any thing in your lab.

So if you work in a lab, you’ll now have a very good idea of who rules there: It’s them. Now and forward, in your time as a researcher, you’ll go into many labs, meet a lot of people, and learn a lot of new stuff… but nothing and no one will judge you like those silent machines sitting smugly atop work benches of your lab.

Many a times, I have started working with one, thinking, oh this is going to be one of the easier ones. Never has that attitude worked for me. It’s like you cannot trust any single one of them; even the ones that you think you know, won’t think twice when it is as tempting as shattering your trust in them.

They don’t care if you are enthusiastic about research and have chosen this field as your career with such joy. They will happily make you think that you have nice typical graphs* in your presentation that you are about to show in your group meeting. They want you to suffer, to taste embarrassment, like they want to see how long you are going to last in this field that you have been so passionate about …

… Until you have proven yourself worthy of research.

As a result, one of the few things I have always found myself doing in the beginning is to start gaining favor of the machines that are in my lab. To build good rapport with them. And it is not easy, not with every machine. Even after you feel you two are going along well, they will still  test you, or just throw you under the bus when they feel like it**.

If it all sounds depressing to you, let me tell you there is hope. Somehow, after you have stood beside them long hours and worked with them at odd times***, they will start to slowly accept you and let you enter the ranks of researchers. It’s blood, sweat and tears to gain that kind of trust, but you can gain the status of meh-you’ll-do in the eyes of the machines.

But don’t ever expect them to start liking you, because that’s ridiculous, they probably don’t have a heart.

 

 * Well, okay, they may have been looking a little weird to you as well, but early on, you wouldn’t know that, thinking this is probably how they are supposed to be.

** Which tends to happen most on Fridays, followed next in probability by Mondays.

*** Condition of high-levels-of-consistency needs to be met with these requirements of long-hours and odd-times.

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Other Stuff

A while back, I came across this post about how science can make you feel stupid.

I shared it, thinking I understood perfectly what it meant and felt like. I actually didn’t then, because now I know what it means and feels like. (And yet, I am not exactly sure how it feels like. Stupid can take so many forms).

When I started off my PhD, I was like any normal person, motivated about starting a new “project” that they are excited about. It’s just like new year, and we all know how that goes:

1) You start off with a long list of resolutions;

2) You start following through on almost all of them immediately;

3) You feel so good that you are following through, and how this year did not turn out like last year (and we all know how that went);

4) You start realizing how by starting everything, you broke all rules of developing new habits, and how this is not sustainable at all (did you really even want all of this?);

5) You start going back to your normal routine, and your resolutions start feeling less important to you now;

6) New year, and you have almost forgotten (almost) how last year went and are ready for a new cycle of highly-motivated-to-back-to-“normal”.

But of course, everybody knows these stages, everyone has new-year moments. And when I started my PhD, I knew I’d face some kind of a slump some of the times. People-on-the-internet told me that the PhD dip is inevitable, and it is not a question of if you will come across it but when you will actually experience it (although they also told me that this phase comes sometime around the second year and I am still in my first, so am I just going through a trailer for the actual movie that will be officially opening in months to come?).

The thing is, despite knowing this, I didn’t really plan for this time (that is another kind of stupid right there). Because, like any normal person motivated about starting a new “project” that they are excited about, I wanted to be laser-focused on my PhD and on things that would take it forward.

So if I needed a break from lab work, I could read or catch up on literature, and if I needed a break from reading, I could take some online course. I did like doing other stuff, but all of that could wait until I had my PhD a little more on routine (a thing, that I am finding out only now, was not as easy as I supposed it was, but that could be for another time).

And this is the importance of comparatively-dumber-sounding other stuff.

Because when you are doing something as crazy as a PhD, where you can go months running around in circles finding your way back to square-one’s, feeling-stupid does become inevitable. And when you see it’s been a while since you last made progress, or learnt something new, or developed a new skill, or added something to you, yourself, as a person, that can be eexxttrreemmeellyy demotivating.

But other stuff can help you here.

Because if you have a little something going on the side, like learning a new skill that may not be completely related to your PhD, it is some progress that you can, at least, show to yourself: So, yes, I still haven’t been able to decide if zinc chloride is better or if I should go for zinc acetate for my solutions, but I have completed six-hundred-and-eighty-five blog posts! That should be a milestone!

So that’s why I have started to think about starting other stuff this new semester. Like taking a language course (I have always wanted to learn another language and now might be a perfect opportunity), or starting to draw (I have some half-developed scripts for a comic on how my PhD stuff is going), or taking up other random workshops and activities where I can just change my environment and see what else is up in the world.

And there is another reason why the other stuff can be so complementary to your PhD: so now when you are moving about progress-less, you can blame it on the other stuff, and how, because of other stuff, you probably have not been able to focus on your PhD.

But the other stuff was your stupid idea, wasn’t it?

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Breakdowns

Recently, the hotplate I had been using was glitching a lot (it was fine when I was not using it this much). And then I saw this in my social media newsfeed and this seemed to hit right on point:

Currently, I think we have both started to understand each other more, so we have been getting along better. Now, I have nothing but honest praise for the hot plate.

Dear hotplate, you are the best! 🙂

(I took the image from this link but could not find whose brilliant theory this actually is).

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Doing Dishes

When I am a little frustrated, I blog about it. I have found that it clears your head and is quite an effective release mechanism.

And recently, I have found another way for times when I am more frustrated – a LOT frustrated:

Wash glassware.

Just grab a brush, pour a dollop of soap and take it ALL out on the stupid organic stuff sticking in there that just won’t go away. Bonus points for you if you can get it sparkly clean (suitable for a dish-washing soap advertisement) and a gold medal (albeit imaginary) if you can make that water “sheet” over your glassware during your distilled water rinse (nothing more spiritually satisfying than that).

Washing glassware is my research-bane: it’s the rate limiting step to my progress – where I’m most likely to procrastinate when starting a new experiment. To a normal human mind, washing some glass bottles and beakers could look like a mundane and quick step, but it can be quite tiring and time-consuming depending on how finicky you are about your glassware (and of course, we scientists-of-the-wet-labs, have specific procedures for washing our dishes.)

(Also, if you would like to read in depth about the many things that can get your beakers sparkly clean (except for cracks and scratches, where the only remedy might be to get new sparkly-clean glassware), I highly recommend this link).

So if I can have really clean bottles in my cabinet and methodically washed glass slides in my drawer, I have one less excuse to laze around and can start working on my next steps. It’s like killing two birds with one stone and so this is where I can channel the frustration of my failed-lab-experiments.

Now, from my recent frustrations, I have accumulated a fair number of glass bottles and washed slides and I am good for a couple of upcoming series that I should be running.

After I run out, I can think about learning the art of strategic frustration so that I can always have some clean glassware at hand in future, or when needed (not sure how that would work, but if I do, that can be for another blog post).

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Problem: Solutions

Lately in science, I have been trying to dissolve a  couple of salts in some “solvent” – I have now tried some options, but nothing has really worked so far.

So today, after around 3 weeks of trying to dissolve that salt, I sat down to compile the results of all my mixtures of salts-and-solvents – they could definitely not be called “solutions”.

And that was exactly what was wrong with these, not only scientifically and technically, but also on a literary level: What do I call them when I am writing my report to send to my supervisor?

We, scientists, are supposed to be very specific in terms of technical terms. So I could call them “solutions”, because I was ultimately aiming to make a solution but just was not getting there. But then, they weren’t really solutions, so how could I call them that? If I did, what kind of a scientist would that make me? Would I even be able to sleep at night?

It was a relief I could use the word “suspension” appropriately enough for some of them. That was really so considerate of that particular salt-solvent combination to give me the freedom to use another word.

For all other… Mixtures? Salt-solvent systems?… What do I call them? Or do I just craftily go on writing about them, carefully avoiding sentence structures where I would need to use the you-know-what word?

It’s crazy what people expect of a PhD student: they have to be a scientist in the lab, a writer when writing reports and papers and dissertations, and  an excellent communicator when they are supposed to present their work (and the best sales person if they choose to go into entrepreneurship).

And being a writer at that particular moment of time, how many times could I allow myself to use the word “mixture” over and over again? Or using the same sentence structure for every next line?

I do not know how I managed all that today. I just hope I can sleep at night.

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Bad Day for Science?

It hadn’t been past 12 yesterday when I had officially declared it a bad day for science.

The hot plate I was supposed to be using extensively broke down (again!) and I discovered that one of the good things going on seemed so rosy because I had been miscalculating some things (and misleading myself and others about how it was going so good at that end).

Having been through all that by 11, well, what could happen now that would make it a better day?

But in retrospect, I think I may have labelled yesterday a little too soon – because the day itself didn’t turn out that bad after all.

We, scientists, we label. And that’s a good thing. You should label all your solutions and chemicals as soon as possible, even before you put your stuff in a blank bottle, but essentially ahead of forgetting what you put in there (and until you have labelled these, your life hangs in some kind of a science-purgatory where you keep chanting the words in your head until you have penned it down where it belongs).

But today – today when I was again tempted to categorize the day in the bad-days* section, I reminded myself that a scientist should not label her days as hastily as she should label her sample bottles.

 

* A “bad” day for science constitutes all days that are worse than your usual days, when it’s normal that things don’t always work the way you want them to. A “bad” day happens when you discover having an outlier compared to your average kind of day (which can normally be rated quite close to each other on a scale of 1 to 10).

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The Pessimist In The Lab

It’s a very good thing to practice optimism in life.  Optimism can keep you going in the face of challenges, and absurd optimism can keep you going despite past failed experiences and your knowledge of all odds being against you.

But in general, optimism is healthy, and I have been thinking about practicing more of this optimistic approach to life in general.

Except when it comes to my lab work. There, I don’t see how optimism helps me.

Because lab work, well, you can never be too sure about lab work.

And, as past experiments have shown, 95% of experiments don’t usually work the way you want them to (and if you are optimizing some procedure or recipe, the success rate can be quite close to zero).

In principle, I could be optimistic about my experiments before I start them, that may be this is THE time they will work.

But being a scientist, how can you just ignore the evidence of your low success rate of past experiments? How can you disregard all that data?

The thing is, it is easy to be optimistic and hard to be a realistic pessimist. It’s also called “the planning fallacy”, which describes how we, humans, are optimistic about our abilities and predict we can do things much faster and smarter than we actually are able to.

And I have been subject to this planning fallacy numerous times. Dozens of times I have thought I could do my part in group assignments well in time, or that I could surely submit a work update to my supervisor by the end of a week. Or that by July, I’d surely be at a stage where I’d be making my own Perovskite solar cell devices (didn’t happen, just so you know).

90% of the times, though, I have found myself not even close to meeting those deadlines.

So with the lab work, I have no choice but to opt for pessimism as my approach-of-choice to go about it. That despite every strategy that I develop to get my next solution not-cloudy, is probably not going to work, because:

1) It didn’t work the last time, when I very well thought that may be this in THE time it will work.

2) If it, by some tiny amount of chance, does work, and the cloudiness from my solutions clear up, well then, that is exactly how I’d prefer it, wouldn’t I?

So, in the long run, pessimism in the lab is a better option. If it doesn’t work, you are not disappointed, because you knew it wouldn’t work.

And if it does work, you can be twice more happy compared to if you thought that may be this in THE time it will work.

P. S. However, of course, there is a catch. If all of a sudden, everything starts going right in the lab, well, that’s enough ground to include skepticism in your mix of pessimism.