What’s Wrong with Being Confident?

Our society expects us to display confidence in order to succeed. Some people internalize this pressure much more than others, with young adults demonstrating high levels of the personality trait called effortlessly perfect self-presentation. Someone high in effortlessly perfect self-presentation not only feels a need to appear perfect, but also to appear to have achieved perfection naturally, without any reason for effort or self-doubt. News stories have suggested that pressures to appear effortlessly perfect are associated with mental health problems such as depression and anxiety in college students. However, little data actually exist on this issue.

The research question the personality lab has been studying is whether there may be a cost to always being encouraged to be confident when you are not feeling your best. We suspected that the need to present constant self-confidence may lead people to criticize and judge themselves for the negative emotions that they experience under stress, rather than approaching these situations with self-compassion. Self-compassion is the concept of being kind to yourself, practicing mindfulness, and reminding yourself that it is part of being human to have flaws and make mistakes. Most importantly, self-compassion implies that you not be too harsh on yourself, especially when you’re struggling.

In our study, we had participants fill out a number of personality questionnaires. Following this, participants were asked to complete a five-minute writing task in one of three conditions, where they were primed to think about the importance of self-confidence, self-compassion, or a neutral condition. The neutral control prompt involved writing about a place to eat around Gettysburg.

After the five minutes were up, participants were presented with a very difficult series of puzzles. The experimenter was required to sit alongside the participant in complete silence as the participant struggled with the puzzles. Shortly after, the participant completed a few more questionnaires about their feelings of self-compassion and mood at that moment.

After running more participants this summer, we started analyzing the data. First, we were required to do data coding, which consisted of deciding whether participants’ written responses to the priming task fulfilled our criteria for inclusion. Many participants had to be excluded from data analysis because their responses were not relevant to the prompt.

For our data analysis, we ran regression analyses to statistically test the significance of the interaction between priming condition and effortlessly perfect self-presentation predicting change in state self-compassion after the puzzle task. We found a significant two-way interaction; the results are presented in the figure below:

As shown in the graph, there is a statistically significant interaction between priming condition and effortlessly perfect self-presentation predicting state self-compassion. Specifically, participants who were low in effortlessly perfect self-presentation showed no significant differences in state self-compassion, regardless of priming condition.  However, among participants who were high in effortlessly perfect self-presentation, those who were primed to focus on the importance of self-confidence showed significantly lower state self-compassion compared to those participants who were primed to focus on self-compassion or food (neutral).

These results have a few implications. For one, this empirically shows that when you remind a person who strives to appear effortlessly perfect that it is important to be confident in themselves to succeed, the end result is that they beat themselves up and blame themselves for not being confident enough when success doesn’t come easily.

Ultimately, this shows that placing too much emphasis on self-confidence can come at a cost to  self-compassion. While popular belief proposes that self-confidence is related to success, leadership, and more competence, our research shows that it does not prepare you well for coping with setbacks. This is particularly relevant in college students, in which they feel pressured to exert confidence for success and are otherwise seen as inferior if they do not exhibit this self-confidence. College students are likely to face failure, rejection, and other very challenging situations in their careers, so it is important that we emphasize more self-compassion than self-confidence to better prepare them to cope.

Personality Lab Research Assistants:

Cindy Campoverde

Jackson Guyton

Stella Nicolaou

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Snailed it!

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Follow @snails_and_friends on Instagram for your daily dose of snails!


The Adventurous Souls of the Snail Lab:

Kelsey DiPenta, the “Parafilm & Plate Pouring Prodigy”

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Kelsey is the youngest member of the lab and arguably the cutest and most genuine soul found on campus. Being a Biology major, she can almost always be found in the science center, either pouring plates or taking a power nap on the couches. Her spontaneity is 100% contagious. She doubles as a generous chauffeur who will ALWAYS drive you anywhere, whether to Rita’s down the street or an hour away. Despite her allergy to cats and dogs, Kelsey has aspirations of owning several cats of her own. Whether in lab or around campus, Kelsey always will brighten your day.

Sarahrose Jonik, the “Life of the Lab”

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Sarahrose Jonik (a.k.a. Sayro, Troublemaker #1, or The Life of the Lab) ensures that the snail lab stays on its toes. When she’s not maneuvering snails like an absolute champ or rocking boot pants in the field, she entertains the lab by belting out the Krusty Krab pizza song. No one will ever really know how she manages to keep her white kicks so crisp and clean, or how she can keep her energy up while being a dedicated biology student and softball star. Her star power on the field is matched only by her star power during epic drum solos in Rock Band.

 Courtney Ward, the Ray of Sunshine 

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Courtney is the lab’s resident BMB major, and is the girl to go to if you ever need any life advice. Courtney brightens up the snail lab, both with her colorful leggings and sunshiny attitude. Her cooking skills are absolutely impeccable, establishing the best gourmet meals a college student could prepare. Whether it be in the game Cooking Mama or in the Appleford kitchen, she is always creating something delicious. One look at her chickpea cookie dough or her chicken salad will leave your mouth-watering. Courtney also has a very active lifestyle, which is reflected in her job at the Den and her love of yoga and zumba.

 Dr. Peter Fong, our Fearless Leader 

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As a pioneer in aquatic toxicology and dog enthusiast, Dr. Fong has been an absolute privilege to work with. He is extremely devoted and passionate about his work and all things invertebrate. You can almost always catch him with his best friend, Messi, a beautiful golden retriever. He frequents the Covered Bridge as his prime fishing location and does anything he can to avoid mowing the lawn. It has been a wonderful summer in Snail Lab and we will never forget our Fearless Leader!

Check out his some of his recent work here.


A Day in the Life for us Snail Folk:

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SPOILER ALERT: These are mussels. They were all dead.

Every day, the bright-eyed individuals of McCreary 212 fell into a routine revolving around snails. We set up hundreds of finger bowls filled with water, collected the snails, waited for them to get comfy in their new home, treated the snails, waited an hour for the drugs to kick in, collected the data for each individual snail, and repeated this process.

We were not deterred by the absence of living mussels in the Magothy river which put a hold on one experiment, OR the extremely high stream levels which prevented crayfish collection. Instead we turned to what we knew best–MORE snails.

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Who took these pictures you ask? No one! #selfiemode

Snail Assay

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We named every snail to make them feel more at home #nobias

The main focus of this toxicology lab is on the induced effects that pharmaceuticals & other industrial chemicals commonly found in the environment have on specific behaviors of aquatic invertebrates. We were particularly focused on the marine and freshwater snails, Ilyanassa obsoleta and Leptoxis carinata respectively.

The tricyclic antidepressants, imipramine, chlomipramine and amitriptyline, were tested on the snails at varying concentrations to determine if they have a significant impact on the righting behavior of these creatures. The righting reflex is the snail’s ability to re-orient itself after being knocked on its back by natural forces, or by pesky student researchers with a blunt pair of forceps.

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Crayfish Assay

During the last week in lab, the snail experts began crayfish experimentation. It was a unanimous agreement that snails are preferred over the violent tendencies of the crayfish. Their righting behavior when exposed to methoxychlor, a pesticide commonly found in the environment, was observed. All creatures were safely returned to their homes in the wild.


Important Life Lessons Learned in Snail Lab:

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Sayro & Kelsey showing proper dish stacking technique #21isthelimit

  • How to siphon gasoline (handy)
  • How to short-sheet a bed (useful prank)
  • How to create a galaxy in coffee with cream (fun when bored)
  • And most importantly… How to properly stack dishes! –>

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The 8 Week Journey Described Through Song:

Life Is A Highway – The adventures we embarked on in Snail Lab are those that will last a lifetime

Jeopardy Theme Song – No explanation needed, we work with snails.

Toxic, Britney Spears – … well, we work in a toxicology lab #drugs

Sweet Escape – Despite the fact that some snails can take 45 minutes to right themselves, they have an amazing talent for escaping their bowls in record timing.

Country Road, Take Me Home – Its been real. Snail Lab, OUT! ❤

Birds of a Feather Fun

This summer we have been working on research with Professor Neller. We have been utilizing machine learning to create an efficient solver of his game Birds of a Feather. Birds of a Feather is a solitaire game consisting of a four by four grid of standard playing cards. The goal is to consolidate the grid of cards into one single stack. To do this, you can move cards that are in the same row or column if they are also the same or adjacent rank or same suit. These rules result in many solvable states of the puzzle. However, in some cases, the puzzle can have unsolvable states, which result from a card or multiple cards that cannot be consolidated with another card. A typical day in our lab consists of us working at our computers in Glatfelter Hall. We’re usually working with Java, but in order to do some probabilistic calculations, we use R.

A basic brute force search algorithm blindly searches. In terms of Birds of a Feather, a brute force search algorithm searches if the puzzle is solvable or not without direction. Our goal, this summer, has been to speed up the search algorithm by giving it direction. We have been giving the searcher “strategies” or heuristics to achieve this efficiency. Imagine if you were playing any game without a strategy in mind. Every move you made in the game would be made without considering the next. Success would rely solely on random chance. The brute force search is just that. The heuristics, as you would imagine, improve the efficiency of our search algorithm by giving it a strategy for search.

Our heuristics attempt to lead the searcher to what is most likely a solvable state of a puzzle. The heuristic we have had the most success with this summer is what we call “total flockability.” In short, a card’s flockability is determined as the amount of cards it can be consolidated with on the board without considering the row and column restrictions. Total flockability is defined as the sum of the flockability of each card on the board. This heuristic says that board states with higher flockability are more likely to be solvable, so when we search, the searcher prefers the board state with the highest flockability. There are many other heuristics that can be used, this is just one example.

Another method we have used is to prune our search by determining if it will be fruitless to search something prior to actually searching it. One example of this is checking for “odd birds”. An odd bird is a situation where a card cannot be consolidated with any other card, or, in other words, a card that has a flockability of 0. If there is a card on the board that cannot be consolidated with another card, then there is no possible way to consolidate into a single stack; therefore, search will be fruitless. This saves a lot of wasted time since a blind search will go into a path with no solution while our search will avoid this fruitless path.

We have not finished our work yet, but we are currently developing a search that considers multiple heuristics through the use of statistics. Through statistical analysis, we can determine which heuristics are the most predictive of solvability. If each heuristic is given a weighting from 0 to 1, then as a sum, they can be used as a heuristic. By making our searcher be able to consider more heuristics at once, it can more quickly solve a puzzle.

 

The Searcher

This part of the description is basic and does not require a computer science background for understanding. The searcher operates by taking the original board state, the cards dealt out in a 4 by 4, and then creating the state of every possible move from that state, or, in other words, creating a board for every single card that you can move on your first move. First it checks if any of those moves are already unsolvable. If it is solvable, then it calculates a heuristic for each one of these states. Then whichever board, by the heuristic, is the most promising, we expand. This means we find all of the boards that result from making all the possible moves from that board. This process repeats until we do or do not find a solvable state. If we did, then the search was over and the board is solvable. If we did not, it searched every possible state and concludes that it is unsolvable.

This part of the description will most likely only make sense if you have a background in computer science. The searcher is a selective depth-first search that utilizes a queue, a stack and a priority queue. First, a node is taken off the queue. If closed, a hash set, already contains this state, then we skip searching it as it would be redundant; otherwise, we add it’s string form to closed and continue the search. If this node is the goal, then we assign it to be the goal node and return true. Otherwise, we expand the node. Then we prune the search by only adding nodes that pass various tests. This avoids unnecessarily checking nodes that are guaranteed to not be the goal node since we already know they are not. Each child that is not pruned has its compareVal, a value used for sorting the priority queue, calculated and then is added to the priority queue. We continue this process until the queue is not empty.

 

Next we iterate through the priority queue, pushing each node onto the stack. The priority queue is organized so that the element that is first is the last element that, according to theheuristic, we want to search. Therefore, the last element, is the most promising node to be pushed onto the stack, so it will be the first node searched. If the stack is not empty, then we pop a node from the stack and offer it to the queue. If the stack is still not empty and the next node on the stack has an equivalent compareVal to the previous node, then we also add that to the queue. We continue this process until there is no longer a tie or the amounts of nodes added to the queue is equal to numTies. If the queue is empty after, then the search is over and we return false; otherwise, start the algorithm from the beginning.

But what are we doing other than playing the game? I wish we could just play the game all day in our lab, but, we are doing even more interesting and challenging things than that. What we are focusing on are the ways to implement how we could solve the puzzle better. We work on designing and exploring many features or behaviors within the game that would help us better understand whether or not a particular puzzle is solvable/unsolvable and what makes it so. Designing these features can be challenging, and sometimes the best you could do is take a guess and hope that it workss. So far, we have worked on implementing around 10 features and have tried to look into how such features would correlate to solvability; how much impact would they have on making a puzzle solvable or unsolvable, measured in terms of probability. The measure of such impact is a heavy computational task, and it is basically a simulation of thousands of possible moves that we might make in the puzzle and analysis on how well we could solve the puzzle if we follow a certain feature/features or a combination of two or more features. We have accumulated more than a G.B of data so far from those simulations and it is just getting bigger and bigger. We are nurturing the data that we get just like our little baby, and for the time being what we can do is just hope that our baby will love us back as it grows older.

 

A day in our computer lab

 

This summer we’ve all had a great time learning more about the field of artificial intelligence through our research. It has definitely been a positive learning experience, and we hope to use these skills that we’ve learned both throughout the rest of our time here at Gettysburg and beyond.

 

 

Exploring Galaxy Cluster Dynamics in Phase Space

This summer, I experienced my first real taste of what scientific research is all about. Having done nothing of the sort prior to this summer, it took a while to get the wheels rolling. I was thrown into an unknown environment working with unknown tools to explore an area of science I knew nothing about. But that is exactly what research is for.

In Dr. Johnson’s lab this summer, I was tasked with exploring the dynamics of a simulated galaxy merger in an effort to understand the dynamics of large-scale structures in our universe. The simulation in question was performed in the late 2000’s by Dr. Zuhone, an Astrophysicist working for the Harvard-Smithsonian Center for Astrophysics in Massachusetts[1]. His simulations were aimed at capturing the effect that a galaxy merger would have on dark matter, which is collisionless, meaning that interactions with other particles have very little to no effect on the system as a whole. The reason that we can model galaxies in a similar way is because both dark matter and galaxies are collisionless and therefore their dynamics should be the same in the event of a merger. The data I worked with was only a small chunk of the enormous and numerous simulations performed by Dr. Zuhone. I was given the xyz-positional and velocity data of one-hundred thousand “galaxies”. My goal was to track these galaxies throughout the merger simulation and plot them in what is known as phase space. Phase space plotting is a method used to plot higher dimensional data in lower dimensions. For instance, when trying to understand the dynamics of three-dimensional space, a phase space plot can show it more plainly in two-dimensions.

My goal in research this summer was to begin to understand some of the dynamics of fourth-dimensional space using this phase space plotting method. The entirety of my work this summer has been on Python. I have been writing a program that gathers, groups, normalizes, and plots the simulation data. This process did give me an incredible opportunity to enter the research world and get a taste of what full time research would be like, at least from a computational standpoint.

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Figure 1. Final working code in Python

The most important part of this research are the graphs and figures I’m able to produce. Phase plots are not as straightforward as normal plots would seem. This plotting style is particularly useful in the plotting and analysis of light curves. The process of creating the plot is somewhat confusing and unnatural. To start, it’s important to understand what the data I dealt with looked like. Of the hundred-thousand “galaxies” previously mentioned, about fifty-one percent came from one cluster while the remaining forty-nine came from the other. My goal was to see if “galaxies” from either cluster exhibited any dynamical differences. The data I received for the merger simulation stretched over seventy-one timesteps, so I had to track the “galaxies” throughout the merger. My program was designed to take an equal amount of randomly chosen “galaxies” from each cluster and gather their position and velocity data over time.

Even though seventy-one time steps is quite a lot, one issue that came up was that they were not all sequential. Some time steps data had been corrupted and therefore lost. I needed to interpolate twelve missing time steps, which was done using linear interpolation. Once all the data was compiled, interpolated, and subsequently normalized, I grouped my data by variable. Then I grouped the values into successive groups of three. These groups of three values then acted as the x, y, and z-coordinates for my phase diagram. This section from a paper by Nada Jevtic illustrates it very clearly.

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I used this method of plotting with my simulation data to create plots like this. Position measurements have been normalized and are in units of cm. Velocity measurements have also been normalized and are units of cm/s.
X-Position: (two separate sets of 10 “galaxies”)

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Red lines: Cluster 1

Blue lines: Cluster 2
X-Velocity: (same sets of 10 “galaxies”)

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I am now at the analysis stage of my research and plan to have an in-depth analysis of all of the graphs over the coming weeks prior to our return to campus in August. Dr. Johnson has given me and the rest of the students in the lab a good bit of independence so that we develop troubleshooting and problem-solving skills on our own, instead of relying on others as a crutch. Even though the eight weeks are just about over, I’m excited to continue my research at home as well as through the upcoming school year.

But now I must talk about the only reason I chose to do research this summer. The mystical ancient art of hacky-sacking, foot-bagging, etc. There are few schools throughout not only the United States but the world that study the art. Dr. Johnson’s hack circle appears from the outsider’s perspective to be nothing more than people in a circle kicking a bag full of assorted beads. But once you enter the circle, your entire world changes for the better. You learn patience, teamwork, how to succeed with style and smack talk, and most importantly how to fail while simultaneously looking like you’ve forgotten how to use your legs.

Hacking offer’s a reprieve from the constricting world of Python(bonus pts for the pun). Hacking is a time to reflect on yourself and what you’ve done, what you still have to do and is great to refocus yourself because you just lost half an hour kicking a sack around. For me hacking has been a stress-free environment that I can use to clear my head and get revitalized for the rest of the day. As an unexpected but welcome bonus, I have also learned basic teleportation and levitation from my time studying at the Johnson School of Hacking. I have learned the proper form of arms above the shoulders and to always look for “fours” and “elevens”. Hacking is both an incredibly individual activity as well as a group activity. It has taught me that to succeed you can’t rely on one person, even if they’re as good as Dr. Kerney, and that focusing on working as a team is the best way to solve any and every problem. Working off of each other’s strengths and weaknesses to form a cohesive and functioning machine is what being a team and being a researcher is all about. I can’t, and most definitely do not, know everything there is to know about research, my field, or physics in general. But I know that to succeed it’s going to take a lot of work from me but also, a lot of faith in others.

~Hayden Hall

Really important link : https://www.youtube.com/watch?v=czTksCF6X8Y

[1] https://arxiv.org/abs/1004.3820

In The Cosmos

In The Cosmos

(reader’s beware: very dramatic and 4th wall will be broken)

Galaxies far far far away are about a couple million to a billion light-years away. The smart physics community discovered that light travels 3.00 x 10^8 meters in one second

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(aka THE UNIVERSAL SPEED LIMIT) convert that over to days to years (3600 seconds in a day and 365 days in a year); so galaxies that are millions of light-years are about billion meters or miles (distances you mere mortals understand) away from the Earth. Light shoots out from ‘nearby’ galaxies having to travel across the dead, empty, and cold cosmos (or is it dead? Aliens… maybe? TBA).  Einstein’s Theory of Relativity discovered that time is relative across space. It’s dependent upon a person’s reference frame. Due to this…

 

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The light hitting us is really in the past! (you’re thinking ‘WHAAAAA’) As time is not stagnant and moves along during the process of light traveling all those millions of light-years to us. It means the light is a piece of history of the galaxy it’s originally from (like a  slice of home in the sky).

Now meet my buddy Han Solo he will inform us and give a visualization of galaxies clusters (here come the meet and potatoes of the project hehehe)

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Han Solo: Well, Hiya guys! Come join me in my intergalactic spaceship, The… *drum roll please* Millennium Falcon! Fancy, huh?

Chewbacca: *in chewbaccian language* Time to go!

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Ahhhh, welcome to the cosmos! Every burst of light represents a galaxy. The galaxies forms clusters such as this one (happens during implosion and explosion of galaxies/stars formation) where the cluster moves together through space-time.

STSCI-HST-abell370_1797x2000Combing what we know now about Eintensin’s theory, light-year distance, and galaxy clusters we get- the effect of light-travel time! What phenomenon is this? Well as the light is traveling to us in the milky way during that time galaxy clusters move so the images we get are only projections in the past (as well 2D only, smh). Well my group of fantastic young lads is studying the effect light-travel time has on these projections.

My side of the project was  more a methods research. I took density profiles (which are representative of position) and velocity profiles as a function of radius and mapped them out. I took the 2D projections and made them 3D to give them to my superior Chief-Hack-Jedi Craig. I did this by building a program through python which runs just different equations and numbers to eventually spit out a beautiful graph in the end.

I had no background knowledge of the material as I came in young and fresh in the cut-throat game of computer programming . I was ripe pickening for the shark codes (deadly recurring nuisances in programming). I started off running my head into a brick wall with being stuck on one problem a day then progressively solving one only to stumble on an another. I was faced with do or death halfway through X-SIG as I wasn’t progressing as fast. What came next….

“I asked them to put me on… so they did put me on”- Drake. I asked for help from my fellow jedi around me (Hayden and Craig) and overcame the minuscule problems of the past. I progressed in my jedi training and quickly moving up the ranks. Where now I have a handle on python and am able to fix my own problems in the code.  I have now created over hundreds of lines codes and this brings me to end of my 8 weeks.AI

*An example of python code as it filled with many functions, loops, and more*

I used the density profiles of King Isothermal, Burkert Profile, N.F.W. Profile, Einasto Profile, Hernquist Profile. The velocity has two profile one based on Kepler’s Law and the other uniformed density so a function of one of the profiles above.

*Below is graph of Burkert Density profile and Uniformed Density Velocity (based on the same density except non normalized) this would be the output of my python code created *

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Velocity Burkert nonN

I sat around wondering as my man Drake said “Is There More?”

*as I stood here thinking about the questions of life only one quote played in my head from the great Master-Chief-in-Hack-Jedi Johnson- the hack circle is an allegory of life*

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Throughout the summer I was confuzzled (confused and puzzled combine) by this mystery quote. How can such a simple game represent life as a whole? Through the wisdom of the force, friends I have made, and memories 🙂 that will last a lifetime this summer- the answer is with me.

Just as in hack circle we accept and are open to anybody no matter where they came from, what they got on, or how they look. We just enjoy the hack as in bigger groups completing the faithful hack (everyone in the circle gets one touch on it) seemed to be too big of a goal. We instead shared laughs of each other mishaps in the circle or discussed over topics such as sports, politics, or about life while cracking and making jokes nevertheless. In the the group we never hung too much on the highs (as we celebrated but moved on) or too much on the lows (as we didn’t even give the thought a time in the day). It was said best when Simba got smacked in the head by the Rafiki (the monkey) in Lion King “The past can hurt. But the way I see it, you can either run from it or learn from it”. That’s what happen in the hack circle and that is what life is about. I will never forget Craig barefoot throws, or the ballerina esque Dr. Johnson’s roundhouse kick, or the Kevin Durant look of Hayden whenever he did a cool trick or save in the hack circle, my very own math logic 10 + 5 = 14 , and last but not least “vertically is key”. These memories I will always cherish in my heart.

Catalyst Extravaganza

Have you ever wondered how ligand substituents affect the efficiency of an iron catalyst during oxidation/reduction reactions? We do!


Synthetic chemistry is a large part of everyday life, whether you know it or not! Synthetic chemists are responsible for creating everyday objects like pharmaceuticals, plastics, and batteries. A common synthetic step required to make these things are oxidation and reduction reactions. Below are simplified examples of a ketone reduction and an alcohol oxidation.

However, many oxidation and reduction reactions can be costly, inefficient, and an environmental burden. In some cases large amounts of toxic and/or expensive reagents are needed. In other cases highly efficient catalysts are used, but they may be made from rare metals or use reactive terminal oxidants or reductants. Our research is continuing the ongoing investigation of a class of (cyclopentadienone)iron tricarbonyl compounds that catalyze these reactions, with a focus on low cost, efficiency, and sustainability. Iron is the second most abundant metal in the earth’s crust, and we are using simple, relatively unreactive oxidants and reductants in our processes.

We have two main projects this summer: 1) synthesizing and exploring the reactivity of iron catalysts with different substituents (collections of atoms) to determine how electron distribution impacts catalyst activity; and 2) exploring the reactivity of (cyclopentadienone)iron tricarbonyl compounds in transfer oxidations. Amelia and Evan are working on project #1, and Tracy and Melanie are tackling project #2.

 

Amelia Hou

Amelia spent the first couple weeks synthesizing a catalyst and purifying it, but now she is testing out the reactivity of five different catalysts. She spends her days oxidizing 4-phenyl-2-butanol and reducing acetophenone and comparing the relative rates and conversions of those reactions to gain insight into whether electron-rich or electron-poor catalysts are more reactive. Considering each reaction takes 48-72 hours, it takes her a lot of time to get data points! The goal of all her waiting is to find which catalyst is the best at each reaction. We will eventually use what we have learned to design new, more active catalysts.

Evan Bertonazzi

Column Chromatography

Evan has been synthesizing cyclopentadienones and their corresponding iron carbonyl compounds for most of his time in the lab. Isolating and purifying those catalysts has produced a love-hate relationship between Evan and flash column chromatography. After he isolated said catalysts, he began testing one of them for its catalytic ability over 24 hours and running it through a kinetic gauntlet to determine how the initial rate of his catalyst in redox reaction compares to the other catalysts. He is currently performing cyclic voltammetry on the catalysts to gain insight into their electrochemical behavior.

Tracy Tang

Tracy is testing three different catalysts on an abundance of different diols (compounds with two alcohols) to try to form lactones (cyclic esters) through sequential oxidations. Lactones are found in everything from natural flavors to antibiotics. She is collecting mountains of data on these reactions to collect as much information on the reactivities of these catalysts as possible. Not only does she have to test each diol with each catalyst, but she often has to make the diol as well!

Melanie Hempel

Melanie is working with one catalyst/oxidant system that has shown itself to be efficient in alcohol oxidation reactions. Conveniently, the terminal oxidant is readily available from agricultural waste, and because she is also using an iron-based catalyst, her process is sustainable. She has spent the summer first finding the best conditions for the catalyst (solvent, temperature, length of reaction, concentration of substrate, etc) and is now testing these conditions on multiple alcohols to determine how well it works with different structures and functional groups. The objective of these studies is to determine how general her synthetic method is. She is also working on getting isolated yields of carbonyl products to see how much of the product from the reaction can actually be obtained.

 

Checking In with the CRISPR Crew

 

CRISPR Crew Header

In the Shariat lab, we are studying Salmonella presence in the Susquehanna River. Although mostly associated with chickens, Salmonella is also found in aquatic environments due to factors like runoff and farming. Earlier this summer we took a three-day sampling adventure up and down the river along with some of our collaborators from the US Food and Drug Administration, to look for Salmonella in both the river itself as well as its tributaries. We split into Team Chicken (North) and Team Cow (South) that sampled six to eight sites per day – all while looking real good in waders (check out the picture of Abby below!). Together, we drove nearly 1000 miles!

 

Abby River

In order to determine the presence of Salmonella in the different sites, the samples were cultured in a variety of media and then plated on nearly 400 plates and left to grow overnight. We used indicator plates to be able to distinguish between Salmonella and other bacterial species found in the river. On the indicator plates we used, Salmonella appears as black colonies. After analyzing all these plates, we used a molecular screening technique to confirm that the colonies were Salmonella. Spoiler alert: we found a whole lot of Salmonella!

Since Salmonella is super diverse, we have been trying to figure out what specific kind of Salmonella was in each sample. In order to do this, we have been analyzing a specific section of bacterial DNA, known as the CRISPR region. From the sequences found in this region, we are able to determine exactly what we are dealing with. In addition to our CRISPR analyses, we are working with the Lamendella Lab at Juniata College to study interactions of microbial communities in the Susquehanna River.

There are two big takeaways from our work so far:

  1. Do not drink the water in the Susquehanna. Ever.
  2. Order your data numerically. Even if you think you can handle it, you can’t. Ordering your data makes it possible to do two weeks of work in just one morning.

Team Chicken and Team Cow hit the road next week for anther sampling trip – round #2, here we come!

Plate Picture CroppedThree plates that we streaked; two are positive for Salmonella (left, center), while the third is negative (right). Both plates came from our favorite site, Beefmaster (we’ve got some stories about this site – you should stop by our lab and ask us!)