Expanding Upon the Single-Use Paper Battery Prototype Using a Biodegradable Encasing With a Pull-Tab Activation Technique for Simplified Use

Poulin et al. (2022) have developed a water-activated paper battery model which uses a paper substrate with conductive inks consisting of a zinc-based anode, graphite-based cathode, and carbon-based current collector, activating on contact with water (2022). Increases in electronic waste show it is crucial to transition to clean energy. As such, the paper battery is entirely biodegradable, with an infinite shelf life until contact with water. The research aimed to improve the model for practicality and ease of use. The original design depicts the paper substrate with the conductive inks applied, with an external water source for activation. The method of improvement was to develop a biodegradable casing with a pull-tab that would separate the paper substrate from an internal water source, allowing for simple activation. It was hypothesized that these improvements would result in a similarly efficient battery that is better developed for practical applications. Two paper batteries were constructed according to the methods described in the original study (Poulin et al., 2022), and the biodegradable casing was 3D printed using algae-based filament. Both batteries were tested for current potential (V), and a two-tailed, paired z-test was performed. It was determined that the improved battery had a significantly lower potential, with a 5.7% decrease compared to the original battery. Conductive inks were not stencil printed, leading to variability in output measurements based on contact position of the measurement nodes used for measurement. Results indicate that while effective, the improved model will have to be produced through additive manufacturing in order to maximize efficiency

Using Artificial Intelligence to Formulate New Deep Eutectic Solvents

The advances in Artificial Intelligence (AI) in the past two decades have enabled algorithms to perform daily human-like tasks such as driving cars, playing complex games, composing classical music, and even generating realistic images by using text as the input parameter. These achievements were accomplished with the implementation of Deep Neural Network (DNN) architecture along with the use of large databases, as well as the increase in computing power. This strategy has also shown promise in several sub-fields of natural sciences such as chemistry, biology, and physics through speech recognition, data analysis, and computer vision. More specifically, in chemistry, deep learning has been used to predict the properties of molecules and predict chemical reactions. To predict the properties of molecules and chemical reactions, a large database of compounds or molecules, such as Deep Eutectic Solvents (DES), must be written in a simplified text such as a Simplified Molecular Input Line Entry System (SMILES). A SMILES database is easily understood by computers, and it translates a chemical structure into a string. With the use of the SMILES database, we were able to train a model with Natural Deep Eutectic Solvents, so the AI could eventually determine if the compounds inputted with SMILES were unstable or stable

Comparing the Effect of Ibuprofen and Acetaminophen on the Concentration of Dissolved Oxygen in Elodea canadensis

Water is the essential substance within our environment that fills basic needs for all organisms (Chopra & Kumar, 2020). Research shows that potentially toxic pharmaceuticals enter our water system due to prevalence of human pharmaceutical use. As these drugs enter the water system, they have a high likelihood of impacting the health and growth of other organisms that rely on this water usage. The purpose of this study was to see the effect of commonly ingested pharmaceuticals and their impact on plant growth and the concentration of dissolved oxygen. It was hypothesized that a common plant, Elodea canadensis, would have the lowest concentration of dissolved oxygen once treated with a solution of 25 mg/L of ibuprofen. This group was hypothesized to be the most affected based on research on other plants, Ibuprofen causes problems in the chloroplast and acetaminophen has not been tested thoroughly on plants(Magdalena et al.,2022). In order to test the hypotheses, three separate groups of plants were treated with a different solution: ibuprofen, acetaminophen, and distilled water (control). The Elodea canadensis was measured through the concentration of dissolved oxygen, plant growth and physical features daily. A One-Way Anova test was conducted to analyze and compare the data. Results from this study indicated that plants with either solution had decreased quality of life, with acetaminophen impacting quality of life most therefore rejecting the hypothesis

Die Schule der Tonleitern, Accore und Verzierurgen, Op. 88, Heft 3

https://scholarexchange.furman.edu/periphery-kharbintsy2/1022/thumbnail.jp

Formalization of Elementary Row Operations Using LEAN

LEAN is a programming language that helps write mathematical proofs by using mathlib, an open-source library with countless theorems and lemmas. LEAN is used to formalize proofs, allowing them to be more easily verified and understood by both computers and humans. The goal of the research was to add elementary row operations of matrices to mathlib using LEAN to allow for other mathematicians to build upon our work. By contributing to mathlib, these new theorems and lemmas reduce the amount of work required by future mathematicians, who can instead work on expanding boundaries instead of proving what has already been done. We first had to learn as much of the LEAN programming language using resources provided by Dr. Kevin Buzzard\u27s Xena project as possible. The next step was to begin formalizing lemmas for elementary row operations using lemmas and theorems already in mathlib. We were able to create lemmas for each of the row operations on 2x2 and 3x3 matrices but were unsuccessful in generalizing the lemmas to any NxN matrix. In conclusion, even though what was accomplished may seem small, it is an excellent first step in proving many more complex theorems and lemmas related to matrices using LEAN, such as Gauss-Jordan elimination

The Effect of Exposure to Lentinula edodes and Flammulina velutipes on the Biodegradation of Crystallized Polylactic Acid

Crystallized polylactic acid (CPLA) is a prevalent bioplastic which, despite being biodegradable, is extremely resistant to decomposition in natural environments. Thus, CPLA waste contamination is rapidly becoming a prevalent issue. Lentinula edodes and Flammulina velutipes are two widespread white-rot fungi whose capabilities for bioplastic degradation have not been examined. This study aimed to determine a natural approach for reducing plastic waste by evaluating the ability of L. edodes and F. velutipes to degrade CPLA bioplastic. It was hypothesized that CPLA samples would decrease in mass when individually treated with L. edodes and F. velutipes due to their similarities to previously-examined fungi such as C. versicolor and P. chrysosporium (Arikan & Bilgen, 2019; Roldán-Carrillo et al., 2003). To assess this hypothesis, 180 discs 6 mm in diameter were cut from Simple Truth™ CPLA knives and submerged in potato dextrose broth (PDB), L. edodes liquid culture, and F. velutipes liquid culture for 23 days. The initial and final masses were used to calculate the percent change in mass. An ANOVA test showed that the p-value of 0.3234 was greater than the alpha value of 0.05. Hence, the results were not significant and the hypothesis was not supported. Therefore, bioplastic waste accumulation, particularly CPLA waste, remains a prevalent issue as the material is predominantly unaltered by fungal biodegradation within 23 days

The Effect of Time When Zinc(II) is Placed in Soil on the Ability of Chlamydomonas reinhardtii to Bioremediate Urban Soils

Heavy metal pollutants have been found in soil, causing the area to become unsustainable by destroying pre-existing ecosystems. However, cyanobacteria, specifically C. reinhardtii, have shown promise in revitalizing polluted areas via biosorption. The purpose of this experiment was to determine whether the initial presence of cyanobacteria or the introduction of cyanobacteria as a new factor after contamination would be more effective in the reduction of heavy metal concentration and toxicity in the environment. It was hypothesized that if Zn2+ was placed in urban soil after C. reinhardtii was added and before C. reinhardtii was added, more biosorption would take place when C. reinhardtii was placed before Zn2+ is added. Soil samples with no C. reinhardtii added, Zn2+ added first, and C. reinhardtii added first were tested over a 12-day period. The sample with no C. reinhardtii added had a mean Zn2+ concentration of 292 parts per million (ppm), C. reinhardtii added first had a mean concentration of 62 (ppm); and Zn2+ added first had a concentration of 84 ppm.The results of this experiment suggests that there is a significant difference in the order of addition of C. reinhardtii, between-groups d.f.=2, within-groups d.f.=87, F=1749.86, p\u3c.00001. The null hypothesis was rejected, thus having cyanobacteria present beforehand may have a greater chance of preventing heavy metal toxicity in soil

The Effect of the Addition of Carrot Seed Oil and Raspberry Seed Oil on the UV Intensity of a Sunscreen

With continuous developments in research indicating the potential health risks of chemical sunscreen filters, the novel approach of incorporating natural oils with UV light blocking properties has been explored. Natural oils extracted from fruit and vegetables have been proven to possess photoprotective properties, but their effectiveness in combination with synthetic sunscreen filters has not been as thoroughly investigated. The purpose of this study was to determine whether the addition of carrot and raspberry seed oil to an SPF 30 sunscreen would decrease the UV intensity of UVB light passing through the sunscreen and oil mixture. It was hypothesized that if both carrot seed oil and raspberry seed oil were added to a sunscreen, then the UV intensities would be lower for the sunscreen mixtures with carrot and raspberry seed oil compared to the mixture without each oil, because these oils contain high concentrations of anti-aging polyphenols. Three different sunscreen mixtures were made: one with no oils added, one with raspberry seed oil, and one with carrot seed oil. Using a UVB light and UV sensor, the UV intensity (mW/m2) of the UVB light as it passed through each mixture was measured. The results of a one-way ANOVA test with an alpha value of 0.05 suggest there were significant differences between the control group and each of the two experimental groups, (ANOVA[F(2, 135) = 222.95, p \u3c 0.001]). Thus, it was concluded that there was sufficient evidence to suggest that there was a difference in UV intensity between the mixture without oils and the two individual mixtures with the oils