The authors looked into eco-friendly alternatives for insulating material. They ultimately found that a polyurethane derived from eggshells was an effective insulator and further research into it is warranted.
Read More...Investigating sustainable insulation materials: Analysis of biofoams and petroleum-derived foams
The authors looked into eco-friendly alternatives for insulating material. They ultimately found that a polyurethane derived from eggshells was an effective insulator and further research into it is warranted.
Read More...Citrate and lactate drive glioblastoma progression via activation of tumor-associated macrophages
The authors looked at the impact of citrate and lactate on glioblastoma progression. Their results provide important insights for future immunotherapies aimed at treating glioblastoma.
Read More...The optimization of high-protein duckweed cultivation in eutrophicated water with mutualistic bacteria
he rapid growth of the human population is driving food crises in Thailand and Southeast Asia, while contributing to global food insecurity and a larger carbon footprint. One potential solution is cultivating duckweed (Wolffia globosa) for consumption, as it grows quickly and can provide an alternative protein source. This research explored two methods to optimize duckweed cultivation: using phosphorus- and nitrogen-rich growing media and plant growth-promoting bacteria (PGPB).
Read More...Utilizing sorbitol to improve properties of cellulose-based biodegradable hydrogels
Hydrogels are commonly used in medicine, pharmaceuticals, and agriculture. Hydrogels absorb water by swelling and re-release this water by diffusion. This study sought to synthesize a biodegradable, cellulose-based hydrogel that is more effective at absorbing and re-releasing water than those produced by current methods. We tested the compressive strength of both the dry and swollen gels and the tensile strength of the swollen gels to elucidate the gel structure.
Read More...Identifying factors, such as low sleep quality, that predict suicidal thoughts using machine learning
Sadly, around 800,000 people die by suicide worldwide each year. Dong and Pearce analyze health survey data to identify associations between suicidal ideation and relevant variables, such as sleep quality, hopelessness, and anxious behavior.
Read More...Investigating Lemna minor and microorganisms for the phytoremediation of nanosilver and microplastics
The authors looked at phytoremediation, the process by which plants are used to remove pollutants from our environment, and the ability of Lemna minor to perform phytoremediation in various simulated polluted environments. The authors found that L. minor could remove pollutants from the environment and that the addition of bacteria increased this removal.
Read More...Suppress that algae: Mitigating the effects of harmful algal blooms through preemptive detection & suppression
A bottleneck in deleting algal blooms is that current data section is manual and is reactionary to an existing algal bloom. These authors made a custom-designed Seek and Destroy Algal Mitigation System (SDAMS) that detects harmful algal blooms at earlier time points with astonishing accuracy, and can instantaneously suppress the pre-bloom algal population.
Read More...Building an affordable model wave energy converter using a magnet and a coil
Here, seeking to identify a method to locally produce and capture renewable energy in Hawai'i and other island communities, the authors built and tested a small-scale model wave energy converter. They tested various configurations of a floated magnet surrounded by a wire coal, where the motion of the magnet due to a wave results in induction current in the coil. While they identified methods to increase the voltage and current generated, they also found that corrosion results in significant deterioration.
Read More...Evaluating machine learning algorithms to classify forest tree species through satellite imagery
Here, seeking to identify an optimal method to classify tree species through remote sensing, the authors used a few machine learning algorithms to classify forest tree species through multispectral satellite imagery. They found the Random Forest algorithm to most accurately classify tree species, with the potential to improve model training and inference based on the inclusion of other tree properties.
Read More...Machine learning-based enzyme engineering of PETase for improved efficiency in plastic degradation
Here, recognizing the recognizing the growing threat of non-biodegradable plastic waste, the authors investigated the ability to use a modified enzyme identified in bacteria to decompose polyethylene terephthalate (PET). They used simulations to screen and identify an optimized enzyme based on machine learning models. Ultimately, they identified a potential mutant PETases capable of decomposing PET with improved thermal stability.
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