Informal Science Pedagogical Innovation to Promote Understanding of Technology Application in Fluids Mechanics

This study attempts to support the preparation, setup and implementation of innovation for informal science learning that help students with technology application in physics topic. There are 349 students were selected as the sample of this study, in two STEM activities during International Scout STEM Camporee. Following the module developed for informal learning activity, the data collection focus using a survey focus on the learning opportunities created by the module for technology application. Teaching technology application in formal setting is known challenging due to syllabus con-straints. Here, fluid mechanics is a branch of physics knowledge that relates directly with the technology application. The innovation in the informal sci-ence learning module has a specific focus on the technology application of fluid mechanic. Post and pre-test were conducted. The post-test was con-ducted to investigate prior understanding of fundamental fluid mechanic concepts during the designing stage with the students’ ability to make infer-ence. Descriptive analysis and Pearson correlation were used as the data analysis methods. The finding shows that most of the students did have an initial idea of science concepts such as buoyancy, density, weight of mini Titanic, and Archimedes’ principle. However, their ability to make inference and observation is weakly correlated and not significant. This findings show that students are capable to produce observations and inferences when technology application is brought into discussion during informal science learning. As a conclusion, for informal science learning, it is important for educators to consider the outcomes for students’ learning because the opportunities for students to tap into their prior knowledge from self-determination can be easily achieved within this setting. This study proposes important measures during informal science learning environment to maximize the learning opportunities.

Assessing the Value and Balance of In-Class Demos and Videos in Fluid Mechanics Lecture

The use of in-class demonstrations and videos in an introductory fluid mechanics can have many positive outcomes in regards to student learning and engagement. However, the face-to-face class time an instructor has during lecture is a valuable commodity which can disappear all too quickly given the amount of topics to be covered, example problems, exams, etc. Thus, there is a balance to be struck amongst the various in-class activities, which must also factor in the amount of preparation time demanded of the instructor. This paper examines the utility of in-class demonstrations and videos using student surveys and feedback from both the instructor and students. Survey results reveal that students see the benefits of videos and in-class demonstrations differently and generally agree on the balance to be struck in the how class time is allocated. The results further reveal how students view in-class time in light of the opportunity to have a flipped, or partially flipped course whereby students watch lecture content outside of class. Student recall for relevant fluid mechanic concepts highlighted during demos is discussed. Additionally, the paper describes some of the specific fluid mechanics demonstrations and videos while providing references to other resources.

Fluid-mechanic model for fabrication of nanoporous fibers by electrospinning

A charged jet in the electrospinning process for fabrication of nanoporous fibers is studied theoretically. A fluid-mechanic model considering solvent evaporation is established to research the effect of solvent evaporation on nanopore structure formation. The model gives a powerful tool to offering in-depth physical under-standing and controlling over electrospinning parameters such as voltage, flow rate, and solvent evaporation rate.

Computational cell analysis for label-free detection of cell properties in a microfluidic laminar flow

This method utilizes machine learning algorithms and fluid mechanic properties of cells in a microfluidic channel to find the compositions of cell mixtures.