Development of a Teaching Platform about Plasmonics Based on the Color Perception of Colloidal Gold

Abstract

In contrast to a macroscopic solid, the color and spectroscopic properties of gold nanoparticles change with size. Here, we propose a teaching platform for high school students (hereafter defined as students, attending secondary school in Europe, preparatory high schools in the US, or any educational institution of a comparable level) that explains the relationship between the perceived color of colloidal solutions and their physicochemical properties. For this aim, an open-source code, based on Mie theory and colorimetry, was developed to link the perceived colors to the plasmonic effects. Moreover, a set of such nanoparticles, varied in diameter, was created to support the theoretical description. The employed seed-mediated synthesis approach provides sufficient spherical roundness that is crucial for color matching. The proposed teaching platform is available online and can be linked to an outreach activity or practical laboratory course. Furthermore, we illustrate the linkage of nanoscience concepts to real-world examples (e.g., stained church glass), which can be used for promoting the potential of nanotechnology in other fields, including electronics, optics, and biomedicine adding to the interdisciplinarity of the teaching platform.

Design of Multimedia Intelligent Teaching Platform for Preschool Curriculum Based on Edge Computing

“…It can be seen from Figure 1 that the teaching platform under edge computing must always run through the teaching strategy of strengthening the communication between teachers and students in the design process and make the best use of the existing foundation to build the teaching platform. Teachers do not need to know more about the technical details of the platform and devote all their energy to the multimedia intelligent teaching of children's courses to cultivate children's learning ability [15,16]. Teachers and parents can interact with each other through the platform.…”

Synthesis and Characterization of Superparamagnetic Iron Oxide Nanoparticles: A Series of Laboratory Experiments

“…There are several examples of inquiry-based undergraduate teaching laboratories exploring fundamental nanoengineering concepts − and training students in various nanocharacterization techniques. , One family of nanomaterials commonly incorporated into teaching laboratories is optically responsive nanoparticles, including gold nanoparticles − and fluorescent quantum dots. ,, While these materials serve as an ideal platform for exploring complex quantum concepts, such as the discretization of energy states , and manipulation of light at the nanoscale, , the notable properties of these nanoparticles are limited to geometry and optical response. In contrast, nanomaterials can demonstrate a plethora of behaviors, including mechanical, optical, ,, electrical, chemical, and more. − One unique nanomaterial is iron oxide nanoparticles.…”

Validating and Utilizing Machine Learning Methods to Investigate the Impacts of Synthesis Parameters in Gold Nanoparticle Synthesis

“…For many applications, the control of optical properties, size, and shape of gold nanoparticles is crucial to ensure proper processing and applicability. − However, gold nanoparticle synthesis is complex, as it is highly dependent on various synthesis parameters that have interdependent effects on each other. Shape and size control can be achieved by various metrics, such as the type and concentration of reducing agent, amount of gold precursor in relation to reducing agent or seed amount, or shape directing agents. , …”

“…1−3 However, gold nanoparticle synthesis is complex, as it is highly dependent on various synthesis parameters that have interdependent effects on each other. Shape and size control can be achieved by various metrics, such as the type and concentration of reducing agent, 4 amount of gold precursor in relation to reducing agent 5 or seed amount, 6 or shape directing agents. 7,8 To navigate and understand this complex synthesis space, machine learning offers promising tools to learn and explain the underlying concepts.…”