Graphene growth
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This is a presentation detailing the goals of my research. In this lab, I am responsible for growing samples of graphene on a variety of different substrates and materials. I included some of the images taken from the microscope. I hope to include images in future presentations from the Raman spectroscopy to show the confirmation of graphene growth. This is all as a part of the STEM Teacher and Researcher fellowship program. I am placed at the Quantum Materials and Nanodevice Laboratory at San Francisco State University.
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Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
Applied Science: Thermodynamics, Laws & Methodology.pdf
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
bordetella pertussis.................................ppt
Bordettela is a gram negative cocobacilli spread by air born drop let
SAR of Medicinal Chemistry 1st by dk.pdf
In this presentation include the prototype drug SAR on thus or with their examples .
Syllabus of Second Year B. Pharmacy
2019 PATTERN.
Unlocking the mysteries of reproduction: Exploring fecundity and gonadosomati...
The pygmy halfbeak Dermogenys colletei, is known for its viviparous nature, this presents an intriguing case of relatively low fecundity, raising questions about potential compensatory reproductive strategies employed by this species. Our study delves into the examination of fecundity and the Gonadosomatic Index (GSI) in the Pygmy Halfbeak, D. colletei (Meisner, 2001), an intriguing viviparous fish indigenous to Sarawak, Borneo. We hypothesize that the Pygmy halfbeak, D. colletei, may exhibit unique reproductive adaptations to offset its low fecundity, thus enhancing its survival and fitness. To address this, we conducted a comprehensive study utilizing 28 mature female specimens of D. colletei, carefully measuring fecundity and GSI to shed light on the reproductive adaptations of this species. Our findings reveal that D. colletei indeed exhibits low fecundity, with a mean of 16.76 ± 2.01, and a mean GSI of 12.83 ± 1.27, providing crucial insights into the reproductive mechanisms at play in this species. These results underscore the existence of unique reproductive strategies in D. colletei, enabling its adaptation and persistence in Borneo's diverse aquatic ecosystems, and call for further ecological research to elucidate these mechanisms. This study lends to a better understanding of viviparous fish in Borneo and contributes to the broader field of aquatic ecology, enhancing our knowledge of species adaptations to unique ecological challenges.
Authoring a personal GPT for your research and practice: How we created the Q...
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
8.Isolation of pure cultures and preservation of cultures.pdf
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Graphene growth
- 2. Goals - Uniformity Fluid experiment (monolayer Cu balls and tubes) Smoother surface Faster flow rate Less drag Lithium ion battery (multilayer Ni foils) Sandwiched between SEI and current collector Hold energy better 10x faster charge time
- 3. Goals - Uniformity Monolayer Graphene Much easier because of the nature of the material Bilayer Graphene (thickness < 1 nm) Gas-sensing properties Could be better at managing heat of high-power electrical devices Appropriate number of layers balances electrical conductivity with transparency
- 4. Process Initial cleaning – copper items DI water, acetone, repeat Ammonium persulfate bath Tubes – sonicate, then electropolish Initial cleaning – nickel items DI water, acetone, isopropanol
- 5. Process Pressure must be at 50 torr to open vacuum tube and mount sample Flushing the system 100 sccm of methane for 10 minutes 100 sccm of hydrogen for 10 minutes 100 sccm of argon for 10 minutes
- 6. Process Oxide removal in furnace 35 sccm of hydrogen Temperatures Nickel – 1000°C Copper - 1035°C Cooking times Foils and nickel foam: 70 minutes Copper tubes and ball: 50 minutes
- 7. Process – Methane Exposure Ni foil (few layer) CH4/H2 – 35/2 sccm 30 minutes Ni foam CH4/H2 – 35/2 sccm 5 minutes
- 8. Nickel foil and foam – 20x
- 9. Process – Methane Exposure Copper balls and tubes CH4/H2 – 40/15 sccm 30 minutes
- 10. Copper ball and tube control – 20x Before cleaning
- 11. Copper ball and tube with graphene? Tube, 100x Ball, 20x Tube, 20x
- 12. Process – Methane Exposure Monolayer Graphene on Cu foil CH4/H2 – 35/2 sccm 30 minutes Bilayer Graphene on Cu foil CH4/H2 – 16/70 sccm 120 minutes
- 13. Copper foil Monolayer, 20x Bilayer, 100x
- 14. Future Goals Confirm graphene growth on Cu tubes and balls Uniform samples on Ni foil and foam
- 15. Sources http://physicsworld.com/cws/article/news/2010/may/12/few-layer-graphene- keeps-its-cool http://www.crcnetbase.com/doi/abs/10.1201/b19461-35 “Thermal properties of graphene and few-layer graphene: applications in electronics” http://ndl.ee.ucr.edu/IET-Review-2015.pdf