11 - 20 of 236 results for: MS

This mini-course offers a series of lectures and hands-on labs to discuss the development and reporting of reproducible quantitative LC-MS/MS molecular assays using a triple quadrupole. We will discuss chromatography and mobile phase selection, mass spectrometry parameters and selection of fragment ions, and the reporting and interpretation of published methods. Additionally we will cover the use of internal standards and sample preparation, and normalization methods for reproducible data analysis. Students will have the opportunity to work with a mass spectrometer and will design a cohesive plan for a targeted assay of an example molecule in their research.

Analysis of volatile and semi-volatile compounds provides a fast and powerful tool to understand how chemical profiles of organisms change with specific conditions. Gas chromatography coupled to mass spectrometry (GC-MS) can be used to separate and characterize chemical profiles of different organisms, extracted in vivo or preserved in tissue samples. However, tools to analyze reproducible datasets have advanced only in the last decade. In this course, students will learn how to design extraction procedures aligned to specific biological questions, and receive hands-on experience analyzing GC-MS data using the Global Natural Products Social Molecular Networking (GNPS) analysis infrastructure, a free open-source tool.

Teaches principles and methods for quantitative modeling and mitigation of risks in project planning, design, construction and operation, using new MS Excel capabilities and standardized probability distributions. Several case studies will be covered, including ongoing work with PG&E to roll up operational risks.

Directed study for CEE-MS students on subjects of mutual interest to students and faculty. Student must obtain faculty sponsor.

Policymakers rely on quantitative systems models to inform decision-making about environmental policy design, infrastructure development, and resource allocation. However, many rapid, transformational changes in the climate and socioeconomic systems are difficult to predict and quantify in models. Therefore, reliance on traditional model-based decision analysis can leave policymakers vulnerable to unforeseen risks. In this class, students will learn quantitative methods for addressing deep uncertainties using systems modeling, enabling them to identify potential vulnerabilities and design decision policies that are robust and resilient to a wide range of uncertain futures. Drawing on tools in simulation, optimization, and machine learning, specific methods include: exploratory modeling, scenario discovery, robust decision making, and adaptation pathways. We will demonstrate these approaches in a range of sustainability domains such as water resources, agriculture, and energy systems. Students will complete Python-based modeling assignments, read contemporary journal articles, and develop a research proposal. Prerequisites: Prior coursework in applied optimization (e.g. CEE 266G or MS&E 211); and prior coursework in decision or policy analysis (e.g. CEE 275D or MS&E 250A or MS&E 252); and proficiency in Python programming at the level of CME 193

This is a laboratory course that serves as a stepping stone toward independent research in organic chemistry. Through several 1-2 step syntheses, this course trains students on basic organic laboratory techniques on purification of products, including extraction, distillation, recrystallization, thin layer chromatography, and column chromatography, as well as characterization of product structures using IR, GC-MS, and NMR spectroscopy. This course reviews MS, IR, and 1H and 13C NMR spectroscopy knowledge from Chem 33 and 121 with an emphasis on the practical interpretation of spectra, so that students can become independent in using these techniques to identify the purity and structures of organic compounds.Prerequisite: Chem 121. Corequisite: Chem 123.

The core objectives of the course will focus upon introducing and providing hands-on practice with analytical separation, spectroscopic identification, and calibrated quantification with strong technical communication (for the Writing-in-the-Major requirement) emphasized throughout the course. Lectures will focus on theory, and laboratory activities will provide hands-on practice with the GC, LC, XPS, ICP, MS, and UV/Vis instruments. Data analysis will be emphasized throughout the course with Python being the primary tool for plotting and computations. Statistical measurements will be introduced to gauge the quality and validity of data. Lectures will be three times a week with a required four-hour laboratory section. The course should be completed prior to CHEM courses 174,176, or 184. Prerequisite: CHEM 33 or CHEM 100; and CS 106A.

This is a discussion-based seminar focused on archaeological theory as it has evolved since the 1970s. Together we will select symptomatic readings and subject them to commentary and critique. We will map a field of basic concepts for archaeology that will include things, making, presence, history, agency, power, society, culture, place, economy, status, cognition, affect, memory, experience, (im)materiality, and more. Throughout we will be interested in relationships of concepts to methodology, disciplinary pragmatics and politics. The purpose is NOT to compile a list of theories that have been claimed to feature in the recent history of the discipline (processual, post-processual, behavioral, cognitive, new materialist, symmetrical, post-humanist, techno-scientific, whatever). Seminar lead MS has been at the forefront of every shift in archaeological thought since the 80s - as proponent, critic, and practitioner. For the last decade he has presented an annual seminar at Stanford on cutting-edge thought in archaeology, and here he is taking the opportunity to review the overall state of the discipline and related fields, to assess archaeology's fitness to address matters of common and pressing contemporary concern. Seminar members should be prepared for a roller-coaster of challenging conversation that will embrace our different standpoints and interests.

ICME Xplore is a graduate level independent study course designed to give students hands-on experience through real-world research projects and experiential activities. Matched with industry research teams and Stanford faculty tackling complex computational challenges, dynamic student teams explore preselected topics. Taking skills beyond the classroom, students apply a computational and data analytics lens while embracing design thinking methodology. Students are encouraged to try new industries and analytical techniques and develop soft skills necessary for success, such as effective team building and communication. Through active engagement, students learn to exercise creativity working on open-ended problems, identify stakeholders, scope a project toward positive impact, and recognize and mitigate ethical problems. Registration is by application only. Quarterly projects are open to current ICME (MS and PhD) and Stats students. Projects will be assigned on a rolling basis, giving priority to ICME students. New students may take this course for 3-4 units; returning students may take this course for 1-4 units.