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71 results found

Per- and polyfluoroalkyl substances (PFAS) Interferents List

Data provided by  National Institute of Standards and Technology

A table of known interferences for per- and polyfluoroalkyl substances using mass spectrometry.

Tags: per- and polyfluoroalkyl substances,PFAS,mass spectrometry,analytical chemistry,high-resolution mass spectrometry,HRMS,LC-MS/MS,

Modified: 2024-02-22

Views: 0

Database Infrastructure for Mass Spectrometry - Per- and Polyfluoroalkyl Substances

Data provided by  National Institute of Standards and Technology

Data here contain and describe an open-source structured query language (SQLite) portable database containing high resolution mass spectrometry data (MS1 and MS2) for per- and polyfluorinated alykl substances (PFAS) and associated metadata regarding their measurement techniques, quality assurance metrics, and the samples from which they were produced. These data are stored in a format adhering to the Database Infrastructure for Mass Spectrometry (DIMSpec) project. That project produces and uses databases like this one, providing a complete toolkit for non-targeted analysis.

Tags: per- and polyfluoroalkyl substances,PFAS,database,mass spectrometry,SQLite,emerging contaminants,high resolution mass spectrometry,non-targeted analysis,NTA,HRMS,

Modified: 2024-02-22

Views: 0

Sim-PROCESD: Simulated-Production Resource for Operations and Conditions Evaluation to Support Decision-making

Data provided by  National Institute of Standards and Technology

Sim-PROCESD is a discrete event simulation package written in Python that is designed to model the behavior of discrete manufacturing systems. Specifically, it focuses on asynchronous production lines. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Sim-PROCESD provides class definitions for manufacturing devices/components that can be configured by the user to model various real-world manufacturing systems.

Tags: discrete-event simulation,manufacturing,production,maintenance,python,

Modified: 2024-02-22

Views: 0

Microplastic and nanoplastic chemical characterization by thermal desorption and pyrolysis mass spectrometry with unsupervised machine learning

Data provided by  National Institute of Standards and Technology

This data publication contains the mass spectrometry chemical characterization of microplastic and nanoplastic chemical analysis. The data from this study includes mass spectra of pure, mixed, and weathered microplastics and nanoplastics at high and low fragmentation, extracted ion chronograms, Kendrick mass defect plots, code, and the derived and processed data. The data analysis code (MATLAB 2022a*) used for unsupervised learning of cluster and compositional relationships is also included.

Tags: Microplastic,Nanoplastics,environment,mass spectrometry,GC-MS,Chemical Characterization,machine learning,

Modified: 2024-02-22

Views: 0

Thermal Drift Monitoring Experiment 01

Data provided by  National Institute of Standards and Technology

An experiment was set up within a machine tool at the National Institute of Standards and Technology (NIST) to test vision-based thermal drift tracking methods. A wireless microscope within a tool holder in the spindle is used to capture videos of image targets attached to the worktable. For each target, one video is captured during spindle rotation orthogonal to the worktable and another video is captured during axis translation orthogonal to the worktable.

Tags: Thermal error,machine tool,monitoring,manufacturing,Microscope,

Modified: 2024-02-22

Views: 0

FCpy: Feldman-Cousins Confidence Interval Calculator

Data provided by  National Institute of Standards and Technology

Python scripts and Python+Qt graphical user interface for calculating Feldman-Cousins confidence intervals for low-count Poisson processes in the presence of a known background and for Gaussian processes with a physical lower limit of 0.

Tags: python,SIMS,statistics,mass spectrometry,Confidence Interval,CI,Feldman,Cousins,Poisson,Gaussian,

Modified: 2024-02-22

Views: 0

Cost Assessment Tool for Sustainable Manufacturing (CATS)

Data provided by  National Institute of Standards and Technology

This tool uses techniques from ASTM E3200 for evaluating manufacturing investments from the perspective of environmentally sustainable manufacturing by pairing economic methods of investment analysis with environmental aspect of manufacturing. The economic techniques used include net present value, internal rate of return, payback period, and hurdle rate. These four techniques are deterministic, meaning that they deal with known values that are certain. The tool also conducts a sensitivity analysis using Monte Carlo techniques.

Tags: investment analysis,manufacturing,net present value,internal rate of return,environmental impact,sustainability,

Modified: 2024-02-22

Views: 0

Supplemental Data and Source Code for ILSA Research

Data provided by  National Institute of Standards and Technology

Source code associated with the Inverted Library Search Algorithm (ILSA) for identifying mixture components using is-CID mass spectra. This source code is frozen to accompany the manuscript titled "Updates to the Inverted Library Search Algorithm for mixture analysis" by Moorthy et. al.

Tags: mass spectrometry,Mixture Analysis,Search Algorithms,seized drug analysis,

Modified: 2024-02-22

Views: 0

Linear Axis Testbed at IMS Center - Run-to-Failure Experiment 01

Data provided by  National Institute of Standards and Technology

A linear axis testbed at the Center for Intelligent Maintenance Systems (IMS Center) at the University of Cincinnati was run to failure (the detection of backlash) over one year with periodic data collected from an inertial measurement unit (IMU) on the carriage, two triaxial accelerometers on the ball nut, and the controller.

Tags: manufacturing,Industry 4.0,smart manufacturing,linear axis,machine tool,ball screw,backlash,sensor,accelerometer,inertial measurement unit,IMU,error motion,data analysis,monitoring,diagnostics,

Modified: 2024-02-22

Views: 0

Simantha: Simulation for Manufacturing

Data provided by  National Institute of Standards and Technology

Simantha is a discrete event simulation package written in Python that is designed to model the behavior of discrete manufacturing systems. Specifically, it focuses on asynchronous production lines with finite buffers. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Classes for five basic manufacturing objects are included: source, machine, buffer, sink, and maintainer. These objects can be defined by the user and configured in different ways to model various real-world manufacturing systems.

Tags: discrete-event simulation,manufacturing,production,maintenance,python,

Modified: 2024-02-22

Views: 0