Data and Code for Spin and Orbital Spectroscopy in the Absence of Coulomb Blockade in Lead Telluride Nanowire Quantum Dots

This repository contains combined data for two papers. Growth of PbTe nanowires by Molecular Beam Epitaxy<br> Authors: Sander G. Schellingerhout, Eline J. de Jong, Maksim Gomanko, Xin Guan, Yifan Jiang, Max S.M. Hoskam,<br> Sebastian Koelling, Oussama Moutanabbir, Marcel A. Verheijen, Sergey M. Frolov, Erik P.A.M. Bakkers <br> Spin and Orbital Spectroscopy in the Absence of Coulomb Blockade in Lead Telluride Nanowire Quantum Dots<br> Authors: M. Gomanko, E.J. de Jong, Y. Jiang, S.G. Schellingerhout, E.P.A.M. Bakkers, and S.M. Frolov <br> Content of this repository: Readme file. /RawData/<br> Original data obtained at the time of measurement separated into 3 folders from different chips/cooldowns<br> Data from devices 1,3 and 4 can be found in "RawData/PbTe_chip1", device 2 in "RawData/PbTe_chip2" and devices 5-8 in "RawData/PbTe_GBg" /Measurement notebooks/<br> OneNote notebook with 4 different sections for 3 chips (two sections for backgate chip). Pages in these sections contain the device number in the name.<br> Also, the same notebook is exported in pdf format for convenience. /Data processing/<br> Readme file, Jupiter notebooks, data files, and pictures, that were used to extract g-factors for different field orientations in device2. /Data summaries/<br> Powerpoints, that were used to overview data during the measurement stage.<br> Not all data from these powerpoints are present in the repository or paper (specifically excluding early data used in "additional backgate devices.pptx" and "PbTe 3rd device.pptx"). Data file types: data_NNN.dat - the original data file obtained at the time of the experiment<br> dataNNN.py - the original QTLab data acquisition script saved with data<br> data_NNN.set - settings of measurement instruments at the time of measurement<br> data_NNN.meta - auxillary file necessary for plotting data using SpyView (see below) <br> data_NNN.MTX - a simple 2D/3D matrix format developed for Spyview NNN stands for dataset number, automatically indexed by QTLab <br> How to plot data: 1) Spyview - a free data plotting program written by Gary Steele Data in this repository can be simply dropped into Spyview for plotting. Spyview also produces and can read .mtx files which are available for some of the data in this repository. https://nsweb.tn.tudelft.nl/~gsteele/spyview/ <br> 2) QTPlot - a Python plotter written by Ruben van Gulik Data in this repository can be directly opened with QTPlot, which will read axis labels. https://github.com/Rubenknex/qtplot Note: requires PyQT4本仓库收录两篇学术论文的整合实验数据集。 第一篇论文：《分子束外延（Molecular Beam Epitaxy）生长碲化铅纳米线》，作者：Sander G. Schellingerhout、Eline J. de Jong、Maksim Gomanko、Xin Guan、Yifan Jiang、Max S.M. Hoskam、Sebastian Koelling、Oussama Moutanabbir、Marcel A. Verheijen、Sergey M. Frolov、Erik P.A.M. Bakkers 第二篇论文：《碲化铅纳米线量子点无库仑阻塞（Coulomb Blockade）效应下的自旋与轨道光谱学》，作者：M. Gomanko、E.J. de Jong、Y. Jiang、S.G. Schellingerhout、E.P.A.M. Bakkers、S.M. Frolov 本仓库包含以下内容： 1. README说明文件。 2. `/RawData/` 文件夹： 测量阶段获取的原始数据按不同芯片/冷却批次划分为3个子文件夹。其中，器件1、3和4的数据存储于"RawData/PbTe_chip1"，器件2的数据存储于"RawData/PbTe_chip2"，器件5至8的数据存储于"RawData/PbTe_GBg"。 3. `/Measurement notebooks/` 文件夹： 包含一个OneNote笔记本，针对3个实验芯片设有4个分区（其中背栅芯片占用2个分区），各分区内的页面名称均包含对应器件编号。为便于查阅，该笔记本同时导出为PDF格式。 4. `/Data processing/` 文件夹： 包含用于提取器件2中不同磁场取向下g因子（g-factor）的README文件、Jupyter笔记本、数据文件及可视化图片。 5. `/Data summaries/` 文件夹： 包含用于实验阶段数据复盘的PowerPoint演示文稿。并非所有演示文稿中的数据均收录于本仓库或对应论文中，特别排除了"additional backgate devices.pptx"与"PbTe 3rd device.pptx"中使用的早期实验数据。 数据文件格式说明如下： - `data_NNN.dat`：实验测量时直接获取的原始数据文件 - `dataNNN.py`：随原始数据一同保存的QTLab数据采集脚本 - `data_NNN.set`：测量时刻度仪器的配置参数文件 - `data_NNN.meta`：使用SpyView绘制数据所需的辅助元文件 - `data_NNN.MTX`：为SpyView开发的简易2D/3D矩阵格式文件。其中`NNN`为数据集编号，由QTLab自动生成索引。 数据可视化方法如下： 1. SpyView：由Gary Steele开发的免费数据绘图软件。本仓库中的数据可直接拖拽至SpyView中完成绘图。SpyView支持生成并读取`.mtx`格式文件，本仓库部分数据已提供该格式文件。官方下载地址：https://nsweb.tn.tudelft.nl/~gsteele/spyview/ 2. QTPlot：由Ruben van Gulik开发的Python绘图工具。本仓库中的数据可直接通过QTPlot打开，其可自动识别并读取坐标轴标签。官方仓库地址：https://github.com/Rubenknex/qtplot。注意：该工具依赖PyQT4库。