少即是多 – 通过电子计数 EELS 减少样品辐照剂量、损伤和表征时间

获取高保真度的电子能量损失谱(EELS)一直是个难题。需要同时满足大能量范围(keV)、高能量分辨率(< eV)以及高动态范围(跨越若干数量级)的要求,这是一项重大挑战。传统上EELS 是在单个并行传感器上记录的。然而,常规探测器的动态范围有限,且在高能量分辨率下能量范围非常有限。可以通过快速连续获取两条谱线来改进这些问题,但代价是浪费施加在样品上的电子剂量(以及采集时间)。进一步扩展采集更多条谱线似乎是合乎逻辑的下一步。然而,这仅对最耐辐射的样品以及对采集时间不敏感的情况才可行。如果将此做法推至极限,我们就会回到序列 EELS 谱仪的时代,这种谱仪可以提供不错的谱线,但几乎没人真心愿意回到那个时代。

使用直接探测相机进行剂量分割的 EELS 谱成像采集

多帧累加的谱成像(SI)被证明是能够在扫描透射电子显微术(STEM)表征下同时提高谱成像分辨率和信噪比(SNR)的有效方法[1]。基于闪烁体的 CMOS 和 CCD 探测器性能不足以支持在低剂量和高速条件下的多帧电子能量损失谱(EELS)的 SI 采集,这主要是由于这类探测器的读出噪音带来的不利影响。但是,单电子直接探测的电子计数相机能够达到趋近于零噪音的读出,使得它们成为低剂量率以及低的总剂量下实现多帧 EELS SI 采集的理想选择[2]。

在本次网络研讨会中,我们将展示 Gatan 的电子计数相机和 eaSI™ 技术的的卓越能力,它们的组合使得我们能够以高至 9,000 像素/秒的速率采集 SI 数据。这类相机近乎于零的读出噪音使得超高速的采谱速率成为有效的采集方式,意味着针对所有实验流程多帧采集策略通常都能够带来高度的益处。我们能够在短短数秒之内达成大视野的 SI 帧采集,带来数据采集中更低的剂量率和高频的漂移矫正。异常或受损数据帧能够在后处理中选择丢弃,意味着我们能够在效应发生后对数据进行“剂量调整”——特别是当您还不了解对总剂量敏感的样品在何时达到关键损伤剂量的情形。

Gatan eaSI 技术实现剂量高效的 EELS 谱像采集

GIF Continuum K3 IS: Advanced direct detection for in-situ chemical analysis

Gatan DigitalMicrograph is a well-established software tool for performing in-situ (S)TEM experiments. This powerful capability has now been expanded to allow capture and analysis of in-situ EELS and spectrum imaging datasets. This video demonstrates the unique in-situ data acquisition and analysis capability of this advanced software platform when operated together with the GIF Continuum® K3® system.

Continuum IS: Versatile time-resolved data collection webinar

Gatan's latest spectrometer, the Continuum, enables a wide range of techniques for (S)TEM investigations. This versatility is enhanced further with the Continuum IS, where acquisition modes have been expanded to capture data continuously over time.

This webinar will present the numerous types of data that can be collected with the Continuum IS as well as the powerful tools we provide to process that data, including our redesigned IS player and Python scripting.

Continuously acquired 4D STEM and EELS spectrum images for in-situ microscopy webinar

In this webinar, we presented new tools for in-situ EELS spectrum imaging and in-situ 4D STEM. In addition to a simple interface for continuous acquisition with live drift correction, Gatan has developed a suite of tools for processing and visualizing these multi-dimensional datasets. While faster detectors make a continuous acquisition of analytical data feasible, these tools for the management of the resulting data make such experiments practical.

Auf den Spuren von Lithium im Mikrometerbereich

Lithium-Ionen-Batterien (Li) werden aufgrund ihrer herausragenden Energiedichte und geringen Masse für eine Vielzahl von Energiespeicheranwendungen eingesetzt. Es besteht weiterhin großes Potential zur Verbesserung der Kapazität und der Effizienz dieser Energiematerialien durch die Optimierung der verwendeten Komponenten und Materialien. Insbesondere durch den Mangel an geeigneten Charakterisierungstechniken auf der Mikro- und Nanoebene, sind die Abbaumechanismen und Strukturentwicklungen noch nicht ausreichend erforscht.

Capturing low-dose images, in-situ video, and diffraction data with the Metro counting camera

Gatan’s latest counting camera, Metro, produces excellent results at low dose rates and low to moderate accelerating voltages (60 – 200 kV). In this webinar, we show images from zeolites and MOFs, as well as diffraction patterns and 4D STEM datasets from 2D materials. We demonstrate the in-situ capabilities of the camera, which can capture video datasets at up to 41 frames per second at full 2k x 2k resolution.

3DED: A brief overview of data collection, and data analysis

The determination of precise atomic arrangements in a crystal material is the key to understanding the structure-property relationship, and it will further facilitate synthetic designs of new materials. In the past decades, the structure determination of submicron/nanometer crystals has been achieved via 3D electron diffraction (3DED). This technique is also known as continuous rotational electron diffraction (CRED) or MicroED (microcrystal electron diffraction.

DualEELS:对低能损电子能量损失谱数据校正的重要性

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