Cnt Forest Trimming

CNT forest trimming is an engineering process for selectively reducing or shaping vertically aligned carbon nanotube forests, which are dense arrays of nanotubes grown from a substrate. The process removes selected portions of the forest, typically through controlled etching or other localized material-removal methods, to adjust nanotube height, geometry, and surface profile while preserving the underlying array. By tuning these structural features, researchers can tailor electrical, thermal, mechanical, optical, and interfacial properties. CNT forest trimming therefore supports the fabrication of microscale and nanoscale devices, including sensors, actuators, electrodes, thermal interfaces, and patterned materials for advanced engineering research.

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Education

JoVE Core - Statistics

Trimmed Mean

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2023

While measuring the mean of a data set, care needs to be taken when associating the mean to its central tendency. The same goes for the arithmetic mean, the geometric mean, or the harmonic mean. This is because the presence of a single outlier data value can significantly affect the mean. That is, the mean is sensitive to fluctuations in the data set. Although certain measures of central tendency are not sensitive to outliers, there are alternative versions of the mean that get around the...

Research

JoVE Journal - Environment
Free Sample

Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils

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Cited by 19 •

2016

Repeated soil sampling has recently been shown to be an effective way to monitor forest soil change over years and decades. To support its use, a protocol is presented that synthesizes the latest information on soil resampling methods to aid in the design and implementation of successful soil monitoring programs.

Research

JoVE Journal - Environment
Free Sample

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

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Cited by 1 •

2025

Recent advancements in remotely piloted aircraft systems (RPAS) allow sub-meter resolution, ideal for forest recovery monitoring. Integrating artificial intelligence (AI) enables deeper insights from large remotely sensed datasets. This protocol improves monitoring by supporting more efficient assessment and management of forested lands recovering from disturbance.

Precision Milling of Carbon Nanotube Forests Using Low Pressure Scanning Electron Microscopy

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2017

Low pressure scanning electron microscopy in a water vapor ambient is used to machine nanoscale to microscale features in carbon nanotube forests.

Simulating Impacts of Ice Storms on Forest Ecosystems

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Cited by 6 •

2020

Ice storms are important weather events that are challenging to study because of difficulties in predicting their occurrence. Here, we describe a novel method for simulating ice storms that involves spraying water over a forest canopy during sub-freezing conditions.

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