
There are a number of gas ionization sensors using carbon nanotubes as cathode or anode. Unfortunately, their applications are greatly limited by their multi-valued sensitivity, one output value corresponding to several measured concentration values. Here we describe a triple-electrode structure featuring two electric fields with opposite directions, which enable us to overcome the multi-valued sensitivity problem at 1 atm in a wide range of gas concentrations. We used a carbon nanotube array as the first electrode, and the two electric fields between the upper and the lower interelectrode gaps were designed to extract positive ions generated in the upper gap, hence significantly reduced positive ion bombardment on the nanotube electrode, which allowed us to maintain a high electric field near the nanotube tips, leading to a single-valued sensitivity and a long nanotube life. We have demonstrated detection of various gases and simultaneously monitoring temperature, and a potential for applications.
Ions, Nanotubes, Carbon, Temperature, Electrochemical Techniques, Ethylenes, Article, Oxygen, Sulfur Dioxide, Gases, Electrodes, Hydrogen
Ions, Nanotubes, Carbon, Temperature, Electrochemical Techniques, Ethylenes, Article, Oxygen, Sulfur Dioxide, Gases, Electrodes, Hydrogen
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 38 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
