Yuemei Zhang | PubMed Health

PubMed2006

Boron-doped carbon nanotubes serving as a novel chemical sensor for formaldehyde.

To search for a novel sensor to detect the presence of formaldehyde (HCOH), we investigate reactivities of the intrinsic and boron-doped (B-doped) single-walled (8, 0) carbon nanotube (SWCNT) with HCOH using density functional theory calculations. Compared with the intrinsic SWCNT, the B-doped SWCNT presents high sensitivity to HCOH. This is attributed to the strongly chemical interaction between the electron-rich oxygen atom of HCOH and the electron-scarce boron atom of the doped SWCNT. B-doped SWCNTs are expected to be a potential candidate for detecting the presence of HCOH.

Journal Article↗

PubMed2006

Novel chemical sensor for cyanides: boron-doped carbon nanotubes.

Boron-doped (B-doped) single-walled (8, 0) carbon nanotubes (SWCNTs) are investigated using density functional theory (DFT) calculations as sensor models to detect the presence of cyanides, such as hydrogen cyanide (HCN) and cyanogen chloride (CNCl). Comparing the results of the intrinsic SWCNTs with HCN and CNCl, we discover that B-doped SWCNTs present a high sensitivity to the gaseous cyanide molecules, which is indicated by optimized geometry and electronic properties of these systems. On the basis of calculated results, we call attention to the fact that B-doped SWCNTs would be potential candidates for the detection of gaseous cyanide molecules. The present results provide guidance to experimental scientists in developing CNT-based chemical sensors.

Journal Article↗

PubMed2005

An intelligent tutoring system that generates a natural language dialogue using dynamic multi-level planning.

OBJECTIVE: The objective of this research was to build an intelligent tutoring system capable of carrying on a natural language dialogue with a student who is solving a problem in physiology. Previous experiments have shown that students need practice in qualitative causal reasoning to internalize new knowledge and to apply it effectively and that they learn by putting their ideas into words. METHODS: Analysis of a corpus of 75 hour-long tutoring sessions carried on in keyboard-to-keyboard style by two professors of physiology at Rush Medical College tutoring first-year medical students provided the rules used in tutoring strategies and tactics, parsing, and text generation. The system presents the student with a perturbation to the blood pressure, asks for qualitative predictions of the changes produced in seven important cardiovascular variables, and then launches a dialogue to correct any errors and to probe for possible misconceptions. The natural language understanding component uses a cascade of finite-state machines. The generation is based on lexical functional grammar. RESULTS: Results of experiments with pretests and posttests have shown that using the system for an hour produces significant learning gains and also that even this brief use improves the student's ability to solve problems more then reading textual material on the topic. Student surveys tell us that students like the system and feel that they learn from it. The system is now in regular use in the first-year physiology course at Rush Medical College. CONCLUSION: We conclude that the CIRCSIM-Tutor system demonstrates that intelligent tutoring systems can implement effective natural language dialogue with current language technology.

Artificial Intelligence↗