Evaluation of composition C‐4 odor signature on a novel sorption training aid substrate

The need for reliable non-detonable training aid systems within explosive canine detection applications is imperative for optimal operational performance. This study utilized Getxent polymer technology for the creation of Composition C-4 training aid material. The objectives of this research were to (1) evaluate target volatile odor concentration emitted from tubes following mass (1 g, 10 g, and 20 g) and impregnation time (24 h, 72 h, and 1 week) variations and (2) monitor training aid shelf-life over a period of up to 4 months. Solid-Phase Microextraction (SPME) coupled with Gas Chromatography/Mass Spectrometry (GC/MS) was a viable way to measure target C-4 odorants (2-ethyl 1-hexanol (2E1H), cyclohexanone, 2,3-dimethyl-2,3-dinitrobutane (DMNB)) from Getxent tubes. On average, the Getxent tubes absorbed 8% mass of total target VOCs within bulk C-4 headspace. The 20 g, 72-h impregnated Getxent tubes yielded detection of all three targeted odor volatiles while concurrently depicting the highest average mass amount of 2E1H and cyclohexanone. A shelf-life study over a four-month period depicted that DMNB yielded higher mass amounts across all impregnation and bulk mass variations while 2E1H and cyclohexanone had lower mass amounts, losing detection levels of 2E1H after a period of 3 months. The study demonstrated the capability of the Getxent tubes in the impregnation of C-4 explosive target odor volatiles, exhibiting differences in odor mass amounts and shelf-life across the distinctive odor volatiles monitored.

Authors

Institutions

Publication Details

Journal
Journal of Forensic Sciences
Published
2025-04-15
DOI
https://doi.org/10.1111/1556-4029.70053
Primary Topic
Advanced Chemical Sensor Technologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluation of composition C‐4 odor signature on a novel sorption training aid substrate

Paola A. Prada‐Tiedemann, Juliet DeNapoli
Journal of Forensic Sciences
Advanced Chemical Sensor Technologies
article

Evaluation of composition C‐4 odor signature on a novel sorption training aid substrate

Paola A. Prada‐Tiedemann, Juliet DeNapoli
article en

Abstract

The need for reliable non-detonable training aid systems within explosive canine detection applications is imperative for optimal operational performance. This study utilized Getxent polymer technology for the creation of Composition C-4 training aid material. The objectives of this research were to (1) evaluate target volatile odor concentration emitted from tubes following mass (1 g, 10 g, and 20 g) and impregnation time (24 h, 72 h, and 1 week) variations and (2) monitor training aid shelf-life over a period of up to 4 months. Solid-Phase Microextraction (SPME) coupled with Gas Chromatography/Mass Spectrometry (GC/MS) was a viable way to measure target C-4 odorants (2-ethyl 1-hexanol (2E1H), cyclohexanone, 2,3-dimethyl-2,3-dinitrobutane (DMNB)) from Getxent tubes. On average, the Getxent tubes absorbed 8% mass of total target VOCs within bulk C-4 headspace. The 20 g, 72-h impregnated Getxent tubes yielded detection of all three targeted odor volatiles while concurrently depicting the highest average mass amount of 2E1H and cyclohexanone. A shelf-life study over a four-month period depicted that DMNB yielded higher mass amounts across all impregnation and bulk mass variations while 2E1H and cyclohexanone had lower mass amounts, losing detection levels of 2E1H after a period of 3 months. The study demonstrated the capability of the Getxent tubes in the impregnation of C-4 explosive target odor volatiles, exhibiting differences in odor mass amounts and shelf-life across the distinctive odor volatiles monitored.

Journal of Forensic SciencesVol. 70(4)
Texas Tech University (US)
Responsible consumption and production
Openalex Percentile: Top 93%
Advanced Chemical Sensor Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.