PFI:AIR - TT: Technology Translation: Demonstration and Validation of a Novel Field Drug Test System for Law Enforcement

PFI:AIR - TT：技术翻译：用于执法的新型现场药物测试系统的演示和验证

• 批准号: 1701917
• 负责人: Richard Blair
• 金额: $ 20万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1701917&HistoricalAwards=false
• 关键词: PFI; AIR; TT; Technology; Translation

This PFI: AIR Technology Translation project focuses on translating a new system for identifying and detecting controlled substances to meet the needs of the public and law enforcement for enhanced accuracy and flexibility.  This technology will reduce the incidence of false identifications that can seriously impact the lives and families of innocent individuals.  The combination of new indicators, a cell phone enabled reader, and a dedicated analysis app is important because it improves the accuracy of the identification and limits the waste of public law enforcement resources. The project will result in a beta system consisting of a test, a reader, and an app suitable for deployment in volunteer departments. This low-cost, easy-to-use identification system has the advantages of improved accuracy, data management, and evidence preservation when compared to the leading competing presumptive chemical tests in this market space. It is also more cost effective than current field deployable instrumentation, which will facilitate wider adoption. This project addresses technology gaps in utility, scope and user safety as it translates from research discovery toward commercial application. The team has very preliminary data on the identification of illicit substances. Although these results are promising, this data set must be improved. By working with crime labs and academic forensic labs with access to these substances, as well as "street" drugs, the team will improve the quality of the substance identification database to make it an effective tool. Critical to producing a useable database is the ability to identify a wide range of substances. The current drug-indicator works best for compounds with cyclic amines like cocaine, PCP, and fentanyl. Relatively simple amines such as methamphetamine, MDMA, JWH-synthetic cannabinoids, LSD, and DMT require a different indicator. There are several candidates and the team will assess them while improving their dataset for scheduled substances that contain cyclic amines. There is also the possibility of unwanted exposure to the substance under test. The ability to identify a wide range of substances does not, in itself, reduce the exposure risk. Currently users must handle the unknown by applying a small sample onto a test strip. The team will work with volunteer departments to produce a new sampling device that will allow substance sampling with reduced exposure risk to the user. In addition, the post-doctoral scholar in this project will receive technology translation and managerial experiences through beta testing with volunteer law enforcement agencies and the direction of software development. The project engages IDem Systems to guide commercialization aspects and Softechnologies Corporation to improve the mobile app usability in this technology translation effort from research discovery toward commercial reality. This effort will also rely on partnerships with crime labs in Florida, Georgia, Maryland, Texas, Virginia, and California.

该 PFI：AIR 技术翻译项目的重点是翻译一种用于识别和检测受控物质的新系统，以满足公众和执法部门对提高准确性和灵活性的需求。该技术将减少可能严重影响生活的错误识别的发生率。新指标、手机阅读器和专用分析应用程序的结合非常重要，因为它提高了识别的准确性并限制了公共执法资源的浪费。该项目将进行测试。系统由测试组成，与市场上领先的竞争推定化学测试相比，这种低成本、易于使用的识别系统具有更高的准确性、数据管理和证据保存的优势。它比当前的现场部署仪器更具成本效益，这将有助于更广泛地采用该项目，因为该项目从研究发现转向商业应用。非法物质的识别。结果很有希望，但必须改进该数据集，通过与能够获取这些物质以及“街头”毒品的犯罪实验室和学术法医实验室合作，该团队将提高物质识别数据库的质量，使其成为有效的。生成可用数据库的关键是能够识别各种物质，目前的药物指示剂最适合含有环胺的化合物，例如可卡因、五氯苯酚和相对简单的胺，例如甲基苯丙胺、 MDMA、JWH 合成大麻素、LSD 和 DMT 需要不同的指标，团队将对其进行评估，同时改进含有环胺的表列物质的数据集。此外，还存在意外接触该物质的可能性。目前，识别多种物质的能力本身并不能降低暴露风险，该团队将与志愿者部门合作生产新的样本。允许物质采样的装置此外，该项目的博士后学者将通过志愿执法机构的 Beta 测试获得技术翻译和管理经验，并聘请 IDem Systems 来指导商业化方面。和 Softechnologies Corporation 合作，在这项从研究发现到商业现实的技术转化工作中提高移动应用程序的可用性。这项工作还将依赖于与佛罗里达州、佐治亚州、马里兰州、德克萨斯州、弗吉尼亚州和加利福尼亚州的犯罪实验室的合作。