人工進化研究所（AERI）
- Jun 16, 2023
- 8 min read

Characteristics of explosives detection techniques conventionally in use,& their problems and issues

Updated: Jun 18, 2023

Professor Kamuro's near-future science predictions:

Characteristics of explosives detection techniques conventionally in use,

and their problems and issues

Quantum Physicist and Brain Scientist

Visiting Professor of Quantum Physics,

California Institute of Technology

IEEE-USA Fellow

American Physical Society-USA Fellow

PhD. & Dr. Kazuto Kamuro

AERI：Artificial Evolution Research Institute

Pasadena, California

HP: https://www.aeri-japan.com/

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In this session, prof. Kamuro in AERI (Artificial Evolution Research Institute HP： https://www.aeri-japan.com/) will give a lecture on characteristics of explosives detection techniques conventionally in use, and their problems and issues.

I. Explosives detection techniques conventionally in use

Explosive detection is a non-destructive inspection process to determine whether a container contains explosive material. Explosive detection is commonly used at airports, ports and for border control.

1. Colorimetrics & automated colorimetrics:

The use of colorimetric test kits for explosive detection is one of the most established, simplest, and most widely used methods for the detection of explosives. Colorimetric detection of explosives involves applying a chemical reagent to an unknown material or sample and observing a color reaction. Common color reactions are known and indicate to the user if there is an explosive material present and in many cases the group of explosives from which the material is derived. The major groups of explosives are nitroaromatic, nitrate ester, and nitramine explosives, as well as inorganic nitrate-based explosives. Other groups include chlorates and peroxides which are not nitro based explosives. Since explosives usually contain nitrogen, detection often is based around spotting nitrogenous compounds. As a result, traditional colorimetric tests have a disadvantage: some explosive compounds (such as acetone peroxide) do not contain nitrogen and are therefore harder to detect.2.

2. Dogs:

Specially trained dogs can be used to detect explosives using their noses which are very sensitive to scents. While very effective, their usefulness becomes degraded as a dog becomes tired or bored.

These dogs are trained by specially trained handlers to identify the scents of several common explosive materials and notify their handler when they detect one of these scents. The dogs indicate a 'hit' by taking an action they are trained to provide - generally a passive response, such as sitting down and waiting.

The explosive detection canine was originated at the Metropolitan Police Department in Washington, D.C. in 1970, by then trainer Charles R. Kirchner.

The explosive detection canine was first used in Algeria in 1959 under the command of General Constantine.

Recent studies suggest that mass spectrometric vapor analysis techniques, such as secondary electrospray ionization (SESI-MS), could support canine training for explosive detection.

3. Honey bees

This approach couples trained honey bees with advanced video computer software to monitor the bee for the strategic reaction. Trained bees serve for 2 days, after which they are returned to their hive. This proven system is not yet commercially available. Biotechnology firm Inscentinel claims that bees are more effective than sniffer dogs.

4. Mechanical scent detection

Several types of machines have been developed to detect trace signatures for various explosive materials. The most common technology for this application, as seen in US airports, is ion mobility spectrometry (IMS). This method is similar to mass spectrometry (MS), where molecules are ionized and then moved in an electric field in a vacuum, except that IMS operates at atmospheric pressure. The time that it takes for an ion, in IMS, to move a specified distance in an electric field is indicative of that ion's size-to-charge ratio: ions with a larger cross-section will collide with more gas at atmospheric pressure and will, therefore, be slower.

Gas chromatography (GC) is often coupled to the detection methods discussed above in order to separate molecules before detection. This not only improves the performance of the detector but also adds another dimension of data, as the time it takes for a molecule to pass through the GC may be used as an indicator of its identity. Unfortunately, GC normally requires bottled gas, which presents logistical issues since bottles would have to be replenished. GC columns operated in the field are prone to degradation from atmospheric gases and oxidation, as well as bleeding of the stationary phase. Columns must be very fast, as well, since many of the applications demand that the complete analysis be completed in less than a minute.

5. Spectrometry:

Technologies based on ion mobility spectrometer (IMS) include ion trap mobility spectrometry (ITMS), and differential mobility spectrometry (DMS). Amplifying fluorescent polymers (AFP) use molecular recognition to "turn off" or quench the fluorescence of a polymer. Chemiluminescence was used frequently in the 1990s, but is less common than the ubiquitous IMS. Several attempts are being made to miniaturize, ruggedize and make MS affordable for field applications; such as an aerosol polymer that fluoresces blue under UV but is colorless when it reacts with nitrogen groups.

One technique compares reflected ultraviolet, infrared and visible light measurements on multiple areas of the suspect material. This has an advantage over olfactory methods in that a sample does not need to be prepared. A patent exists for a portable explosive detector using this method.

Mass spectrometry is seen as the most relevant new spectrometry technique. Several manufacturers have products that are under development, both in the US, Europe and Israel, including Laser-Detect in Israel, FLIR Systems and Syagen in the US, and SEDET in Europe.

6. X-ray machines:

Specially designed X-ray machines using computed axial tomography can detect explosives by looking at the density of the items.. These systems that are furnished with dedicated software, containing an explosives threat library and false-color coding to assist operators with their dedicated threat resolution protocols. X-ray detection is also used to detect related components such as detonators, but this can be foiled if such devices are hidden inside other electronic equipment.

Recently, machine learning algorithms have been developed that can automatically detect threats in x-ray scans.

7. Neutron activation:

Specially designed machines bombard the suspect explosives with neutrons and read the resulting gamma radiation decay signatures to determine the chemical composition of the sample. The earliest developed forms of Neutron Activation Analysis use low-energy neutrons to determine the ratios of nitrogen, chlorine, and hydrogen in the chemical species in question and are an effective means of identifying most conventional explosives. Unfortunately, the much smaller thermal Neutron cross sections of carbon and oxygen limit the ability of this technique to identify their abundances in the unknown species, and it is partly for this reason that terror organizations have favored nitrogen absent explosives such as TATP in the construction of IEDs. Modifications to the experimental protocol can allow for easier identification of carbon and oxygen-based species, (e.g. the use of inelastic scattering from fast neutrons to produce detectable gamma rays, as opposed to simple absorption occurring with the thermal neutrons), but these modifications require equipment that is prohibitively more complex and expensive, preventing their widespread implementation.

8. Silicon nanowires for trace detection of explosives

Silicon nanowire configured as field effect transistors have been demonstrated to detect explosives including TNT, PETN and RDX in sensitives superior to those of canines. The detection in this method is performed by passing a liquid or vapor containing the target explosive over the surface of a chip containing tens to hundreds of silicon nanowire sensing elements. Molecules of the explosive material interact with the surface of the nanowires and induce a measurable change in the electrical properties of the nanowire.

II. Problems and challenges of Explosives detection techniques conventionally in use

a. Conventional explosives detection techniques face several challenges and limitations that impact their effectiveness in detecting concealed explosives. Here are some of the key problems and challenges associated with these techniques:

1. Detection Limitations: Conventional techniques often rely on detecting specific components or characteristics of explosives, such as metal content or chemical signatures. This means they may not be able to detect non-metallic or homemade explosives that do not contain distinctive chemical markers. It's challenging to keep up with the ever-evolving techniques used by criminals and terrorists to create concealed explosives.

2. False Positives and False Negatives: Explosives detection systems can produce false positives, indicating the presence of explosives where none exist. This can lead to unnecessary alarm, disruption, and inconvenience for innocent individuals. On the other hand, false negatives, where explosives are not detected, pose significant security risks by allowing threats to go undetected.

3. Sensitivity and Specificity: Achieving the right balance between sensitivity (ability to detect actual threats) and specificity (ability to avoid false alarms) is challenging. Increasing sensitivity may lead to higher false positives, while reducing sensitivity could result in missed detections.

4. Detection Speed and Throughput: In high-traffic areas or crowded environments, conventional detection techniques may struggle to process individuals quickly enough. This can lead to delays, long queues, and reduced throughput, impacting the efficiency of security screening.

5. Concealment Techniques: Criminals and terrorists continually develop new methods to conceal explosive devices, such as sophisticated concealment compartments, body cavity concealment, or using innocent-looking items to disguise explosives. These techniques pose challenges to conventional detection methods.

6. Cost and Accessibility: Some conventional explosives detection technologies require expensive equipment, extensive infrastructure, or skilled operators, making them less accessible and scalable in certain settings or regions with limited resources.

7. Privacy Concerns: Some conventional detection methods, such as full-body scanners or invasive pat-down searches, raise privacy concerns and may be perceived as intrusive by individuals undergoing screening. Striking a balance between security needs and individual privacy rights is an ongoing challenge.

III. Conclusion:

Prof. Kamuro ended this lecture by adding

“a. Addressing these challenges requires continuous research and development efforts to improve existing techniques and develop new approaches. Emerging technologies, such as advanced imaging systems, artificial intelligence, machine learning, and nanotechnology, hold promise for overcoming some of these limitations by enhancing detection capabilities, reducing false positives, and increasing operational efficiency.

It's important to note that no single technique or technology can provide a complete solution to the challenges of explosives detection. Instead, a multi-layered, integrated approach combining different detection methods, intelligence gathering, behavioral analysis, and personnel training is crucial for effective and reliable explosives detection in various security scenarios.

b. Technology for finding a person carrying concealed explosives or firearms among a crowd of people requires non-detection by the perpetrator, on-the-spot observation, remote monitoring, non-destructiveness, non-contact, real-time, and pinpoint.

Technology for finding a person carrying concealed explosives or firearms among a crowd of people is (1) undetectable by the perpetrator (in vitro), (2) on-the-spot(in situ) observation, (3) remote monitoring, (4) non-destructive, (5) Non-contact, (6) real-time, and (7) pinpoint are essential. None of the above conventional explosives detection techniques, however, satisfies all of the essential characteristics (1) through (7) above.

The AI (artificial intelligence)-equipped suicide bombing detection system( https://www.aeri-japan.com/ai-detection-bombers , https://www.aeri-japan.com/blank-34 ) under development by AERI (Artificial Evolution Research Institute HP：https://www.aeri-japan.com/) is the world's only ultimate anti-terrorism system that satisfies all of the essential characteristics (1) to (7) above”.

END

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Quantum Brain Chipset & Bio Processor (BioVLSI)

Prof. PhD. Dr. Kamuro

Quantum Physicist and Brain Scientist involved in Caltech & AERI Associate Professor and Brain Scientist in Artificial Evolution Research Institute（ AERI: https://www.aeri-japan.com/ ）

IEEE-USA Fellow

American Physical Society Fellow

PhD. & Dr. Kazuto Kamuro

https://www.aeri-japan.com

email: info@aeri-japan.com

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【Keywords】　Artificial Evolution Research Institute：AERI

HP： https://www.aeri-japan.com/

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