Explosif

Résumé

Un explosif est défini par un mélange de corps qui, lors de leur transformation, sont susceptibles de dégager en un temps très court un grand volume de gaz porté à haute température, ce qui constitue une explosion. Une charge explosive (en explosive charge) est une quantité mesurée de matière explosive. Histoire Le premier composé explosif était très probablement la poudre noire, communément appelée la poudre à canon. Sa première apparition date de 220 avant J.-C. lorsque des alchimistes chinois l'ont enflammée par accident. Cependant, la poudre noire n'a pas été introduite en Europe avant le XIIIe siècle. Elle est pour la première fois utilisée en génie civil afin de réaliser le dragage de la Rivière Niermen en Europe du Nord. En 1846, le professeur italien Ascanio Sobrero découvre la nitroglycérine liquide. Quelques années plus tard, un inventeur du nom de Immanuel Nobel va développer un procédé de fabrication de la nitroglycérine. Dan

Source officielle

https://fr.wikipedia.org/wiki/Explosif

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Séances de cours associées (21)

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Hydrogen as an antioxidant gas has been widely used in the medical and biological fields for preventing cancer or treating inflammation. However, controlling the hydrogen concentration is crucial for practical use due to its explosive property when its volume concentration in air reaches the explosive limit (4%). In this work, a polymer-based microcantilever (mu-cantilever) hydrogen sensor located at the end of a fiber tip is proposed to detect the hydrogen concentration in medical and biological applications. The proposed sensor was developed using femtosecond laser-induced two-photon polymerization (TPP) to print the polymer mu-cantilever and magnetron sputtering to coat a palladium (Pd) film on the upper surface of the mu-cantilever. Such a device exhibits a high sensitivity, roughly -2 nm %(-1) when the hydrogen concentration rises from 0% to 4.5% (v/v) and a short response time, around 13.5 s at 4% (v/v), making it suitable for medical and environmental applications. In addition to providing an ultracompact optical solution for fast and highly sensitive hydrogen measurement, the polymer mu-cantilever fiber sensor can be used for diverse medical and biological sensing applications by replacing Pd with other functional materials.

AMER CHEMICAL SOC2020

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Industrial chemical processes may involve thermal risks as most of the reactions performed are exothermic, the chemicals used are often thermally unstable, and the operating conditions are set to induce high conversion and throughput. Besides the reactive steps, all operations from mixing to storage and from processing to transport involving sensitive chemicals should be conducted under strictly controlled conditions ensuring safe operations. Performing an efficient risk assessment and implementing the proper risk mitigation measures are essential to avoid, or at least reduce, accidents and their potentially disastrous consequences. For optimal design and implementation of safety measures, it is important that these considerations are taken into account at early stages of process development. The required data should be made available at the appropriate time so it can be properly accounted for and efficiently serve the design. Yet, at early design phases, some information may be unavailable due to several reasons: the process design being still in development, some parameters can be unknown; experimental analysis of chemicals could be hindered or impossible due to products availability in required quantities; several alternatives are under investigation which raises the necessary resources (time and material) for experimental tests. Predictions would be the appropriate response to such scenario. The aim of this dissertation is to develop predictive models for two hazardous behaviors of chemicals: explosive sensitivity and thermal stability. For the models to be applicable at early development phases, it is preferable to minimize the information feed requirements, and therefore, structure-based approaches are applied. Two methods were identified: Quantitative Structure-Property Relationships (QSPR) and Group Contributions Method (GCM). The hazardous behaviors are studied through characteristic measurements: the Minimal Ignition Energy (MIE) to represent explosive sensitivity and Differential Scanning Calorimetry (DSC) for thermal stability. These measurements are widely employed in safety studies and deliver necessary information to identify potential hazards. Moreover, their specificities call for predictive models: MIE tests require repetitive analysis and hence are time and material consuming; regarding DSC experiments, experts have noted that they seem to exhibit structurally dependent features, and so far no study has comprehensively investigated this phenomenon. This work presents in a first part the structure-based approaches that are applied and the elements of Data Analysis necessary for developing predictive models and simulating experimental results. Secondly, both experimental analysis are detailed and the important information our models should be able to represent will be exposed. Finally, the third and last part is dedicated to the applications: the obtained predictive models are presented, evaluated and discussed. Most of the initial objectives are met as efficient solutions are proposed, nonetheless, some improvement strategies may also be considered.

EPFL2016

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Handling and storing chemicals in the industrial world require a conscientious hazards assessment and the implementation of appropriate measures to ensure safety for the workers, the company, the environment and the society. Reliable evaluations and predictions based solely on the molecular structure would represent a valuable tool in the preliminary hazard assessment process as it would reduce the necessary time and resources for extensive testing. This work presents how Quantitative Structure-Property Relationships (QSPR) of various hazardous chemical properties (i.e. thermal decomposition enthalpy or Minimal Ignition Energy) can be built to meet these needs. Results are to be illustrated with some examples. Two sets of chemicals were studied and the corresponding experimental results were correlated to 90 molecular descriptors. The models were built following a regression analysis. The best multi-linear regressions presenting 6 parameters or less with high determination and cross-validation coefficients are withheld as predictive models.

2013

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EPFL2016