IR spectroscopy in catalysis

There has been a recent surge in the development of mass spectrometric methods for detailed characterization of naphthenic acid fraction compounds (all C(c)H(h)N(n)O(o)S(s), species, including heteroatomic and aromatic components in the acid-extractable fraction) in environmental samples. This surge is driven by the increased activity in oil sands environmental monitoring programs in Canada, the exponential increase in research studies on the isolation and toxicity identification of components in oil sands process water (OSPW), and the analytical requirements for development of technologies for treatment of OSPW. There has been additional impetus due to the parallel studies to control corrosion from naphthenic acids during the mining and refining of heavy bitumen and crude oils. As a result, a range of new mass spectrometry tools have been introduced since our last major review of this topic in 2009. Of particular significance are the developments of combined mass spectrometric methods that incorporate technologies such as gas chromatography, liquid chromatography, and ion mobility. There has been additional progress with respect to improved visualization methods for petroleomics and oil sands environmental forensics. For comprehensive coverage and more reliable characterization of samples, an approach based on multiple-methods that employ two or more ionization modes is recommended. On-line or off-line fractionation of isolated extracts, with or without derivatization, might also be used prior to mass spectrometric analyses. Individual ionization methods have their associated strengths and weaknesses, including biases, and thus dependence upon a single ionization method is potentially misleading. There is also a growing trend to not rely solely on low-resolution mass spectrometric methods (<20,000 resolving power at m/z 200) for characterization of complex samples. Future research is anticipated to focus upon (i) structural elucidation of components to determine the correlation with toxicity or corrosion, (ii) verification of characterization studies based on authentic reference standards and reference materials, and (iii) integrated approaches based on multiple-methods and ionization methods for more-reliable oil sands environmental forensics.

Advances in mass spectrometric characterization of naphthenic acids fraction compounds in oil sands environmental samples and crude oil—A review

Quantitative characterization of the solid acidity of montmorillonite using combined FTIR and TPD based on the NH3 adsorption system

Ammonia adsorption was studied under dynamic conditions, at room temperature, on activated carbons of different origins (coal-based, wood-based and coconut-shell-based carbons) before and after their impregnation with various inorganic compounds including metal chlorides, metal oxides and polycations. The role of humidity was evaluated by running tests in both dry and moist conditions. Adsorbents were analyzed before and after exposure to ammonia by thermal analyses, sorption of nitrogen, potentiometric titration, X-ray diffraction and FTIR spectroscopy. Results of breakthrough tests show significant differences in terms of adsorption capacity depending on the parent carbon, the impregnates and the experimental conditions. It is found that surface chemistry governs ammonia adsorption on the impregnated carbons. More precisely, it was demonstrated that a proper combination of the surface pH, the strength, type and amount of functional groups present on the adsorbents’ surface is a key point in ammonia uptake. Water can have either positive or negative effects on the performance of adsorbents. It can enhance NH3 adsorption capacity since it favors ammonia dissolution and thus enables reaction between ammonium ions and carboxylic groups from the carbons’ surface. On the other hand, water can also reduce the performance from the strength of adsorption standpoint. It promotes dissolution of ammonia and that ammonia is first removed from the system when the adsorbent bed is purged with air. Ammonia, besides adsorption by van der Waals forces and dissolution in water, is also retained on the surface via reactive mechanisms such as acid–base reactions (Bronsted and Lewis) or complexation. Depending on the materials used and the experimental conditions, 6–47% ammonia adsorbed is strongly retained on the surface even when the bed is purged with air.

On the reactive adsorption of ammonia on activated carbons modified by impregnation with inorganic compounds.

In addition to the role of the adsorbent’s features (porosity and surface chemistry), the experimental conditions of the breakthrough tests can affect the removal process as well. In particular, the role of water, either in the air stream or on the adsorbent’s surface must be considered.

Role of Water

Pillared clays were prepared by intercalation in a Wyoming montmorillonite with a partially hydrolyzed aluminum chloride solution containing Ce +3 ions as second, and Co +2 , or Ni +2 , or Zn +2 , or Mg +2 ions as third component. These multi-component pillared materials were investigated by Fourier Transform Infra-Red (FT-IR) spectroscopy, X-ray diffraction (XRD), nitrogen (77 K) and ammonia (323 K) adsorption, thermogravimetric (TG) analysis and acidity measurement in order to determine both thermal stability and acidity. The catalyst obtained from a pillaring mixture of three cations (AlCeMg-PILC) showed the most interesting characteristics.

The influence of the composition on the thermal and acid characteristics of multi-component oxide pillared montmorillonite

Acidic crudes, i.e., oils with total acid number (TAN) higher than 0.5 mg of KOH/g, represent an important option for the refiner. This acidity, related to the presence of naphthenic acids (NAs), is considered responsible for a number of drawbacks in refinery, such as corrosion of pipelines and devices. The understanding of the corrosion activity of NAs is complex because of their wide distribution of physical properties and the simultaneous presence in the oil of other species contributing to corrosion (e.g., H2S). In this work, the distribution of NAs was determined by a combination of infrared (IR) and liquid chromatography–mass spectrometry (LC–MS) techniques in crude oils before and after corrosivity tests. An analytical procedure was developed to efficiently extract NAs from crude oils and to monitor them by the LC–MS technique, via electrospray ionization in negative mode [ESI(−)]. The identification of the NAs was possible from the corresponding molecular ions, according to the number of C atoms a...

Evolution of Naphthenic Acids during the Corrosion Process

Currently, Deep learning and especially Convolutional Neural Networks (CNNs) have become a fundamental computational approach applied in a wide range of domains, including some safety-critical applications (e.g., automotive, robotics, and healthcare equipment). Therefore, the reliability evaluation of those computational systems is mandatory. The reliability evaluation of CNNs is performed by fault injection campaigns at different levels of abstraction, from the application level down to the hardware level. Many works have focused on evaluating the reliability of neural networks in the presence of transient faults. However, the effects of permanent faults have been investigated at the application level, only, e.g., targeting the parameters of the network. This paper intends to propose a framework, resorting to a binary instrumentation tool to perform fault injection campaigns, targeting different components inside the GPU, such as the register files and the functional units. This environment allows for the first time assessing the reliability of CNNs deployed on a GPU considering the presence of permanent faults.

Reliability Assessment of Neural Networks in GPUs: A Framework For Permanent Faults Injections

A combination of different techniques [gel permeation chromatography (GPC), nuclear magnetic resonance, elemental analysis, etc.] is usually employed to calculate average molecular features of asphaltenes, directly or through application of models. Such an average picture has intrinsic limitations, particularly concerning the distribution of aromatic rings in the molecular structures. To improve the description of asphaltenes beyond the “average molecule” concept, which is quite limiting in the case of products containing such a large distribution of species, a new approach is developed and applied to untreated and hydrotreated asphaltenes. The hydrotreating process was applied here as a tool for simplifying the pristine asphaltene molecules, breaking them into smaller structures, by not only elimination of the alkyl side chains but also transformation of the original building blocks. In this approach, the GPC elution was separated into 1 min fractions along the whole run and the fractions were further an...

Beyond the Average Molecule Description of Asphaltenes: Hyphenated Gel Permeation Chromatography and Spectroscopic Analyses

Currently, Deep Neural Networks (DNNs) are fun-damental computational structures deployed in a wide range of modern application domains (e.g., data analysis, healthcare, automotive, robotics). The computational complexity is inherent in these cognitive models, which demand high-performance devices like Graphics Processing Units (GPUs). Therefore, the implementation of DNNs on GPU devices is becoming increasingly frequent, even for cutting-edge safety-critical applications (e.g., autonomous and semi-autonomous cars). Thus, the reliability evaluation of these applications is mandatory because several phenomena (including aging) may produce permanent defects in the GPU, thus inducing the DNN to produce wrong results. Until now, the effects of permanent faults on DNNs have been mainly investigated at the application level, only, e.g., acting on the parameters of the network. This paper presents an environment allowing for the first time a more detailed experimental evaluation of the impact of permanent faults in a GPU on the reliability of a DNN running on it, based on considering faults at the architectural level. The results of the fault injection campaigns we performed on the GPU register files are compared with those at the application level, proving that the latter ones are generally optimistic.

Evaluating the impact of Permanent Faults in a GPU running a Deep Neural Network

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