Comparison of gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry for the determination of fatty and resin acids in paper mill process waters

A comparative study of the performance of liquid chromatography (LC)-atmospheric pressure chemical ionisation (APCI)-mass spectrometry (MS) and gas chromatography (GC)-mass spectrometry techniques for the determination of resin and fatty acids from paper mill process waters was carried out. These compounds are responsible for the high toxicity of paper mill effluents and little research has been carried out regarding their analysis using mass spectrometric techniques. To prove the usability of GC and LC-MS, 16 treated and untreated water samples of recycle, kraft and pulp paper mills were analysed and good agreement was observed as regards to compounds detected and corresponding concentrations. This paper also reports the limits of detection, recoveries, reproducibility, linearity and precision using the two methods. GC-MS presented better selectivity and lower detection limits (below 0.2 microg/l), but derivatization of the extracts and the short life of derivatives (12-24 h) made the technique tedious and prone to high variations. Although LC-APCI-MS presented coelution of the non-aromatic resin acids, it also showed good sensitivity (limits of detection <3 microg/l) and permitted the detection of resin and fatty acids at microg/l level. In addition, since samples could be directly injected to the chromatographic system, LC-APCI-MS was proven as a powerful technique for quick and unequivocal quality control during papermaking.     

Determination of alcohol polyether average molar oligomer value/distribution via supercritical fluid chromatography coupled with UV and MS detection

Supercritical fluid chromatography (SFC) was used for the analysis of ethoxylated and propoxylated surfactants. Samples were derivatized to phenylated silyl ethers with a disilazane-chlorosilane mixture. Addition of a phenyl group to the surfactant allowed UV-absorbance detection of each oligomer. Acetonitrile and methanol were evaluated as mobile phase modifiers. Better peak shape was realized with methanol-modified CO2 on an octadecyl silica bonded phase than with acetonitrile-modified CO2. Peak assignments were made via SFC coupled with electrospray ionization-mass spectrometry (ESI-MS) in the positive ion mode. A sulfonamide-embedded alkyl stationary phase was also evaluated for separation of the derivatized samples. SFC-UV and SFC-ESI-MS data were jointly used for calculation of average molar oligomer values which were then compared to values calculated from 1H NMR data of non-derivatized samples. The derivatization or separation method using the sulfonamide embedded phase required no preliminary cleanup and yielded reproducible oligomer values that were consistent with those of the manufacturer's nominal values.     

Crystal hydrates: an investigation of the use of crystal hydrates as stationary phases in gas chromatography

Summary The use of crystallohydrates, crystallohydrate solutions and melts as stationary phases in gas chromatography has been proposed. Crystallohydrates have been shown to display high selectivity in the separation of polar organic compounds when use is made of water vapours as the mobile phase. Some aspects of gas-liquid chromatography in water vapours and with the stationary phase comprising crystallohydrates have been studied, and the preliminary results obtained point to the desirability of further progress in this field.Presented at the 14th International Symposium on Chromatography London, September, 1982     

Normal-phase high-performance liquid chromatographic separations using ethoxynonafluorobutane as hexane alternative. II. Liquid chromatography-atmospheric pressure chemical ionization-mass spectrometry applications with methanol gradients

We have reported recently that high-speed normal-phase (NP) HPLC separations of a broad range of organic compounds can be performed on cyano columns using gradients of methanol in hexane-like solvent-ethoxynonafluorobutane (ENFB), available commercially. In this communication, we demonstrate that atmospheric pressure chemical ionization (APCI) in combination with mass spectrometry (MS) can be effectively used for detection in such separations. The efficiency of APCI under conditions studied has also been compared to the efficiency of traditional electrospray ionization (ESI) in combination with MS for reversed-phase (RP) HPLC of the same compounds. The compounds included in this study were steroids, benzodiazepines, and other central nervous system-active substances, nonsteroidal anti-inflammatory drugs, tricyclic antidepressants, and beta-adrenergic blocking agents. Non-polar compounds were found to respond stronger when APCI-MS technique was used, whereas APCI and ESI ionization efficiencies were comparable when polar substances were studied. The combination of normal-phase HPLC separation conditions with mass spectral detection may expand the range of LC-MS applications traditionally associated with reversed-phase HPLC and ESI-MS detection.     

Effect of Chamber Gas Phase on Mobile Phase pH and on Separation Efficiency in TLC. A New Mode of Chromatography

A new variant of thin-layer chromatography (TLC), based on a gradual change of mobile phase acidity during elution, is proposed. The pH change occurs in the mobile phase moving along the TLC plate as a result of its contact with an acidic or a basic gas phase that replaces the initial mobile phase vapor in the TLC chamber. The potential of this approach has been demonstrated by using carbon dioxide and ammonia gases to improve the resolution of benzoic acids and aromatic amines on polyamide TLC plates.     

CPFinder: Finding an unknown caller's profession from anonymized mobile phone data

Identifying an unfamiliar caller's profession is important to protect citizens' personal safety and property. Owing to the limited data protection of various popular online services in some countries, such as taxi hailing and ordering takeouts, many users presently encounter an increasing number of phone calls from strangers. The situation may be aggravated when criminals pretend to be such service delivery staff, threatening the user individuals as well as the society. In addition, numerous people experience excessive digital marketing and fraudulent phone calls because of personal information leakage. However, previous works on malicious call detection only focused on binary classification, which does not work for the identification of multiple professions. We observed that web service requests issued from users' mobile phones might exhibit their application preferences, spatial and temporal patterns, and other profession-related information. This offers researchers and engineers a hint to identify unfamiliar callers. In fact, some previous works already leveraged raw data from mobile phones (which includes sensitive information) for personality studies. However, accessing users' mobile phone raw data may violate the more and more strict private data protection policies and regulations (e.g., General Data Protection Regulation). We observe that appropriate statistical methods can offer an effective means to eliminate private information and preserve personal characteristics, thus enabling the identification of the types of mobile phone callers without privacy concerns. In this paper, we develop CPFinder —- a system that exploits privacy-preserving mobile data to automatically identify callers who are divided into four categories of users: taxi drivers, delivery and takeouts staffs, telemarketers and fraudsters, and normal users (other professions). Our evaluation of an anonymized dataset of 1,282 users over a period of 3 months in Shanghai City shows that the CPFinder can achieve accuracies of more than 75.0% and 92.4% for multiclass and binary classifications, respectively.     

A survey on deploying mobile deep learning applications: A systemic and technical perspective

With the rapid development of mobile devices and deep learning, mobile smart applications using deep learning technology have sprung up. It satisfies multiple needs of users, network operators and service providers, and rapidly becomes a main research focus. In recent years, deep learning has achieved tremendous success in image processing, natural language processing, language analysis and other research fields. Despite the task performance has been greatly improved, the resources required to run these models have increased significantly. This poses a major challenge for deploying such applications on resource-restricted mobile devices. Mobile intelligence needs faster mobile processors, more storage space, smaller but more accurate models, and even the assistance of other network nodes. To help the readers establish a global concept of the entire research direction concisely, we classify the latest works in this field into two categories, which are local optimization on mobile devices and distributed optimization based on the computational position of machine learning tasks. We also list a few typical scenarios to make readers realize the importance and indispensability of mobile deep learning applications. Finally, we conjecture what the future may hold for deploying deep learning applications on mobile devices research, which may help to stimulate new ideas.     

A new method for the determination of mobile phase volume in normal and reversed-phase liquid chromatography

Summary A new method for the determination of the mobile phase volume (V _m) in liquid chromatography is presented based on the model regarding the retention of ionic solutes in the presence of eluent electrolytes. TheV _m value can be determined by measuring the retention volumes of two ions that have the same charge in two eluent electrolyte systems. Compared with the methods using isotopically labelled eluent components or inorganic salts asV _m markers, the method presented is proved to give more reasonableV _m values for both normal and reversed-phase liquid chromatography. As well as in binary mixed solvent systems, theV _m values in single solvent systems can be determined by this method.     

Faster GC analyses performed by flow programming in short capillary columns

The technique of programming the carrier gas flow rate in gas chromatography, especially in connection with the use of capillary columns shorter than 10 m can significantly accelerate GC analyses. Equations for calculation of the parameters of the exponential flow function and retention data are described. The effects of flow programming in a short capillary column are shown in a few chromatograms. Different programming rates are tested and compared with temperature programming.     

Enhancement of the solubilities of polycyclic aromatic hydrocarbons by weak hydrogen bonds with water

Summary The thermodynamics of mobile order is applied to predict the aqueous solubility of liquid and solid aliphatic and polycyclic aromatic hydrocarbons. The solubility values are mainly determined by the magnitude of the hydrophobic effect. However, contrary to the solubilities of the alkanes, the solubilities of polycyclic aromatic hydrocarbons in water predicted in absence of solute-solvent hydrogen (H) bonds are systematically too low. This shows the contribution of weak specific interactions between the OH groups and the electrons of the aromatic substances. According to the theory, these interactions are characterized by a stability bility constant K_o which can be derived from solubility data. At 25°C, this constant amounts to 80 cm³/mol, the order of magnitude of which can be explained by the competition of these intermolecular bonds with the rather weak self-association bonds in the secondary chains of water.