A novel Desorption Corona Beam Ionization (DCBI) source for direct analysis of samples from surface in mass spectrometry is reported. The DCBI source can work under ambient conditions without time-consuming sample pretreatments. The source shares some common features with another ionization source - Direct Analysis in Real Time (DART), developed earlier. For example, helium was used as the discharge gas (although only corona discharge is involved in the present source), and heating of the discharge gas is required for sample desorption. However, the difference between the two sources is substantial. In the present source, a visible thin corona beam extending out around 1 cm can be formed by using a hollow needle/ring electrode structure. This feature would greatly facilitate localizing sampling areas and performing imaging/profiling experiments. The DCBI source is also capable of performing progressive temperature scans between room temperature and 450 degrees C in order to sequentially desorb samples from the surface and, therefore, to achieve a rough separation of the individual components in a complex mixture, resulting in less congestion in the mass spectrum acquired. Mass spectra for a broad range of compounds (pesticides, veterinary additives, OTC drugs, explosive materials) have been acquired using the DCBI source. For most of the compounds tested, the heater temperature required for efficient desorption is at least 150 degrees C. The molecular weight of the sample that can be desorbed/ionized is normally below 600 dalton even at the highest heater temperature, which is mainly limited by the volatility of the sample.

A new low-dimensional parameterization based on principal component analysis (PCA) and convolutional neural networks (CNN) is developed to represent complex geological models. The CNN-PCA method is inspired by recent developments in computer vision using deep learning. CNN-PCA can be viewed as a generalization of an existing optimization-based PCA (O-PCA) method. Both CNN-PCA and O-PCA entail post-processing a PCA model to better honor complex geological features. In CNN-PCA, rather than use a histogram-based regularization as in O-PCA, a new regularization involving a set of metrics for multipoint statistics is introduced. The metrics are based on summary statistics of the nonlinear filter responses of geological models to a pre-trained deep CNN. In addition, in the CNN-PCA formulation presented here, a convolutional neural network is trained as an explicit transform function that can post-process PCA models quickly. CNN-PCA is shown to provide both unconditional and conditional realizations that honor the geological features present in reference SGeMS geostatistical realizations for a binary channelized system. Flow statistics obtained through simulation of random CNN-PCA models closely match results for random SGeMS models for a demanding case in which O-PCA models lead to significant discrepancies. Results for history matching are also presented. In this assessment CNN-PCA is applied with derivative-free optimization, and a subspace randomized maximum likelihood method is used to provide multiple posterior models. Data assimilation and significant uncertainty reduction are achieved for existing wells, and physically reasonable predictions are also obtained for new wells.Finally, the CNN-PCA method is extended to a more complex non-stationary bimodal deltaic fan system, and is shown to provide high-quality realizations for this challenging example.

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Current direct analysis methods in mass spectrometry (MS) are predominantly focused on desorbing and ionizing samples in the solid phase. Some sampling difficulties are associated with liquid (solution) or gas samples. The present study has expanded direct MS analysis to solution samples by using the desorption corona beam ionization (DCBI) technique in combination with poly(dimethylsiloxane) (PDMS) substrate sampling. Typically, the PDMS substrate is dipped in water for microextraction of pesticide compounds and then is transferred to an MS ion source for desorption and ionization. This approach improves the detection limit for DCBI and allows more organic compounds in complex mixtures to be identified within seconds. The practical application of this device is demonstrated by identifying five pesticides (acephate, isoprocarb, dimethoate, dichlorvos, and dicofol) in water. The obtained detection limits of pesticides are 1 μg/L, the measured dynamic ranges are 3 orders of magnitude, the calculated correlation coefficients are between 0.939 and 0.979 at concentration levels of 5-5000 μg/L, and the repeatabilities defined as a relative standard deviation of five successive injections are in the range of 13-17%. The results indicate that the DCBI technique coupled with PDMS sampling is an excellent method for the analysis of organic pesticides in solution, and it also opens up a new avenue for direct MS studies of solution samples with general importance.