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In this paper the following information interpretation of uncertainty relation is proposed: if one bit of information was extracted from the system as a result of the measurement process, then the measurement itself adds an additional uncertainty (chaos) into the system equaled to one bit. This formulation is developed by calculating of the Shannon information entropy for the classical N-slit interference experiment. This approach allows looking differently at several quantum phenomena. Particularly, the information interpretation is used for explanation of entangled photons diffraction picture compression.

A facile and efficient method for the improved miscibility of natural polymer/synthetic polymer blends is reported here based on the electric-field-driven phase inversion. We have employed bioderived chitosan (CS) and Polyamide-6 (PA6) as an example since their blends are known to always result in a large scale phase separation (i.e. CS settles to the bottom of the blends as sediment) during phase inversion procedure. The condensed structure of the polymer blends has been well characterized, notably by the polarized attenuated total reflectance infrared spectroscopy and proton longitudinal relaxation time (T1) distribution. The application of an electric field can orient the polar groups which will hinder the crystallization of blends and also increase the interphase interaction between PA6 and CS. The miscibility of the PA6/CS blends has been characterized by scanning electron microscopy and confocal Raman spectroscopy. It is shown that this controllable hydrogen bonding environment, induced by the presence of electric field, indeed greatly hinder the sedimentation of CS without destroying its chemical structure. The improved miscibility of PA6/CS blends can thus significantly increase the toughness and generate a somewhat larger tensile strength.

Principal component analysis (PCA) has proved to be a powerful technique for processing NMR data. It is particularly useful in signal quantitation where it often provides better results compared to a direct integration of individual signals. In the present work, we recapitulate the principles and theoretical framework underlying PCA-based quantitation with a special focus on T1 relaxometry. We show that under commonly encountered conditions, this approach can provide up to ~4-fold improvement in scatter of points in magnetization build-up curves compared to direct integration. Best practices to optimize the PCA performance in measuring the total magnetization are discussed, including minimization of the number of signal-related principal components and a proper selection of FT parameters and data quantitation intervals. For signals consisting of distinct relaxation components, formulas are provided for resolving the components relaxation and illustrated on a real-data example. In addition to the problem of quantitation, the use of PCA in denoising of partially relaxed spectra is discussed in connection with such applications as line shape analysis and monitoring relaxation of individual spectral components.

The structure of chain entanglements in the solid state is important for revealing the relationship of structure and properties of polyolefin. In this work, low-field solid-state 1H NMR is used to study the chain dynamics of ultra-high molecular weight polyethylene (UHMWPE) in the solid state. It has been found that the relaxation distribution, analyzed by a multi-exponential inversion program, is an effective method to characterize the heterogeneous chain mobility. It is evidenced from the results that the UHMWPE in a less entangled state presents an obviously heterogeneous distribution of chain mobility in the non-crystalline phase, corresponding to its heterogeneous distribution of entangled points. In comparison, the commercial UHMWPE with a large number of entanglements shows a much more uniform mobility of the non-crystalline components. This heterogeneous distribution of chain mobility becomes even more critical after annealing the samples below the melting point, especially for the less entangled UHMWPE.

European Union’s Horizon 2020 Research and Innovation Programme; Grant/Award number 668119 (project “IDentIFY”).

This work addresses the problem of a compact and easily comparable representation of multi-exponential relaxation data. It is often convenient to describe such data in a few parameters, all being of physical significance and easy to interpret, and in such a way that enables a model-free comparison between different groups of samples. Logarithmic moments (LMs) of the relaxation time constitute a set of parameters which are related to the characteristic relaxation time on the log-scale, the width and the asymmetry of an underlying distribution of exponentials. On the other hand, the calculation of LMs does not require knowing the actual distribution function and is reduced to a numerical integration of original data. The performance of this method has been tested on both synthetic and experimental NMR relaxation data which differ in a signal-to-noise ratio, the sampling range and the sampling rate. The calculation of two lower-order LMs, the log-mean time and the log-variance, has proved robust against deficiencies of the experiment such as scattered data point and incomplete sampling. One may consider using them as such to monitor formation of a heterogeneous structure, e.g., in phase separation, vitrification, polymerization, hydration, aging, contrast agent propagation processes. It may also assist in interpreting frequency and temperature dependences of relaxation, revealing a crossover from slow to fast exchange between populations. The third LM was found to be a less reliable quantity due to its susceptibility to the noise and must be used with caution.

1H relaxometry measurements of petroleum core plugs are commonly performed on low field magnets ( 3 Tesla) are typically employed in small animal MRI studies to improve SNR and image resolution. For many rock core plug samples, susceptibility mismatch effects can be severe at these higher fields leading to decreased T2 and T2*. In this work we seek an answer to the general question of what is the best field for MRI of rock core plugs, anticipating that it will be both sample and measurement method dependent. Free Induction Decay (FID) relaxation time measurements were undertaken to investigate the conditions under which the SNR in Centric Scan SPRITE (Single Point Ramped Imaging with T1 Enhancement) MRI measurements is maximized. The image SNR benefits from greater signal at higher fields, but is negatively impacted by the correspondingly shorter signal lifetimes. Depending on the noise regime of the sample, the maximum SNR may be predicted for Centric Scan SPRITE MRI with T2* being B0 field dependent. In this work we describe a series of simple experimental considerations to determine the optimal B0 field for SPRITE MRI. Selection of the best field is aided by a new generation of superconducting magnets which allows the experimentalist to readily vary the field strength. Such magnets allow one to experimentally control sample magnetization for high sensitivity MRI measurements of core plug samples, while controlling the effect of susceptibility mismatch on the signal lifetimes.

The Open data principle is becoming an increasingly important part of the data revolution, which is recognized worldwide as a key engine for achieving the post-2015 United Nations (UN) Sustainable Development Goals. Open data, publicly available online information that can be used for any purpose at little or no cost, represent one of the most underutilized key assets of modern government. Open data initiatives are often directed at converting open data into formats that can be reused for private sector development, jobs creation, economic growth, and more effective governance and citizen engagement. A 2013 study estimated that using open data in seven sectors of economic activity could create 3 trillion US dollars to 5 trillion US dollars annually in economic value worldwide. The direct, annual economic value of public governmental data has been estimated at up to 40 billion euros for the European Union and 2 billion pounds for the United Kingdom. Numerous examples illustrate how the use of open data can give significant support to achieving the post-2015 UN Sustainable Development Goals.