Composition of Ni2+ cation solvation shell in NiCl2–methanol solution by multinuclear NMR

1H-, 2H- and 13C-NMR spectra have been used to test the Ni2+ solvation shell composition in the 1.1 molal methanol solution of NiCl2. It has been confirmed that Cl− anion takes part in the nearest environment of Ni2+ cation at all the temperatures investigated. Using 2H-NMR allowed us to detect for the first time OD-signal of methanol in the primary solvation shell of Ni2+ cation. Both 2H- and 13C-NMR spectra show that the composition of the cation solvation shell becomes more complicated at temperatures lower than 220 K

Terahertz Bloch oscillator with suppressed electric domains: Effect of elastic scattering

We theoretically consider the amplification of THz radiation in a superlattice Bloch oscillator. The main dilemma in the realization of THz Bloch oscillator is finding operational conditions which allow simultaneously to achieve gain at THz frequencies and to avoid destructive space-charge instabilities. A possible solution to this dilemma is the extended Limited Space-Charge Accumulation scheme of Kroemer (H. Kroemer, cond-mat/0009311). Within the semiclassical miniband transport approach we extend its range of applicability by considering a difference in the relaxation times for electron velocity and electron energy. The kinetics of electrons and fields establishing a stationary signal in the oscillator is also discussed.Comment: Submitted to proceedings of the summer school-conference of AQDJJ programme of ESF, Kiten, Bulgaria, 9-24 June 200

Differential proteomics analysis of the surface heterogeneity of dextran iron oxide nanoparticles and the implications for their in vivo clearance

In order to understand the role of plasma proteins in the rapid liver clearance of dextran-coated superparamagnetic iron oxide (SPIO) in vivo, we analyzed the full repertoire of SPIO-binding blood proteins using novel two-dimensional differential mass spectrometry approach. The identified proteins showed specificity for surface domains of the nanoparticles: mannan-binding lectins bound to the dextran coating, histidine-rich glycoprotein and kininogen bound to the iron oxide part, and the complement lectin and contact clotting factors were secondary binders. Nanoparticle clearance studies in knockout mice suggested that these proteins, as well as several previously identified opsonins, do not play a significant role in the SPIO clearance. However, both the dextran coat and the iron oxide core remained accessible to specific probes after incubation of SPIO in plasma, suggesting that the nanoparticle surface could be available for recognition by macrophages, regardless of protein coating. These data provide guidance to rational design of bioinert, long-circulating nanoparticles.National Cancer Institute (U.S.) (Grant CA119335)National Cancer Institute (U.S.) (Grant CA124427

Upcycling glass wool and spodumene tailings in building ceramics from kaolinitic and illitic clay

With concerns related to the high cost and environmental footprint of energy, reducing the sintering temperature in the ceramic industry remains a key challenge. This study investigates the effect of glass wool waste on the sintering properties of kaolinitic and illitic clays, two types of clay commonly used in the ceramic industry. Both clays were substituted with 20 and 40 wt% glass wool, and the effect on sintering at 850, 950, and 1050 °C was investigated. The properties of the sintered materials were assessed with several techniques, including X-ray diffraction, scanning electron microscopy, bulk density, water absorption, dilatometry, and compressive and flexural strength. The water absorption of the prepared ceramics varied from 0 to 30 %, while compressive and flexural strength were in the ranges of 10–60 MPa and 2–15 MPa, respectively, depending on the glass wool content and sintering temperature. The addition of glass wool induced more glass formation, microstructure densification, and higher firing shrinkage at lower temperatures, which contributed to an increase of more than 100 % strength at 850 °C. The addition of 10–20 parts spodumene tailings was observed to mitigate the firing shrinkage at 950 °C with no significant effect on the mechanical properties.Peer reviewe

Unexpected Temperature Behavior of Polyethylene Glycol Spacers in Copolymer Dendrimers in Chloroform

We have studied copolymer dendrimer structure: carbosilane dendrimers with terminal phenylbenzoatemesogenic groups attached by poly(ethylene) glycol (PEG) spacers. In this system PEG spacers areadditional tuning to usual copolymer structure: dendrimer with terminal mesogenic groups. Thedendrimer macromolecules were investigated in a dilute chloroform solution by 1H NMR methods(spectra and relaxations). It was found that the PEG layer in G = 5 generations dendrimer is “frozen”at high temperatures (above 260 K), but it unexpectedly becomes “unfrozen” at temperatures below250 K (i.e., melting when cooling). The transition between these two states occurs within a smalltemperature range (~10 K). Such a behavior is not observed for smaller dendrimer generations (G = 1and 3). This effect is likely related to the low critical solution temperature (LCST) of PEG and is caused bydendrimer conformations, in which the PEG group concentration in the layer increases with growing G.We suppose that the unusual behavior of PEG fragments in dendrimers will be interesting for practicalapplications such as nanocontainers or nanoreactors.</p

Machine learning and big data analytics in bipolar disorder:A position paper from the International Society for Bipolar Disorders Big Data Task Force

Objectives The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD. Method A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD. Results The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding. Conclusion Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.Peer reviewe