catena-Poly[[dibromidomercury(II)]-μ-3-(1-methyl­pyrrolidin-2-yl)pyridine-κ2 N:N′]

In the title polymeric complex, [HgBr2(C10H14N2)]n, each nicotine mol­ecule is bonded to two adjacent Hg atoms, one through the pyrrolidine N atom and the other through the pyridine N atom, forming zigzag chains along [010]. The coordination around mercury is completed by two bromido ligands resulting in a distorted tetra­hedral arrangement

Bias-Flip Technique for Frequency Tuning of Piezo-Electric Energy Harvesting Devices

Devices that harvest electrical energy from mechanical vibrations have the problem that the frequency of the source vibration is often not matched to the resonant frequency of the energy harvesting device. Manufacturing tolerances make it difficult to match the Energy Harvesting Device (EHD) resonant frequency to the source vibration frequency, and the source vibration frequency may vary with time. Previous work has recognized that it is possible to tune the resonant frequency of an EHD using a tunable, reactive impedance at the output of the device. The present paper develops the theory of electrical tuning, and proposes the Bias-Flip (BF) technique, to implement this tunable, reactive impedance

catena-Poly[[dipyridine­mercury(II)]-μ-5-amino-2,4,6-triiodo­isophthalato]

The reaction of mercury(II) chloride with 5-amino-2,4,6-triiodo­isophthalic acid in pyridine solution leads to the formation of the title compound, [Hg(C8H2I3NO4)(C5H5N)2]n. The structure contains a four-coordinate Hg2+ ion in a distorted tetra­hedral geometry, which lies on a crystallographic twofold axis. The Hg2+ ion is bonded to two N atoms from two pyridine ligands and two carboxylate O atoms from two 5-amino-2,4,6-triiodo­isophthalate anions. The two carboxyl­ate groups of individual 5-amino-2,4,6-triiodo­isophthalate anions act as a bridge to the Hg centers. This anion also resides on a twofold axis, which passes through the amino N and the trans standing I atoms. The Hg—O distance is 2.337 (6) and the Hg—N distance is 2.244 (8) Å

catena-Poly[[[aqua­tripyridine­cobalt(II)]-μ-5-amino-2,4,6-triiodoisophthalato-κ2 O 1:O 3] pyridine solvate]

The reaction of cobalt(II) nitrate with 5-amino-2,4,6-tri­iodo­isophthalic acid (ATPA) in pyridine solution leads to the formation of the title compound, {[Co(C8H2I3NO4)(C5H5N)3(H2O)]·C5H5N}n. The Co2+ ion is six-coordinated by three N atoms, one water O atom and two O atoms from two ATPA ligands to form a distorted octa­hedral geometry. The two carboxyl­ate groups of ATPA act as bridging ligands connecting the CoII metal centers to form one-dimensional zigzag chains along the c axis, with Co—O distances in the range 2.104 (4)–2.135 (4) Å. The average Co—N distance is 2.171 Å. A classical O—H⋯N hydrogen bond is formed by the coordinating water mol­ecule and the pyridine solvent mol­ecule. The structure was refined from a racemically twinned crystal with a twin ratio of approximately 8:1

Tris{N-[(anthracen-9-yl)methyl­ene­amino]thio­ureato}cobalt(III) tetra­hydrate

In the title complex, [Co(C16H12N3S)3]·4H2O, the central CoIII atom is in a distorted octa­hedral coordination environment. There are three N-[(anthracen-9-yl)­methyl­ene­amino]­thio­ureate ligands coordinated to the CoIII atom via three imine N and three thio­amide S atoms. The Co—S and Co—N bond distances are in expected ranges [2.2194 (8)—2.2545 (8) and 1.926 (2)—1.985 (2)Å, respectively]. The endocyclic S—Co—N bond angles in the five-membered chelate rings range from 82.91 (7) to 85.33 (7)°. The structure contains four water mol­ecules which are disordered over 12 sites and link the complex mol­ecules into a three-dimensional network through N—H⋯O, O—H⋯O, O—H⋯N, and O—H⋯S hydrogen bonds

Characterization of Flavor Profile of "Nanx Wudl" Sour Meat Fermented from Goose and Pork Using Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) Combined with Electronic Nose and Tongue

Sour meat is a highly appreciated traditional fermented product, mainly from the Guizhou, Yunnan, and Hunan provinces. The flavor profiles of sour meat from goose and pork were evaluated using gas chromatography-ion mobility spectrometry (GC-IMS) combined with an electronic nose (E-nose) and tongue (E-tongue). A total of 94 volatile compounds were characterized in fermented sour meat from both pork and goose using GC-IMS. A data-mining protocol based on univariate and multivariate analyses revealed that the source of the raw meat plays a crucial role in the formation of flavor compounds during the fermentation process. In detail, sour meat from pork contained higher levels of hexyl acetate, sotolon, heptyl acetate, butyl propanoate, hexanal, and 2-acetylpyrrole than sour goose meat. In parallel, sour meat from goose showed higher levels of 4-methyl-3-penten-2-one, n-butyl lactate, 2-butanol, (E)-2-nonenal, and decalin than sour pork. In terms of the odor and taste response values obtained by the E-nose and E-tongue, a robust principal component model (RPCA) could effectively differentiate sour meat from the two sources. The present work could provide references to investigate the flavor profiles of traditional sour meat products fermented from different raw meats and offer opportunities for a rapid identification method based on flavor profiles

H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps

Solving real-world complex tasks using reinforcement learning (RL) without high-fidelity simulation environments or large amounts of offline data can be quite challenging. Online RL agents trained in imperfect simulation environments can suffer from severe sim-to-real issues. Offline RL approaches although bypass the need for simulators, often pose demanding requirements on the size and quality of the offline datasets. The recently emerged hybrid offline-and-online RL provides an attractive framework that enables joint use of limited offline data and imperfect simulator for transferable policy learning. In this paper, we develop a new algorithm, called H2O+, which offers great flexibility to bridge various choices of offline and online learning methods, while also accounting for dynamics gaps between the real and simulation environment. Through extensive simulation and real-world robotics experiments, we demonstrate superior performance and flexibility over advanced cross-domain online and offline RL algorithms