Electrochemical
reduction of CO2 is a value-added approach
to both decrease the atmospheric emission of carbon dioxide and form
valuable chemicals. We present a zero gap electrolyzer cell, which
continuously converts gas phase CO2 to products without
using any liquid catholyte. This is the first report of a multilayer
CO2 electrolyzer stack for scaling up the electrolysis
process. CO formation with partial current densities above 250 mA
cm–2 were achieved routinely, which was further
increased to 300 mA cm–2 (with ∼95% faradic
efficiency) by pressurizing the CO2 inlet (up to 10 bar).
Evenly distributing the CO2 gas among the layers, the electrolyzer
operates identically to the sum of multiple single-layer electrolyzer
cells. When passing the CO2 gas through the layers consecutively,
the CO2 conversion efficiency increased. The electrolyzer simultaneously provides high partial current density, low
cell voltage (−3.0 V), high conversion efficiency (up to 40%),
and high selectivity for CO production.
This paper reports on a novel continuous-flow hydrogenation reactor and its integration with a liquid handler to generate a fully automated high-throughput hydrogenation system for library synthesis. The reactor, named the H-Cube, combines endogenous hydrogen generation from the electrolysis of water with a continuous flow-through system. The system makes significant advances over current batch hydrogenation reactors in terms of safety, reaction validation efficiency, and rates of reaction. The hydrogenation process is described along with a detailed description of the device's main parts. The reduction of a series of functional groups, varying in difficulty up to 70 degrees C and 70 bar are also described. The paper concludes with the integration of the device into an automated liquid handler followed by the reduction of a nitro compound in a high throughput manner. The system is fully automated and can conduct 5 reactions in the time it takes to perform and workup one reaction manually on a standard batch reactor.
A method has been developed for the estimation of the distribution coefficient (D), which considers the microspecies of a compound. D is calculated from the microscopic dissociation constants (microconstants), the partition coefficients of the microspecies, and the counterion concentration. A general equation for the calculation of D at a given pH is presented. The microconstants are calculated from the structure using Hammett and Taft equations. The partition coefficients of the ionic microspecies are predicted by empirical equations using the dissociation constants and the partition coefficient of the uncharged species, which are estimated from the structure by a Linear Free Energy Relationship method. The algorithm is implemented in a program module called PrologD.
The high attrition rate of drug candidates during clinical trials for poor pharmacokinetic and metabolic properties has created a need to do these studies as early as it is possible during the drug discovery process. In addition the most successful drug is often not the most potent one but the one that has the suitable level of potency, safety, and pharmacokinetics. Science and technology development during the last few years and the generation of last databases and information has created the basis for doing early experimental PK and ADME studies in addition to eADME. Similarly, testing safety features as early as possible is key to affordable drug discovery and development. Throughput and cost are crucial for early application. In silico methods have by far the highest throughput, followed by the in vitro and in vivo approaches. On the other hand, with regard to relevance and reliability of data the ranking is the opposite. The great challenge for in silico methods is generation of models that correlate more closely with in vivo systems. For the in vitro assays increasing the throughput is an absolute must. Ex silico methods that combine in silico predictions with experimental methods are new additions to the scientific repertoire (e.g. Chromatographic Hydrophobicity Index that is deduced from the reverse phase HPLC data can be used for calculation of lipophilicity). The emerging new approaches have clear impact on the design of early stage screening and combinatorial libraries. In addition to the Lipinski's rules descriptors such as number of rotatable bonds, number of aromatic rings, branching behavior and polar surface area (PSA) are commonly used is the drug design process.
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