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  • Brand : BIOFRON

  • Catalogue Number : BN-O1177

  • Specification : 98%(HPLC)

  • CAS number : 5192-03-0

  • Formula : C8H8N2

  • Molecular Weight : 132.16

  • PUBCHEM ID : 78867

  • Volume : 5mg

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Catalogue Number


Analysis Method





Molecular Weight



Botanical Source

Structure Type









1.3±0.1 g/cm3


Flash Point

195.0±7.6 °C

Boiling Point

354.0±15.0 °C at 760 mmHg

Melting Point

131-133 °C (dec.)(lit.)


InChl Key


WGK Germany


HS Code Reference

Personal Projective Equipment

Correct Usage

For Reference Standard and R&D, Not for Human Use Directly.

Meta Tag

provides coniferyl ferulate(CAS#:5192-03-0) MSDS, density, melting point, boiling point, structure, formula, molecular weight etc. Articles of coniferyl ferulate are included as well.>> amp version: coniferyl ferulate

No Technical Documents Available For This Product.




Valine dehydrogenase was purified to homogeneity from the crude extracts of Streptomyces aureofaciens. The molecular weight of the native enzyme was 116,000 by equilibrium ultracentrifugation and 118,000 by size exclusion high-performance liquid chromatography. The enzyme was composed of four subunits with molecular weights of 29,000. The isoelectric point was 5.1. The enzyme required NAD+ as a cofactor, which could not be replaced by NADP+. Sulfhydryl reagents inhibited the enzyme activity. The pH optimum was 10.7 for oxidative deamination of L-valine and 9.0 for reductive amination of alpha-ketoisovalerate. The Michaelis constants were 2.5 mM for L-valine and 0.10 mM for NAD+. For reductive amination the Km values were 1.25 mM for alpha-ketoisovalerate, 0.023 mM for NADH, and 18.2 mM for NH4Cl.


Isolation and characterization of valine dehydrogenase from Streptomyces aureofaciens.


I Vancurova, A Vancura, J Volc, J Neuzil, M Flieger, G Basarova, and V Bĕhal

Publish date

1988 Nov;




Chronic diseases are becoming more widespread each year in developed countries, mainly due to increasing life expectancy. Among them, diabetes mellitus (DM) and essential hypertension (EH) are two of the most prevalent ones. Furthermore, they can be the onset of other chronic conditions such as kidney or obstructive pulmonary diseases. The need to comprehend the factors related to such complex diseases motivates the development of interpretative and visual analysis methods, such as classification trees, which not only provide predictive models for diagnosing patients, but can also help to discover new clinical insights.

In this paper, we analyzed healthy and chronic (diabetic, hypertensive) patients associated with the University Hospital of Fuenlabrada in Spain. Each patient was classified into a single health status according to clinical risk groups (CRGs). The CRGs characterize a patient through features such as age, gender, diagnosis codes, and drug codes. Based on these features and the CRGs, we have designed classification trees to determine the most discriminative decision features among different health statuses. In particular, we propose to make use of statistical data visualizations to guide the selection of features in each node when constructing a tree. We created several classification trees to distinguish among patients with different health statuses. We analyzed their performance in terms of classification accuracy, and drew clinical conclusions regarding the decision features considered in each tree. As expected, healthy patients and patients with a single chronic condition were better classified than patients with comorbidities. The constructed classification trees also show that the use of antipsychotics and the diagnosis of chronic airway obstruction are relevant for classifying patients with more than one chronic condition, in conjunction with the usual DM and/or EH diagnoses.

We propose a methodology for constructing classification trees in a visually guided manner. The approach allows clinicians to progressively select the decision features at each of the tree nodes. The process is guided by exploratory data analysis visualizations, which may provide new insights and unexpected clinical information.


Chronic health status, Diabetes, Hypertension, Multivariate visualization, Classification trees, Diagnoses, Drugs


Visually guided classification trees for analyzing chronic patients


Cristina Soguero-Ruiz,corresponding author#1 Inmaculada Mora-Jimenez,#1 Miguel A. Mohedano-Munoz,2 Manuel Rubio-Sanchez,#2 Pablo de Miguel-Bohoyo,3 and Alberto Sanchez#2,4

Publish date





In this work, we demonstrate that a preferential Ga-for-Zn cation exchange is responsible for the increase in photoluminescence that is observed when gallium oleate is added to InZnP alloy QDs. By exposing InZnP QDs with varying Zn/In ratios to gallium oleate and monitoring their optical properties, composition, and size, we conclude that Ga3+ preferentially replaces Zn2+, leading to the formation of InZnP/InGaP core/graded-shell QDs. This cation exchange reaction results in a large increase of the QD photoluminescence, but only for InZnP QDs with Zn/In ≥ 0.5. For InP QDs that do not contain zinc, Ga is most likely incorporated only on the quantum dot surface, and a PL enhancement is not observed. After further growth of a GaP shell and a lattice-matched ZnSeS outer shell, the cation-exchanged InZnP/InGaP QDs continue to exhibit superior PL QY (over 70%) and stability under long-term illumination (840 h, 5 weeks) compared to InZnP cores with the same shells. These results provide important mechanistic insights into recent improvements in InP-based QDs for luminescent applications.


Ga for Zn Cation Exchange Allows for Highly Luminescent and Photostable InZnP-Based Quantum Dots


Francesca Pietra,†# Nicholas Kirkwood,†# Luca De Trizio,‡ Anne W. Hoekstra,† Lennart Kleibergen,† Nicolas Renaud,† Rolf Koole,∥ Patrick Baesjou,∥⊥ Liberato Manna,‡§ and Arjan J. Houtepen*†

Publish date

2017 Jun 27

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