(Table 1) Chemical composition of Central Pacific manganese micronodules containing birnessite as a major component


Autoria(s): Drits, Victor A; Petrova, VV; Gorshkov, Anatoly I; Sval'nov, Vyacheslav N; Sokolova, Alla L; Sivtsov, Alexander V; Karpova, GV
Cobertura

MEDIAN LATITUDE: 10.015567 * MEDIAN LONGITUDE: -146.460567 * SOUTH-BOUND LATITUDE: 10.005000 * WEST-BOUND LONGITUDE: -146.510900 * NORTH-BOUND LATITUDE: 10.034200 * EAST-BOUND LONGITUDE: -146.420800 * MINIMUM DEPTH, sediment/rock: 1.60 m * MAXIMUM DEPTH, sediment/rock: 3.55 m

Data(s)

09/03/1985

Resumo

Detailed mineralogical characterization of micronodules is given. The main regularities of variations in composition of micronodules from Central Pacific sedimentary rocks of different ages are revealed. New data on structure and structural features of manganese minerals are reported.

Formato

text/tab-separated-values, 52 data points

Identificador

https://doi.pangaea.de/10.1594/PANGAEA.777332

doi:10.1594/PANGAEA.777332

Idioma(s)

en

Publicador

PANGAEA

Direitos

CC-BY: Creative Commons Attribution 3.0 Unported

Access constraints: unrestricted

Fonte

P.P. Shirshov Institute of Oceanology, Russian Academy of Sciences, Moscow

Supplement to: Drits, Victor A; Petrova, VV; Gorshkov, Anatoly I; Sval'nov, Vyacheslav N; Sokolova, Alla L; Sivtsov, Alexander V; Karpova, GV (1985): Manganese minerals of Fe-Mn micronodules in sediments from the Central Pacific and their post-sedimentary transformations. Litologiya i Poleznyye Iskopaemyye (Lithology and Mineral Resources), 20(3), 17-39

Palavras-Chave #Aluminium oxide; Archive of Ocean Data; ARCOD; Calcium oxide; Calculated; DEPTH, sediment/rock; DM28; DM28-2483-13; DM28-2483-36; DM28-2483-39; Dmitry Mendeleev; Elevation of event; Equatorial Pacific; Event label; GC; Gravity corer; Iron oxide, Fe2O3; Latitude of event; Longitude of event; Magnesium oxide; Manganese dioxide; Nickel oxide; Number of observations; Polysolenia sp.; Potassium oxide; Silicon dioxide; Sum; Titanium oxide; Water in rock; Wet chemistry
Tipo

Dataset