How many mineral species are there

The group of carbonates is made up of two important minerals: calcite, a calcium carbonate that forms calcareous rocks and dolomite, a calcium and magnesium carbonate that forms dolomite rocks. These minerals and their corresponding rocks melt in water, and form karstic landscapes and dolomitic mountain landscapes. This is the group of sulphates and salts. When oxygen binds to other elements such as iron, oxides and hydroxides are formed. Some examples are magnetite, limonite, haematite, which form rocks with a yellow-red colour, and represent the main iron source of the mining industry Other important mineral deposits are represented by sulphides , minerals containing sulphur and iron, like pyrite.

Sulphur together with iron and copper leads to chalcopyrite. Sulphur together with lead originates galena, while sulphur and mercury results in cinnabar gold,silver and copper are deposits made of only one element.

This is why they are called native elements. Both diamond and graphite are exclusively made of carbon, but they differ from the commercial and crystal structure point of view, as explained in the corresponding paragraph.

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Clilinaction CLIL will no longer be a secret with"clil in action"! Smartenglish Improve your English with Smartenglish! Credit: Morrison et al. Network diagrams can take many forms and shapes but typically consist of two main features: points, or nodes, each of which represents a single entity — a particular mineral, for example — and connecting lines, or edges, which denote some relationship between two nodes.

At their most basic, these diagrams can be simple and largely qualitative, as in friend networks or family trees, where connections imply direct relationships — whether by social interaction or genealogy — but little more. Or they can be made to hold much more information: Nodes can be different sizes, shapes and colors based on certain qualities of whatever it is they represent. Connections, meanwhile, can be different lengths or weights, for example, to indicate the duration or status of a relationship, or perhaps the number of places where two minerals are known to co-occur.

There is virtually an unlimited number of ways to view and parse large datasets with network diagrams. Researchers can set up networks to answer particular questions or, alternatively, if the goal is more explorative, to search for potential lines of inquiry.

Morrison, Hazen and their colleagues are still in the early days of pioneering quantitative network analysis tools for mineralogy. In the study, led by Morrison, the team used numerical algorithms to plot network diagrams and illuminate relationships in various mineral subsets, again based on data from Mindat. Displaying information about existing minerals in such diagrams could help direct researchers looking for unknown minerals or ore deposits, they wrote.

In some diagrams, nodes signified individual minerals, node sizes reflected the commonness of each, and the length of connections represented how often minerals co-exist at the same locations. Accounting for all of the data simultaneously, the algorithms iteratively adjust the nodes' positions to minimize the volume of the overall diagram while maintaining the correct relative distances among the different nodes.

The observation that chromium minerals — whether igneous, metamorphic, hydrothermal, etc. It also reveals finer-grained insights, the team noted, including exceptions to the segregation rule; for example, some abundant chromium minerals, like chromite, as well as certain hydrothermally formed species, do co-occur with many species from other paragenetic groups.

Network diagrams of copper minerals colorful nodes known to have existed in the Archean Eon left and the Cenozoic Era right show how copper mineral diversity has grown dramatically through geologic history.

Archean copper mineralogy was dominated by copper sulfide minerals; in the Cenozoic, sulfides are still prominent but oxygen-containing groups like sulfates, carbonates and phosphates have also proliferated.

Black nodes in these "bipartite" diagrams represent regions in which connected copper minerals occur. The researchers also created a pair of networks that incorporated two different types of nodes instead of just one ; the nodes signified either a particular copper-bearing mineral or a locality where copper minerals are found.

One of the networks included sites and minerals dating to the Archean Eon, while the other related to the Cenozoic Era. This allowed the team to compare mineral trends within and between time periods separated by 2. In the recent Cenozoic, from which there is far greater copper mineral diversity known, sulfides are still prominent but oxygen-containing groups like sulfates, carbonates and phosphates have also proliferated, particularly among the many rare copper minerals.

And, Hazen adds, the potential value of this transition from description to prediction is already being demonstrated. Data science techniques could be useful in analyzing mineral assemblages at established ore deposits, which could then shed light on where other potentially valuable deposits are located.

They predicted that at least an additional carbon minerals have yet to be found, and suggested, based on additional analyses of subgroups of existing carbon minerals minerals containing both carbon and oxygen, or carbon and calcium, for example , that most of these undiscovered species are likely to be hydrous carbonates. They also offered several hundred plausible compositions for the unknown minerals. Since then, 13 new carbon minerals have been discovered , six of which are hydrous carbonates, and two of which were found in samples from a mining district in the Czech Republic that the team had specifically cited as a likely spot for many undiscovered species.

Furthermore, two of the 13 finds — dubbed abellaite and parisite- La — have compositions the team specifically proposed. The approaches the team is developing may also be useful in revealing new or underutilized deposits of existing mineral resources. With respect to minerals, the idea is to be able to look at known mineral assemblages and give probabilities for the occurrence of other minerals. In late April, the researchers held a workshop at the Colorado School of Mines to meet with economic geologists and discuss applications in terms of resource discovery.

That followed a workshop with the U. Geological Survey USGS , which considered how big data and data science approaches like theirs could improve resource assessments. Abellaite left and parisite- La right , both rare carbonate minerals, are two of more than a dozen carbon-bearing minerals newly recognized in the last couple of years.

USGS is still scoping out how data science techniques could be applied in its assessments, Robinson says. One potential use is analyzing patterns in mineral assemblages at established resource deposits, which could then shed light on other potentially valuable localities recorded in Mindat and other databases. In addition to aiding in mineral discovery and resource assessments, applying data science methods in mineralogy may also be useful in the classroom, where interactive network diagrams loaded with information about minerals and their relationships in nature could supplement or replace traditional teaching tools, Hazen and Morrison say.

And there are uses in planetary science. In addition to illuminating mineral diversity on Earth, data science methods could reveal information about the mineralogy and history of Mars and other rocky planets.

He notes other recent collaborations his team has struck up with paleontologists looking to quantify missing portions of the fossil record, which could impact our understanding of extinction events or evolutionary rates, as well as microbial ecologists looking to apply network analysis to better understand the influence of environmental factors like local mineralogy and geochemistry on microbial communities.

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