The formation of adsorption properties of nanoporous materials by thermochemical activation

Nanoporous materials (NPMs) selectively adsorb and concentrate gases into their pores, and thus could be used to store, capture, and sense many different gases. Modularly synthesized classes of NPMs, such as covalent organic frameworks (COFs), offer a large number of candidate structures for each adsorption task. A complete NPM-property table, containing measurements of the relevant adsorption properties in the candidate NPMs, would enable the matching of NPMs with adsorption tasks. However, in practice the NPM-property matrix is only partially observed (incomplete); (i) many properties of any given NPM have not been measured and (ii) any given property has not been measured for all NPMs. The idea in this work is to leverage the observed (NPM, property) values to impute the missing ones. Similarly, commercial recommendation systems impute missing entries in an incomplete item-customer ratings matrix to recommend items to customers. We demonstrate a COF recommendation system to match COFs with adsorption tasks by training a low rank model of an incomplete COF--adsorption-property matrix. A low rank model, trained on the observed (COF, adsorption property) values, provides (i) predictions of the missing (COF, adsorption property) values and (ii) a "map" of COFs, wherein COFs with similar (dissimilar) adsorption properties congregate (separate). We find the performance of the COF recommendation system varies for different adsorption tasks and diminishes precipitously when the fraction of missing entries exceeds 60%. The concepts in our COF recommendation system can be applied broadly to many different materials and properties.

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A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

10.26434/chemrxiv.14205929.v1 ◽

2021 ◽

Author(s):

Arni Sturluson ◽

Ali Raza ◽

Grant D. McConachie ◽

Daniel Siderius ◽

Xiaoli Fern ◽

...

Keyword(s):

Recommendation System ◽

Adsorption Property ◽

Property Values ◽

Recommendation Systems ◽

Nanoporous Materials ◽

Adsorption Properties ◽

Low Rank ◽

Covalent Organic Frameworks ◽

Partially Observed ◽

Different Gases

Nanoporous materials (NPMs) selectively adsorb and concentrate gases into their pores, and thus could be used to store, capture, and sense many different gases. Modularly synthesized classes of NPMs, such as covalent organic frameworks (COFs), offer a large number of candidate structures for each adsorption task. A complete NPM-property table, containing measurements of the relevant adsorption properties in the candidate NPMs, would enable the matching of NPMs with adsorption tasks. However, in practice the NPM-property matrix is only partially observed (incomplete); (i) many properties of any given NPM have not been measured and (ii) any given property has not been measured for all NPMs. The idea in this work is to leverage the observed (NPM, property) values to impute the missing ones. Similarly, commercial recommendation systems impute missing entries in an incomplete item-customer ratings matrix to recommend items to customers. We demonstrate a COF recommendation system to match COFs with adsorption tasks by training a low rank model of an incomplete COF--adsorption-property matrix. A low rank model, trained on the observed (COF, adsorption property) values, provides (i) predictions of the missing (COF, adsorption property) values and (ii) a "map" of COFs, wherein COFs with similar (dissimilar) adsorption properties congregate (separate). We find the performance of the COF recommendation system varies for different adsorption tasks and diminishes precipitously when the fraction of missing entries exceeds 60%. The concepts in our COF recommendation system can be applied broadly to many different materials and properties.

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A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

10.26434/chemrxiv.14205929.v2 ◽

2021 ◽

Author(s):

Arni Sturluson ◽

Ali Raza ◽

Grant D. McConachie ◽

Daniel Siderius ◽

Xiaoli Fern ◽

...

Keyword(s):

Recommendation System ◽

Adsorption Property ◽

Property Values ◽

Recommendation Systems ◽

Nanoporous Materials ◽

Adsorption Properties ◽

Low Rank ◽

Covalent Organic Frameworks ◽

Partially Observed ◽

Different Gases

Nanoporous materials (NPMs) selectively adsorb and concentrate gases into their pores, and thus could be used to store, capture, and sense many different gases. Modularly synthesized classes of NPMs, such as covalent organic frameworks (COFs), offer a large number of candidate structures for each adsorption task. A complete NPM-property table, containing measurements of the relevant adsorption properties in the candidate NPMs, would enable the matching of NPMs with adsorption tasks. However, in practice the NPM-property matrix is only partially observed (incomplete); (i) many properties of any given NPM have not been measured and (ii) any given property has not been measured for all NPMs. The idea in this work is to leverage the observed (NPM, property) values to impute the missing ones. Similarly, commercial recommendation systems impute missing entries in an incomplete item-customer ratings matrix to recommend items to customers. We demonstrate a COF recommendation system to match COFs with adsorption tasks by training a low rank model of an incomplete COF--adsorption-property matrix. A low rank model, trained on the observed (COF, adsorption property) values, provides (i) predictions of the missing (COF, adsorption property) values and (ii) a "map" of COFs, wherein COFs with similar (dissimilar) adsorption properties congregate (separate). We find the performance of the COF recommendation system varies for different adsorption tasks and diminishes precipitously when the fraction of missing entries exceeds 60%. The concepts in our COF recommendation system can be applied broadly to many different materials and properties.

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Nanoporous materials and their adsorption properties

Protection of Metals and Physical Chemistry of Surfaces ◽

10.1134/s2070205109020014 ◽

2009 ◽

Vol 45 (2) ◽

pp. 121-136 ◽

Cited By ~ 17

Author(s):

A. A. Fomkin

Keyword(s):

Nanoporous Materials ◽

Adsorption Properties

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Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

10.33774/chemrxiv-2021-lfljk-v4 ◽

2021 ◽

Author(s):

Arni Sturluson ◽

Ali Raza ◽

Grant D. McConachie ◽

Daniel Siderius ◽

Xiaoli Fern ◽

...

Keyword(s):

Recommendation System ◽

Adsorption Property ◽

Property Values ◽

Recommendation Systems ◽

Nanoporous Materials ◽

Adsorption Properties ◽

Low Rank ◽

Covalent Organic Frameworks ◽

Partially Observed ◽

Different Gases

Nanoporous materials (NPMs) selectively adsorb and concentrate gases into their pores, and thus could be used to store, capture, and sense many different gases. Modularly synthesized classes of NPMs, such as covalent organic frameworks (COFs), offer a large number of candidate structures for each adsorption task. A complete NPM-property table, containing measurements of the relevant adsorption properties in the candidate NPMs, would enable the matching of NPMs with adsorption tasks. However, in practice the NPM-property matrix is only partially observed (incomplete); (i) many properties of any given NPM have not been measured and (ii) any given property has not been measured for all NPMs. The idea in this work is to leverage the observed (NPM, property) values to impute the missing ones. Similarly, commercial recommendation systems impute missing entries in an incomplete item-customer ratings matrix to recommend items to customers. We demonstrate a COF recommendation system to match COFs with adsorption tasks by training a low rank model of an incomplete COF--adsorption-property matrix. A low rank model, trained on the observed (COF, adsorption property) values, provides (i) predictions of the missing (COF, adsorption property) values and (ii) a "map" of COFs, represented as points, wherein COFs with similar (dissimilar) adsorption properties congregate (separate). We find the performance of the COF recommendation system varies for different adsorption tasks and diminishes precipitously when the fraction of missing entries exceeds 60%. The concepts in our COF recommendation system can be applied broadly to many different materials and properties.

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Formation of Adsorption Properties of Carbon Nanostructured Materials by Thermochemical Activation of Sludge-Lignin

Lesnoy Zhurnal (Forestry Journal) ◽

10.37482/0536-1036-2021-4-181-189 ◽

2021 ◽

pp. 181-189

Author(s):

Konstantin Vorontsov ◽

◽

Nikolay Bogdanovich ◽

Elena Sedova ◽

Polina Solovyova ◽

...

Keyword(s):

Methylene Blue ◽

Liquid Phase ◽

Sodium Hydroxide ◽

Adsorption Capacity ◽

Nanostructured Materials ◽

Response Surfaces ◽

Adsorption Properties ◽

Adsorption Activity ◽

Thermochemical Activation ◽

Methylene Blue Removal

Pyrolysis is an effective way to process waste of the pulp and paper industry, in particular, sludge-lignin, which makes it possible to obtain a commercial product – a carbon adsorbent. The method of thermochemical activation using sodium and potassium hydroxides is now widely used in pyrolysis of wood waste processing. This method enables the production of carbon nanostructured materials with high adsorption properties, especially when adsorbed from the liquid phase. The paper studies the influence of conditions for the synthesis of carbon adsorbents of sludge-lignin on their adsorption properties using sodium hydroxide as an activating agent. Sludge-lignin was obtained under laboratory conditions by treating lignin-containing wastewater with aluminum oxychloride coagulant. We applied the method of the planned experiment: a rotatable central composite design of the second order for three factors. We studied the influence of the main factors determining the adsorption properties of coals, namely, temperature, pyrolysis duration and sodium hydroxide dosage, on the values of output parameters characterizing the adsorption efficiency from the liquid phase, i.e. the iodine number and the adsorption capacity of methylene blue removal. We obtained experimental data, which were used to construct response surfaces illustrating the influence of the experimental factors on the output parameters. The positive effect of pyrolysis temperature and alkali dosage on the adsorption properties of the synthesized coals was found. The following results were obtained: the adsorption activity for iodine – 300 %, for methylene blue – 1000 mg/g; indicating a developed micro- and mesoporous surface and the possibility of using these compounds for adsorption of both gases and vapors, and organic substances from solutions. Therefore, samples of activated carbons synthesized from sludge-lignin were tested as lignin adsorbents of lignin-containing wastewater. The obtained dependences correlate well with the data describing the influence of pyrolysis parameters on the coal adsorption capacity of methylene blue removal. The high efficiency of adsorbents of sludge-lignin in the removal of lignin from solutions was shown. The value of the specific adsorption was about 1500 mg/g.

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Synthesis, Characterization, and Adsorption Properties of Nanoporous Materials

Applied Surface Science ◽

10.5772/intechopen.83355 ◽

2019 ◽

Author(s):

Rolando Roque-Malherbe ◽

Carlos de las Pozas del Rio

Keyword(s):

Nanoporous Materials ◽

Adsorption Properties

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A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

10.26434/chemrxiv.14205929.v3 ◽

2021 ◽

Author(s):

Arni Sturluson ◽

Ali Raza ◽

Grant D. McConachie ◽

Daniel Siderius ◽

Xiaoli Fern ◽

...

Keyword(s):

Recommendation System ◽

Adsorption Property ◽

Property Values ◽

Recommendation Systems ◽

Nanoporous Materials ◽

Adsorption Properties ◽

Low Rank ◽

Covalent Organic Frameworks ◽

Partially Observed ◽

Different Gases

Nanoporous materials (NPMs) selectively adsorb and concentrate gases into their pores, and thus could be used to store, capture, and sense many different gases. Modularly synthesized classes of NPMs, such as covalent organic frameworks (COFs), offer a large number of candidate structures for each adsorption task. A complete NPM-property table, containing measurements of the relevant adsorption properties in the candidate NPMs, would enable the matching of NPMs with adsorption tasks. However, in practice the NPM-property matrix is only partially observed (incomplete); (i) many properties of any given NPM have not been measured and (ii) any given property has not been measured for all NPMs. The idea in this work is to leverage the observed (NPM, property) values to impute the missing ones. Similarly, commercial recommendation systems impute missing entries in an incomplete item-customer ratings matrix to recommend items to customers. We demonstrate a COF recommendation system to match COFs with adsorption tasks by training a low rank model of an incomplete COF--adsorption-property matrix. A low rank model, trained on the observed (COF, adsorption property) values, provides (i) predictions of the missing (COF, adsorption property) values and (ii) a "map" of COFs, represented as points, wherein COFs with similar (dissimilar) adsorption properties congregate (separate). We find the performance of the COF recommendation system varies for different adsorption tasks and diminishes precipitously when the fraction of missing entries exceeds 60%. The concepts in our COF recommendation system can be applied broadly to many different materials and properties.

Download Full-text