A biomass derived porous carbon materials with adjustable interfacial electron transmission dynamics as highly-efficient air cathode for Zn-Air battery
• Biomass-derived high-efficiency ORR catalyst that can be mass-produced. • The regulation of pore structure on charge transport was in-depth studied by TPV. • C111-900 outperforms 20% Pt/C in the operation of Zn-Air battery.
Gespeichert in:
| Autor*in: |
Zhou, Yunjie [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Deep Learning for predicting neutralities in Offensive Language Identification Dataset▪ - Sharma, Mayukh ELSEVIER, 2021, (including crystal engineering) : an international journal reporting research on the synthesis, structure, and properties of materials, New York, NY [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:153 ; year:2022 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.materresbull.2022.111908 |
|---|
| Katalog-ID: |
ELV058056483 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV058056483 | ||
| 003 | DE-627 | ||
| 005 | 20230625004028.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 220808s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.materresbull.2022.111908 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001802.pica |
| 035 | |a (DE-627)ELV058056483 | ||
| 035 | |a (ELSEVIER)S0025-5408(22)00180-5 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 004 |q VZ |
| 084 | |a 54.72 |2 bkl | ||
| 100 | 1 | |a Zhou, Yunjie |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a A biomass derived porous carbon materials with adjustable interfacial electron transmission dynamics as highly-efficient air cathode for Zn-Air battery |
| 264 | 1 | |c 2022 | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a • Biomass-derived high-efficiency ORR catalyst that can be mass-produced. • The regulation of pore structure on charge transport was in-depth studied by TPV. • C111-900 outperforms 20% Pt/C in the operation of Zn-Air battery. | ||
| 700 | 1 | |a Wu, Jie |4 oth | |
| 700 | 1 | |a Wang, Zhenzhen |4 oth | |
| 700 | 1 | |a Huang, Hui |4 oth | |
| 700 | 1 | |a Liu, Yang |4 oth | |
| 700 | 1 | |a Kang, Zhenhui |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Sharma, Mayukh ELSEVIER |t Deep Learning for predicting neutralities in Offensive Language Identification Dataset▪ |d 2021 |d (including crystal engineering) : an international journal reporting research on the synthesis, structure, and properties of materials |g New York, NY [u.a.] |w (DE-627)ELV006657850 |
| 773 | 1 | 8 | |g volume:153 |g year:2022 |g pages:0 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.materresbull.2022.111908 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 936 | b | k | |a 54.72 |j Künstliche Intelligenz |q VZ |
| 951 | |a AR | ||
| 952 | |d 153 |j 2022 |h 0 | ||
| author_variant |
y z yz |
|---|---|
| matchkey_str |
zhouyunjiewujiewangzhenzhenhuanghuiliuya:2022----:boaseieprucromtrasihdutbenefcaeetotasisodnmcahgl |
| hierarchy_sort_str |
2022 |
| bklnumber |
54.72 |
| publishDate |
2022 |
| allfields |
10.1016/j.materresbull.2022.111908 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001802.pica (DE-627)ELV058056483 (ELSEVIER)S0025-5408(22)00180-5 DE-627 ger DE-627 rakwb eng 004 VZ 54.72 bkl Zhou, Yunjie verfasserin aut A biomass derived porous carbon materials with adjustable interfacial electron transmission dynamics as highly-efficient air cathode for Zn-Air battery 2022 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Biomass-derived high-efficiency ORR catalyst that can be mass-produced. • The regulation of pore structure on charge transport was in-depth studied by TPV. • C111-900 outperforms 20% Pt/C in the operation of Zn-Air battery. Wu, Jie oth Wang, Zhenzhen oth Huang, Hui oth Liu, Yang oth Kang, Zhenhui oth Enthalten in Elsevier Sharma, Mayukh ELSEVIER Deep Learning for predicting neutralities in Offensive Language Identification Dataset▪ 2021 (including crystal engineering) : an international journal reporting research on the synthesis, structure, and properties of materials New York, NY [u.a.] (DE-627)ELV006657850 volume:153 year:2022 pages:0 https://doi.org/10.1016/j.materresbull.2022.111908 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 54.72 Künstliche Intelligenz VZ AR 153 2022 0 |
| spelling |
10.1016/j.materresbull.2022.111908 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001802.pica (DE-627)ELV058056483 (ELSEVIER)S0025-5408(22)00180-5 DE-627 ger DE-627 rakwb eng 004 VZ 54.72 bkl Zhou, Yunjie verfasserin aut A biomass derived porous carbon materials with adjustable interfacial electron transmission dynamics as highly-efficient air cathode for Zn-Air battery 2022 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Biomass-derived high-efficiency ORR catalyst that can be mass-produced. • The regulation of pore structure on charge transport was in-depth studied by TPV. • C111-900 outperforms 20% Pt/C in the operation of Zn-Air battery. Wu, Jie oth Wang, Zhenzhen oth Huang, Hui oth Liu, Yang oth Kang, Zhenhui oth Enthalten in Elsevier Sharma, Mayukh ELSEVIER Deep Learning for predicting neutralities in Offensive Language Identification Dataset▪ 2021 (including crystal engineering) : an international journal reporting research on the synthesis, structure, and properties of materials New York, NY [u.a.] (DE-627)ELV006657850 volume:153 year:2022 pages:0 https://doi.org/10.1016/j.materresbull.2022.111908 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 54.72 Künstliche Intelligenz VZ AR 153 2022 0 |
| allfields_unstemmed |
10.1016/j.materresbull.2022.111908 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001802.pica (DE-627)ELV058056483 (ELSEVIER)S0025-5408(22)00180-5 DE-627 ger DE-627 rakwb eng 004 VZ 54.72 bkl Zhou, Yunjie verfasserin aut A biomass derived porous carbon materials with adjustable interfacial electron transmission dynamics as highly-efficient air cathode for Zn-Air battery 2022 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Biomass-derived high-efficiency ORR catalyst that can be mass-produced. • The regulation of pore structure on charge transport was in-depth studied by TPV. • C111-900 outperforms 20% Pt/C in the operation of Zn-Air battery. Wu, Jie oth Wang, Zhenzhen oth Huang, Hui oth Liu, Yang oth Kang, Zhenhui oth Enthalten in Elsevier Sharma, Mayukh ELSEVIER Deep Learning for predicting neutralities in Offensive Language Identification Dataset▪ 2021 (including crystal engineering) : an international journal reporting research on the synthesis, structure, and properties of materials New York, NY [u.a.] (DE-627)ELV006657850 volume:153 year:2022 pages:0 https://doi.org/10.1016/j.materresbull.2022.111908 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 54.72 Künstliche Intelligenz VZ AR 153 2022 0 |
| allfieldsGer |
10.1016/j.materresbull.2022.111908 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001802.pica (DE-627)ELV058056483 (ELSEVIER)S0025-5408(22)00180-5 DE-627 ger DE-627 rakwb eng 004 VZ 54.72 bkl Zhou, Yunjie verfasserin aut A biomass derived porous carbon materials with adjustable interfacial electron transmission dynamics as highly-efficient air cathode for Zn-Air battery 2022 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Biomass-derived high-efficiency ORR catalyst that can be mass-produced. • The regulation of pore structure on charge transport was in-depth studied by TPV. • C111-900 outperforms 20% Pt/C in the operation of Zn-Air battery. Wu, Jie oth Wang, Zhenzhen oth Huang, Hui oth Liu, Yang oth Kang, Zhenhui oth Enthalten in Elsevier Sharma, Mayukh ELSEVIER Deep Learning for predicting neutralities in Offensive Language Identification Dataset▪ 2021 (including crystal engineering) : an international journal reporting research on the synthesis, structure, and properties of materials New York, NY [u.a.] (DE-627)ELV006657850 volume:153 year:2022 pages:0 https://doi.org/10.1016/j.materresbull.2022.111908 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 54.72 Künstliche Intelligenz VZ AR 153 2022 0 |
| allfieldsSound |
10.1016/j.materresbull.2022.111908 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001802.pica (DE-627)ELV058056483 (ELSEVIER)S0025-5408(22)00180-5 DE-627 ger DE-627 rakwb eng 004 VZ 54.72 bkl Zhou, Yunjie verfasserin aut A biomass derived porous carbon materials with adjustable interfacial electron transmission dynamics as highly-efficient air cathode for Zn-Air battery 2022 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Biomass-derived high-efficiency ORR catalyst that can be mass-produced. • The regulation of pore structure on charge transport was in-depth studied by TPV. • C111-900 outperforms 20% Pt/C in the operation of Zn-Air battery. Wu, Jie oth Wang, Zhenzhen oth Huang, Hui oth Liu, Yang oth Kang, Zhenhui oth Enthalten in Elsevier Sharma, Mayukh ELSEVIER Deep Learning for predicting neutralities in Offensive Language Identification Dataset▪ 2021 (including crystal engineering) : an international journal reporting research on the synthesis, structure, and properties of materials New York, NY [u.a.] (DE-627)ELV006657850 volume:153 year:2022 pages:0 https://doi.org/10.1016/j.materresbull.2022.111908 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 54.72 Künstliche Intelligenz VZ AR 153 2022 0 |
| language |
English |
| source |
Enthalten in Deep Learning for predicting neutralities in Offensive Language Identification Dataset▪ New York, NY [u.a.] volume:153 year:2022 pages:0 |
| sourceStr |
Enthalten in Deep Learning for predicting neutralities in Offensive Language Identification Dataset▪ New York, NY [u.a.] volume:153 year:2022 pages:0 |
| format_phy_str_mv |
Article |
| bklname |
Künstliche Intelligenz |
| institution |
findex.gbv.de |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
Deep Learning for predicting neutralities in Offensive Language Identification Dataset▪ |
| authorswithroles_txt_mv |
Zhou, Yunjie @@aut@@ Wu, Jie @@oth@@ Wang, Zhenzhen @@oth@@ Huang, Hui @@oth@@ Liu, Yang @@oth@@ Kang, Zhenhui @@oth@@ |
| publishDateDaySort_date |
2022-01-01T00:00:00Z |
| hierarchy_top_id |
ELV006657850 |
| dewey-sort |
14 |
| id |
ELV058056483 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV058056483</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625004028.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220808s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.materresbull.2022.111908</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001802.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV058056483</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0025-5408(22)00180-5</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhou, Yunjie</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A biomass derived porous carbon materials with adjustable interfacial electron transmission dynamics as highly-efficient air cathode for Zn-Air battery</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">• Biomass-derived high-efficiency ORR catalyst that can be mass-produced. • The regulation of pore structure on charge transport was in-depth studied by TPV. • C111-900 outperforms 20% Pt/C in the operation of Zn-Air battery.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Jie</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Zhenzhen</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Huang, Hui</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Yang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kang, Zhenhui</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Sharma, Mayukh ELSEVIER</subfield><subfield code="t">Deep Learning for predicting neutralities in Offensive Language Identification Dataset▪</subfield><subfield code="d">2021</subfield><subfield code="d">(including crystal engineering) : an international journal reporting research on the synthesis, structure, and properties of materials</subfield><subfield code="g">New York, NY [u.a.]</subfield><subfield code="w">(DE-627)ELV006657850</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:153</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.materresbull.2022.111908</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.72</subfield><subfield code="j">Künstliche Intelligenz</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">153</subfield><subfield code="j">2022</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| author |
Zhou, Yunjie |
| spellingShingle |
Zhou, Yunjie ddc 004 bkl 54.72 A biomass derived porous carbon materials with adjustable interfacial electron transmission dynamics as highly-efficient air cathode for Zn-Air battery |
| authorStr |
Zhou, Yunjie |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV006657850 |
| format |
electronic Article |
| dewey-ones |
004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
004 VZ 54.72 bkl A biomass derived porous carbon materials with adjustable interfacial electron transmission dynamics as highly-efficient air cathode for Zn-Air battery |
| topic |
ddc 004 bkl 54.72 |
| topic_unstemmed |
ddc 004 bkl 54.72 |
| topic_browse |
ddc 004 bkl 54.72 |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
j w jw z w zw h h hh y l yl z k zk |
| hierarchy_parent_title |
Deep Learning for predicting neutralities in Offensive Language Identification Dataset▪ |
| hierarchy_parent_id |
ELV006657850 |
| dewey-tens |
000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Deep Learning for predicting neutralities in Offensive Language Identification Dataset▪ |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV006657850 |
| title |
A biomass derived porous carbon materials with adjustable interfacial electron transmission dynamics as highly-efficient air cathode for Zn-Air battery |
| ctrlnum |
(DE-627)ELV058056483 (ELSEVIER)S0025-5408(22)00180-5 |
| title_full |
A biomass derived porous carbon materials with adjustable interfacial electron transmission dynamics as highly-efficient air cathode for Zn-Air battery |
| author_sort |
Zhou, Yunjie |
| journal |
Deep Learning for predicting neutralities in Offensive Language Identification Dataset▪ |
| journalStr |
Deep Learning for predicting neutralities in Offensive Language Identification Dataset▪ |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2022 |
| contenttype_str_mv |
zzz |
| container_start_page |
0 |
| author_browse |
Zhou, Yunjie |
| container_volume |
153 |
| class |
004 VZ 54.72 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Zhou, Yunjie |
| doi_str_mv |
10.1016/j.materresbull.2022.111908 |
| dewey-full |
004 |
| title_sort |
a biomass derived porous carbon materials with adjustable interfacial electron transmission dynamics as highly-efficient air cathode for zn-air battery |
| title_auth |
A biomass derived porous carbon materials with adjustable interfacial electron transmission dynamics as highly-efficient air cathode for Zn-Air battery |
| abstract |
• Biomass-derived high-efficiency ORR catalyst that can be mass-produced. • The regulation of pore structure on charge transport was in-depth studied by TPV. • C111-900 outperforms 20% Pt/C in the operation of Zn-Air battery. |
| abstractGer |
• Biomass-derived high-efficiency ORR catalyst that can be mass-produced. • The regulation of pore structure on charge transport was in-depth studied by TPV. • C111-900 outperforms 20% Pt/C in the operation of Zn-Air battery. |
| abstract_unstemmed |
• Biomass-derived high-efficiency ORR catalyst that can be mass-produced. • The regulation of pore structure on charge transport was in-depth studied by TPV. • C111-900 outperforms 20% Pt/C in the operation of Zn-Air battery. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U |
| title_short |
A biomass derived porous carbon materials with adjustable interfacial electron transmission dynamics as highly-efficient air cathode for Zn-Air battery |
| url |
https://doi.org/10.1016/j.materresbull.2022.111908 |
| remote_bool |
true |
| author2 |
Wu, Jie Wang, Zhenzhen Huang, Hui Liu, Yang Kang, Zhenhui |
| author2Str |
Wu, Jie Wang, Zhenzhen Huang, Hui Liu, Yang Kang, Zhenhui |
| ppnlink |
ELV006657850 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth oth oth |
| doi_str |
10.1016/j.materresbull.2022.111908 |
| up_date |
2024-07-06T17:56:15.789Z |
| _version_ |
1803853313648623616 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV058056483</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625004028.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220808s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.materresbull.2022.111908</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001802.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV058056483</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0025-5408(22)00180-5</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhou, Yunjie</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A biomass derived porous carbon materials with adjustable interfacial electron transmission dynamics as highly-efficient air cathode for Zn-Air battery</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">• Biomass-derived high-efficiency ORR catalyst that can be mass-produced. • The regulation of pore structure on charge transport was in-depth studied by TPV. • C111-900 outperforms 20% Pt/C in the operation of Zn-Air battery.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Jie</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Zhenzhen</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Huang, Hui</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Yang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kang, Zhenhui</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Sharma, Mayukh ELSEVIER</subfield><subfield code="t">Deep Learning for predicting neutralities in Offensive Language Identification Dataset▪</subfield><subfield code="d">2021</subfield><subfield code="d">(including crystal engineering) : an international journal reporting research on the synthesis, structure, and properties of materials</subfield><subfield code="g">New York, NY [u.a.]</subfield><subfield code="w">(DE-627)ELV006657850</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:153</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.materresbull.2022.111908</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.72</subfield><subfield code="j">Künstliche Intelligenz</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">153</subfield><subfield code="j">2022</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| score |
7.3985834 |