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electrolysis_01
Nano Energy 81 (2021) 105620 Available online 18 November 2020 2211-2855/© 2020 The Author(s).
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electrolysis_01
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electrolysis
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Published by Elsevier Ltd.
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This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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Full paper
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electrolysis_01
train3
electrolysis
electrolysis_01
Electrochromic solar water splitting using a cathodic WO3 electrocatalyst
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A R T I C L E I N F O
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Keywords: Electrochromism Hydrogen evolution reaction Water splitting WO3 Site saturation
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electrolysis_01
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A B S T R A C T
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Solar-driven water splitting is an emerging technology with high potential to generate fuel cleanly and sus- tainably.
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electrolysis_01
train3
electrolysis
electrolysis_01
In this work, we show that WO3 can be used as a cathodic electrocatalyst in combination with (Ag,Cu) InGaSe2 solar cell modules to produce hydrogen and provide electrochromic functionality to water splitting devices.
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This electrochromic effect can be used to monitor the charge state or performance of the catalyst for process control or for controlling the temperature and absorbed heat due to tunable optical modulation of the electrocatalyst.
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WO3 films coated on Ni foam, using a wide range of different sputtering conditions, were investigated as cathodic electrocatalysts for the water splitting reaction.
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The solar-to-hydrogen (STH) efficiency of solar-driven water electrolysis was extracted using (Ag,Cu)InGaSe2 solar cell modules with a cell band gap varied in between 1.15 and 1.25 eV with WO3 on Ni foam-based electrolyzers and yielded up to 13% STH ef- ficiency.
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MEASUREMENT
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electrolysis_01
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Electrochromic properties during water electrolysis were characterized for the WO3 films on transparent substrate (indium tin oxide).
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electrolysis_01
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electrolysis
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Transmittance varied between 10% and 78% and the coloration efficiency at a wavelength of 528 nm and the overpotential of 400 mV was 40 cm2 C− 1.
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Hydrogen ion consumption in ion intercalation for electrochromic and hydrogen gas production for water electrolysis processes was discussed.
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1. Introduction
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electrolysis_01
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Concerns about climate change, urban air pollution, and energy se- curity have led to an increased interest of scientists, industry, and po- litical leaders to develop methods for production, usage, and storage of energy in a clean and sustainable way.
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electrolysis_01
train3
electrolysis
electrolysis_01
Molecular hydrogen is here a promising and carbon-free energy carrier for energy storage for later utilization as a source for heat, electricity, or as a chemical feedstock for industry.
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electrolysis_01
train3
electrolysis
electrolysis_01
Electrolysis of water is a technique that yields hydrogen with high purity and would form a sustainable solution if electricity from wind-, hydro-, or solar-harvesting were used.
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A direct production in a photocatalytic or photovoltaic (PV) system coupled to an electrolyzer can be readily used today where molecular hydrogen stores the solar energy in a carbon-free fuel for later use in vehicles or stationary ap- plications.
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The stored energy can be used as heat by combustion or as electrical power via a hydrogen fuel cell on demand or when the solar power is intermittent.
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The history of electrolysis began at the end of the 18th century (1789) when Deiman and van Troostwijk dissociated water using an electrostatic generator and two gold electrodes immersed in water [1].
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Subsequently, Nicholson and Carlisle were the first who described the process of using electricity to dissociate water into hydrogen and oxygen in their experiments on the electrolytic splitting of water using voltaic piles in the year 1800 [2].
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Following the electrifi- cation of society, over 400 industrial water electrolyzers were in use in 1902 [3].
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Light-driven electrolysis was first described by Fujishima and Honda in 1972 using TiO2 as a photoanode and platinum as a cathode [4] and has been an active field of research since then.
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In electrocatalytic water splitting, hydrogen and oxygen gases evolve from reduction and oxidation of water at the cathodic and anodic electrodes, respectively.
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For photocatalytic water splitting using pho- toelectrochemical cells (PECs), the charge carriers are created from the photovoltaic effect close to the catalytic site.
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Solar cell driven electro- catalysis instead has advantages over direct photocatalysts in terms of higher efficiency, high power per area, and the possibility to separate the light absorption process from the electrolysis process [5] allowing optimization of each function independently, and not the least, enable the removal of the photo absorbing material from the electrolyte [6].
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electrolysis
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Proton exchange membrane (PEM) electrolysis, which occurs in acidic electrolytes (pH 0–7), has better efficiency and enhanced ramping capability over other types of electrolysis [7].
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The principle of PEM
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electrolysis_01
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electrolysis
electrolysis_01
electrolysis together with half-cell and overall-cell reactions can be written as 2H+ + 2e− →H2 (cathodic reaction) (1) H2O→2H+ + 1/2O2 + 2e− (anodic reaction) (2) H2O→H2 + 1/2O2 (overall reaction) (3) and are shown in Fig. 1a.
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electrolysis
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The typical operating cell voltage for commercial electrolyzers (1.8–2.0 V) is much larger than the theoretical minimum value (1.23 V), and they are typically operated at higher current densities to produce hydrogen at a high rate in order to offset the capital cost of the materials used.
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electrolysis
electrolysis_01
For water electrolysis, the fabrication of cost-effective non-noble metals has attracted significant interest as a replacement for expensive Pt-based catalysts.
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electrolysis_01
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However, they would also need to operate at a rela- tively high hydrogen production rate to lower the total cost of hydrogen produced over the lifetime of the system.
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Transition metal oxides such as WO3 can be promising alternatives and have previously mainly been used as photoanodic catalystst for oxygen evolution with modest solar- to-hydrogen (STH) efficiency but has recently also been highlighted as a cathodic electrocatalys that show promising stability with HER stability maintained after 2000 cycles [8].
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PV–technologies such as crystalline– and thin film– silicon, perov- skites, and (Ag, Cu)InGaSe2 (A-CIGS) can be used to supply the energy to drive an electrolyzer [6,9–12] with theoretical STH efficiencies span- ning from about 25% from serial interconnected solar cells to 32% for tandem approaches when using un-concentrated solar illumination [13].
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High optical absorption coefficient and adjustable band gap of A-CIGS solar cells make them attractive because of the possibility to achieve high efficiency (η) in the cells.
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The band gap of the A-CIGS cells can be changed by changing the Ga/(Ga+In) ratio [14–16].
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This prop- erty is helpful in finding an optimum condition for a highly efficient solar water splitting system [17].
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Electrochromism on the other hand, is an energy-saving technology that allows control of the amount of solar heat and visible light trans- mitted by a thin film.
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Electrochromic thin films are integral parts of multi-layer devices (Fig. 1b) that are capable of varying their optical properties by application of an externally applied voltage [18,19].
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Electrochromic devices can be used as smart windows in buildings, and this technology can significantly reduce energy use for cooling, air conditioning, and illumination [20].
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The history of electrochromism starts in the 1950s with the devel- opment of qualitative theory and quantitative prediction of the shift of the absorption and emission spectra of certain organic dyes by solvent and polarization effects [21,22].
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Based on these investigations, a possible change of color in dyes by applying an electric field was sug- gested by Platt in 1961 [23].
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It was the first time that the term elec- trochromism was used.
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QUAL_MEASUREMENT
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In 1969, electrochromism was experimentally demonstrated in an inorganic compound, WO3, by Deb [24] and in 1985, Svensson and Granqvist introduced the potential application of electrochromic materials in smart windows [25].
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A typical smart win- dow, as shown in Fig. 1b, contains five layers between two transparent substrates where a transparent ion conductive layer lies at the center of the structure.
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One side of the layer is in contact with a thin film of a cathodic electrochromic material.
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This layer is colored by the insertion of ions and charge-balancing electrons into the film.
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On the opposite side of the ion conductor, an ion storage layer is situated.
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Usually, anodic electrochromic thin films are used in this layer in order to get the highest possible modulation of the transmittance.
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The anodic layer is colored by the extraction of ions and electrons from the film.
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In current electro- chromic technology, the most common devices include thin films based on cathodically coloring WO3 and anodically coloring NiO.
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The color- ation of WO3 is attributed to intervalence electron transfer between W6+ and W5+ valence states [26].
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The most accepted model for the coloration is that polarons formed by the localization of the injected electrons at W5+ sites are hopping between adjacent Wi and Wj sites when the light is absorbed [27].
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hν + W5+ i + W6+ j → W6+ i + W5+ j (4)
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The electrochromic phenomenon in WO3 occurs with the intercala- tion of M+ cations such as H+, Li+, and Na+ from the electrolyte and the reaction mechanism is given as: WO3 + xM+ + xe− ↔ MxWO3 (5)
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The expression for the time-dependent intercalation level is [28]. x(t) = (zedANW) − 1 ∫ t 0 dt′ I(t′) (6) where z is the valence of the intercalated ion, e is the elementary charge, d film thickness, A is the active area of the electrode, NW is the tungsten number density, and I is the current during the measurement.
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Electrochromic properties are assessed by optical modulation which is the difference between the bleached (Tb) and colored transmission (TC) states; optical density is defined as OD(λ, x) = dα(λ, x)where α is the absorption coefficient at the specified optical wavelength λ and inter- calation level x; and differential coloration efficiency K(λ, x) given by [28].
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K(λ, x) = dOD(λ, x)/dq (7) where q is the inserted charge per unit area.
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Fig. 1. Schematic picture of PEM electrolysis (a) and a window type electrochromic device (b), where M+ denotes the inserting ion.
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İ. Bayrak Pehlivan et al Nano Energy 81 (2021) 105620 3
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Considering the relation of OD with total transmittance (Tt) and reflectance (Rt), the differential coloration efficiency K(λ, x) can be written as K(λ, x) = − 1 Tt(λ, x) dTt(λ, x) dq − 1 [1 − Rt(λ, x) ] dRt(λ, x) dq (8)
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Transition metal oxides are of interest for many applications other than electrochromic devices, for example, as catalysts, batteries, supercapacitors, and gas sensors.
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Therefore, there is a growing interest in multi-functional electrochromic devices, which can provide energy storage and energy production functionality together with control over light and/or heat [29,30].
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Photoelectrochromic devices combine elec- trochromism with photovoltaic electricity generation in an integrated device [31,32].
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The energy need of the electrochromic devices can be supplied from the photovoltaic part and directly used for the optical modulation without any demand for external power.
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Very recently, it was found that electrochromic devices can also exhibit tunable micro- wave dielectric properties [33].
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Electrochromic batteries and electro- chromic supercapacitors integrate energy storage functions with optical modulation [34,35].
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In particular, the ability to monitor the level of energy stored in the devices by the color variation makes electrochromic energy storage devices attractive.
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Electrolysis in electrochromic devices was investigated to examine the role of water on device performance [36,37].
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The optical modulation has been shown to increase and the coloration and bleaching times decreased with the presence of water in the ion-conductive layer [38].
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electrolysis_01
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In this study, we combine investigations of electrocatalytic and electrochromic properties of WO3 thin films.
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electrolysis_01
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electrolysis
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The study consists of three main parts.
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11,763
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electrolysis_01
train3
electrolysis
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First, cathodic electrocatalytic properties of WO3 thin films deposited using a wide range of sputter conditions on Ni foam were investigated in the potential range of the hydrogen evolution reaction (HER) in an acidic electrolyte and compared with a well-known NiMo electrocatalyst for the overall water splitting reaction.
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electrolysis_01
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Second, optimum STH efficiency was investigated for electrolyzers combining films pro- duced using different sputtering conditions of the WO3 (cathode) with Ni foam (anode) and A-CIGS solar cell modules with cell band gaps varying in-between 1.15 and 1.25 eV.
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electrolysis_01
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In addition, electrochromic and electrocatalytic properties of WO3 films coated on indium tin oxide (ITO) substrates were investigated in a potential range relevant for the HER, where the coloration efficiency was quantified.
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Finally, we discuss H+ ion intercalation and the relation to the hydrogen evaluation process and possible applications of the reported effect.
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2. Experimental
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2.1. Sample preparation
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WO3 films were deposited on 1.6 mm thick Ni foam (350 g/m2 sur- face density and sheet resistance of 0.1 Ω/□) and ITO coated glass (sheet resistance of 60 Ω/□) substrates by reactive DC magnetron sputtering using a Balzers UTT 400 unit.
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electrolysis_01
train3
electrolysis
electrolysis_01
The target consisted of a 5 cm diameter metallic W (99.99% purity, Plasmaterials) disc.
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The target to substrate distance was 13 cm, and the system was evacuated to ~8 × 10–5 Pa.
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electrolysis_01
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Pre-sputtering was performed for 5 min in argon plasma in order to remove surface impurities.
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electrolysis_01
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The substrate holder was rotated at 3 rpm to improve the homogeneity of the films.
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electrolysis_01
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Coating parameters are given in Table S1.
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electrolysis_01
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electrolysis
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The film thickness determined by a Veeco Dektak 150 surface profilometry instrument was 165 ± 10 and 300 ± 25 nm for films coated on Ni foam and ITO substrates, respectively.
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electrolysis_01
train3
electrolysis
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Ni foam surface coverage of the WO3 thin film and its homogeneity were analyzed by energy–dispersive X-ray spectroscopy (EDS).
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13,594
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['Exp_Characterization', 'Experiment']
MEASUREMENT
{ "text": [ "Ni", "foam", "surface coverage", "WO3", "thin film", "homogeneity", "were analyzed", "energy–dispersive X-ray spectroscopy (EDS)" ], "id": [ 112078, 112087, 112096, 112105, 112114, 112123, 112132, 112141 ], "value": [ "MAT", "FORM", "PROPERTY", "MAT", "FORM", "PROPERTY", "MEASUREMENT", "TECHNIQUE" ], "begin": [ "0", "3", "8", "32", "36", "54", "66", "83" ], "end": [ "2", "7", "24", "35", "45", "65", "79", "125" ], "tokenIndices": [ [ 0, 0 ], [ 1, 1 ], [ 2, 3 ], [ 6, 6 ], [ 7, 8 ], [ 11, 11 ], [ 12, 13 ], [ 15, 22 ] ] }
[ "U-MAT", "U-FORM", "B-PROPERTY", "L-PROPERTY", "O", "O", "U-MAT", "B-FORM", "L-FORM", "O", "O", "U-PROPERTY", "B-MEASUREMENT", "L-MEASUREMENT", "O", "B-TECHNIQUE", "I-TECHNIQUE", "I-TECHNIQUE", "I-TECHNIQUE", "I-TECHNIQUE", "I-TECHNIQUE", "I-TECHNIQUE", "L-TECHNIQUE", "O" ]
{ "ne_id_gov": [ 112132, 112078, 112132, 112105, 112132 ], "ne_id_dep": [ 112078, 112087, 112105, 112114, 112141 ], "label": [ "conditionEnvironment", "hasForm", "conditionSampleFeatures", "hasForm", "usesTechnique" ] }
electrolysis_01
train3
electrolysis
electrolysis_01
The uniform elemental coverage of the WO3 on the Ni foam substrates was identified by EDS (Fig. 2).
[ "The", "uniform", "elemental", "coverage", "of", "the", "WO3", "on", "the", "Ni", "foam", "substrates", "was", "identified", "by", "EDS", "(", "Fig", ".", "2", ")", "." ]
13,721
13,820
['Results']
MEASUREMENT
{ "text": [ "uniform elemental coverage", "WO3", "Ni", "foam", "was identified", "EDS" ], "id": [ 112150, 112159, 112168, 112177, 112186, 112195 ], "value": [ "PROPERTY", "MAT", "MAT", "FORM", "MEASUREMENT", "TECHNIQUE" ], "begin": [ "4", "38", "49", "52", "68", "86" ], "end": [ "30", "41", "51", "56", "82", "89" ], "tokenIndices": [ [ 1, 3 ], [ 6, 6 ], [ 9, 9 ], [ 10, 10 ], [ 12, 13 ], [ 15, 15 ] ] }
[ "O", "B-PROPERTY", "I-PROPERTY", "L-PROPERTY", "O", "O", "U-MAT", "O", "O", "U-MAT", "U-FORM", "O", "B-MEASUREMENT", "L-MEASUREMENT", "O", "U-TECHNIQUE", "O", "O", "O", "O", "O", "O" ]
{ "ne_id_gov": [ 112186, 112186, 112186, 112168, 112186 ], "ne_id_dep": [ 112150, 112159, 112168, 112177, 112195 ], "label": [ "measuresProperty", "conditionSampleFeatures", "conditionEnvironment", "hasForm", "usesTechnique" ] }
electrolysis_01
train3
electrolysis
electrolysis_01
Due to the shadowing effect in EDS, one could not get the same quantification from the lower and the upper surfaces of the porous Ni foam substrate.
[ "Due", "to", "the", "shadowing", "effect", "in", "EDS", ",", "one", "could", "not", "get", "the", "same", "quantification", "from", "the", "lower", "and", "the", "upper", "surfaces", "of", "the", "porous", "Ni", "foam", "substrate", "." ]
13,821
13,969
['Exp_Characterization', 'Experiment']
O
{ "text": [ "Ni", "foam" ], "id": [ 112204, 112213 ], "value": [ "MAT", "FORM" ], "begin": [ "130", "133" ], "end": [ "132", "137" ], "tokenIndices": [ [ 25, 25 ], [ 26, 26 ] ] }
[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "U-MAT", "U-FORM", "O", "O" ]
{ "ne_id_gov": [ 112204 ], "ne_id_dep": [ 112213 ], "label": [ "hasForm" ] }
electrolysis_01
train3
electrolysis
electrolysis_01
However, since sputtering is a highly directional process, only the surface of Ni foam that was facing the sputter target should be expected to be coated.
[ "However", ",", "since", "sputtering", "is", "a", "highly", "directional", "process", ",", "only", "the", "surface", "of", "Ni", "foam", "that", "was", "facing", "the", "sputter", "target", "should", "be", "expected", "to", "be", "coated", "." ]
13,970
14,124
['Background', 'Experiment', 'Exp_Preparation']
O
{ "text": [ "Ni", "foam" ], "id": [ 112222, 112231 ], "value": [ "MAT", "FORM" ], "begin": [ "79", "82" ], "end": [ "81", "86" ], "tokenIndices": [ [ 14, 14 ], [ 15, 15 ] ] }
[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "U-MAT", "U-FORM", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
{ "ne_id_gov": [ 112222 ], "ne_id_dep": [ 112231 ], "label": [ "hasForm" ] }
electrolysis_01
train3
electrolysis
electrolysis_01
2.1.2.
[ "2.1.2", "." ]
14,125
14,131
['Heading']
O
{ "text": [], "id": [], "value": [], "begin": [], "end": [], "tokenIndices": [] }
[ "O", "O" ]
{ "ne_id_gov": [], "ne_id_dep": [], "label": [] }
electrolysis_01
train3
electrolysis
electrolysis_01
The A-CIGS material and subsequent solar cell modules were fabri- cated on soda-lime glass substrates cut to a 5 × 5 cm2 sample size.
[ "The", "A", "-", "CIGS", "material", "and", "subsequent", "solar", "cell", "modules", "were", "fabri", "-", "cated", "on", "soda", "-", "lime", "glass", "substrates", "cut", "to", "a", "5", "×", "5", "cm2", "sample", "size", "." ]
14,169
14,302
['Experiment', 'Exp_Preparation']
O
{ "text": [ "A-CIGS", "solar cell", "soda-lime glass", "5 × 5 cm2", "5 × 5", "cm2" ], "id": [ 112258, 112267, 112276, 112285, 112294, 112303 ], "value": [ "MAT", "DEV", "MAT", "VALUE", "NUM", "UNIT" ], "begin": [ "4", "35", "75", "111", "111", "117" ], "end": [ "10", "45", "90", "120", "116", "120" ], "tokenIndices": [ [ 1, 3 ], [ 7, 8 ], [ 15, 18 ], [ 23, 26 ], [ 23, 25 ], [ 26, 26 ] ] }
[ "O", "B-MAT", "I-MAT", "L-MAT", "O", "O", "O", "B-DEV", "L-DEV", "O", "O", "O", "O", "O", "O", "B-MAT", "I-MAT", "I-MAT", "L-MAT", "O", "O", "O", "O", "B-VALUE+B-NUM", "I-VALUE+I-NUM", "I-VALUE+L-NUM", "L-VALUE+U-UNIT", "O", "O", "O" ]
{ "ne_id_gov": [], "ne_id_dep": [], "label": [] }
electrolysis_01
train3
electrolysis
electrolysis_01
The
[ "The" ]
14,303
14,306
['Experiment', 'Exp_Preparation']
O
{ "text": [], "id": [], "value": [], "begin": [], "end": [], "tokenIndices": [] }
[ "O" ]
{ "ne_id_gov": [], "ne_id_dep": [], "label": [] }
electrolysis_01
train3
electrolysis
electrolysis_01
Fig. 2.
[ "Fig", ".", "2", "." ]
14,307
14,314
['Caption']
O
{ "text": [], "id": [], "value": [], "begin": [], "end": [], "tokenIndices": [] }
[ "O", "O", "O", "O" ]
{ "ne_id_gov": [], "ne_id_dep": [], "label": [] }
electrolysis_01
train3
electrolysis
electrolysis_01
Electron image and elemental mapping of WO3 thin film on Ni foam.
[ "Electron", "image", "and", "elemental", "mapping", "of", "WO3", "thin", "film", "on", "Ni", "foam", "." ]
14,315
14,380
['Results', 'Caption']
MEASUREMENT
{ "text": [ "Electron image", "image", "elemental mapping", "WO3", "thin film", "Ni", "foam" ], "id": [ 112312, 112321, 112330, 112339, 112348, 112357, 112366 ], "value": [ "PROPERTY", "MEASUREMENT", "PROPERTY", "MAT", "FORM", "MAT", "FORM" ], "begin": [ "0", "9", "19", "40", "44", "57", "60" ], "end": [ "14", "14", "36", "43", "53", "59", "64" ], "tokenIndices": [ [ 0, 1 ], [ 1, 1 ], [ 3, 4 ], [ 6, 6 ], [ 7, 8 ], [ 10, 10 ], [ 11, 11 ] ] }
[ "B-PROPERTY", "L-PROPERTY+U-MEASUREMENT", "O", "B-PROPERTY", "L-PROPERTY", "O", "U-MAT", "B-FORM", "L-FORM", "O", "U-MAT", "U-FORM", "O" ]
{ "ne_id_gov": [ 112321, 112321, 112321, 112339, 112339, 112357 ], "ne_id_dep": [ 112312, 112330, 112339, 112348, 112357, 112366 ], "label": [ "measuresProperty", "measuresProperty", "conditionSampleFeatures", "hasForm", "usedTogether", "hasForm" ] }
electrolysis_01
train3
electrolysis
electrolysis_01
İ. Bayrak Pehlivan et al Nano Energy 81 (2021) 105620 4
[ "İ", ".", "Bayrak", "Pehlivan", "et", "al", "Nano", "Energy", "81", "(", "2021", ")", "105620", "4" ]
14,381
14,490
['Metadata']
O
{ "text": [], "id": [], "value": [], "begin": [], "end": [], "tokenIndices": [] }
[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
{ "ne_id_gov": [], "ne_id_dep": [], "label": [] }
electrolysis_01
train3
electrolysis
electrolysis_01
sample structure consists of a stack of thin layers of Mo, NaF, A-CIGS, CdS, ZnO, and ZnO:Al.
[ "sample", "structure", "consists", "of", "a", "stack", "of", "thin", "layers", "of", "Mo", ",", "NaF", ",", "A", "-", "CIGS", ",", "CdS", ",", "ZnO", ",", "and", "ZnO", ":", "Al", "." ]
14,491
14,584
['Experiment', 'Exp_Preparation']
O
{ "text": [ "thin layers", "Mo", "NaF", "A-CIGS", "CdS", "ZnO", "ZnO:Al" ], "id": [ 112375, 112384, 112393, 112402, 112411, 112420, 112429 ], "value": [ "FORM", "MAT", "MAT", "MAT", "MAT", "MAT", "MAT" ], "begin": [ "40", "55", "59", "64", "72", "77", "86" ], "end": [ "51", "57", "62", "70", "75", "80", "92" ], "tokenIndices": [ [ 7, 8 ], [ 10, 10 ], [ 12, 12 ], [ 14, 16 ], [ 18, 18 ], [ 20, 20 ], [ 23, 25 ] ] }
[ "O", "O", "O", "O", "O", "O", "O", "B-FORM", "L-FORM", "O", "U-MAT", "O", "U-MAT", "O", "B-MAT", "I-MAT", "L-MAT", "O", "U-MAT", "O", "U-MAT", "O", "O", "B-MAT", "I-MAT", "L-MAT", "O" ]
{ "ne_id_gov": [ 112429, 112411, 112402, 112393, 112384, 112375 ], "ne_id_dep": [ 112375, 112375, 112375, 112375, 112375, 112420 ], "label": [ "hasForm", "hasForm", "hasForm", "hasForm", "hasForm", "hasForm" ] }
electrolysis_01
train3
electrolysis
electrolysis_01
The Mo layer was fabricated by DC magnetron sputtering to a thickness of 300–400 nm.
[ "The", "Mo", "layer", "was", "fabricated", "by", "DC", "magnetron", "sputtering", "to", "a", "thickness", "of", "300", "–", "400", "nm", "." ]
14,585
14,669
['Experiment', 'Exp_Preparation']
O
{ "text": [ "Mo", "layer", "300–400 nm", "300–400", "300", "400", "nm" ], "id": [ 112438, 112447, 112456, 112465, 112474, 112483, 112492 ], "value": [ "MAT", "FORM", "VALUE", "RANGE", "NUM", "NUM", "UNIT" ], "begin": [ "4", "7", "73", "73", "73", "77", "81" ], "end": [ "6", "12", "83", "80", "76", "80", "83" ], "tokenIndices": [ [ 1, 1 ], [ 2, 2 ], [ 13, 16 ], [ 13, 15 ], [ 13, 13 ], [ 15, 15 ], [ 16, 16 ] ] }
[ "O", "U-MAT", "U-FORM", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-VALUE+B-RANGE+U-NUM", "I-VALUE+I-RANGE", "I-VALUE+L-RANGE+U-NUM", "L-VALUE+U-UNIT", "O" ]
{ "ne_id_gov": [ 112438 ], "ne_id_dep": [ 112447 ], "label": [ "hasForm" ] }
electrolysis_01
train3
electrolysis
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Dataset Card for MuLMS

Example annotation in the Multi-Layer Materials Science Corpus (image source: MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain)

Dataset Description

The Multi-Layer Materials Science corpus (MuLMS) consists of 50 documents (licensed CC BY) from the materials science domain, spanning across the following 7 subareas: "Electrolysis", "Graphene", "Polymer Electrolyte Fuel Cell (PEMFC)", "Solid Oxide Fuel Cell (SOFC)", "Polymers", "Semiconductors" and "Steel". It was exhaustively annotated by domain experts. There are annotations on sentence-level and token-level for the following NLP tasks:

  • Measurement Frames: Measurement annotations are treated in a frame-like fashion, using the span type MEASUREMENT to mark the triggers (e.g., was measured, is plotted) that introduce the Measurement frame to the discourse. Deciding whether a sentence contains a measurement trigger is treated as a sentence-level task, determining the span that triggers the measurement frame is treated as named entity recognition.
  • Named Entities: There are 12 token-level named entities (+ Measurement trigger) available in MuLMS. Named entities can span across multiple tokens.
  • Relations: MuLMS provides relations between pairs of entities. There are two types of relations: measurement-related relations and further relations. The first type always starts at Measurement trigger spans, the scond type does not start at a specific Measurement annotation.
  • Argumentative Zones: Each sentence in MuLMS is assigned a rhetorical function in the discourse (e.g., Background or Experiment_Preparation). There are 12 argumentative zones in MuLMS, which leads to a sentence-level classification task.

You can find all experiment code files and further information in the MuLMS-AZ Repo and MuLMS Repo. For dataset statistics, please refer to both papers listed below. There you can also find detailed explanation of all parts of MuLMS in very detail.

Dataset Details

MuLMS provides all annotated files in UIMA CAS XMI format that can be used with annotation tools that can read these files such as INCEpTION.

Dataset Sources

Uses

Direct Use

This dataset aims at information extraction from materials science documents. It enables the training of (neural) classifiers that can be used for downstream tasks such as NER and relation extraction. Please refer to both repos linked above for training BERT-like models on all NLP tasks provided in MuLMS.

Dataset Structure

MuLMS offers two configs: MuLMS_Corpus, which loads the entire MuLMS dataset, and NER_Dependecies, which loads only Named Entities in CONLL format in order to train models in the NER_as_dependency_parsing setting.

MuLMS is divided into three split: train, validation, and test. Furthermore, train is divided into five sub-splits, namely tune1,...,tune5. This allows for model training on four splits, early stopping on the fivth and remaining split, model picking on validation and evaluation only once on test. HuggingFace datasets do not support these sub-splits, hence they must be loaded as train and post-processed and filtered afterward in a custom dataset loader.

Dataset Config MuLMS_Corpus

  • doc_id: ID of the source document that can be used to lookup the metadata of the paper in MuLMS_Corpus_Metadata.csv.
  • sentence: Each instance in the dataset corresponds to one sentence extracted from scientic papers. These sentences are listed in this field.
  • tokens: Pre-tokenized sentences. Each instance is a list of tokens.
  • begin_offset: Offset of the beginning of each sentence within the full text of the document.
  • end_offset: Offset of the end of each sentence within the full text of the document.
  • AZ_labels: The argumentative zone (= rhetorical function) of each sentence in the discourse of a materials science publication.
  • Measurement_label: Labels each sentence whether it contains a measurement description, i.e., measurement frame evoking trigger word, or not.
  • NER_labels: Contains lists with named entities (NEs) per instance. Every named entity uses one of n indices in these lists, i.e., every 0-th element belong to each other, ...
    • text: List of tokens that are contained in the current sentence instance.
    • id: Unique ID for each named entity
    • value: The named entity class
    • begin: Character offsets of the begin tokens of each NE
    • end: Character offsets of the end tokens of each NE
    • tokenIndices: Token index in the list of tokens
  • NER_labels_BILOU: BILOU tag sequence per token in the sentence (B = begin, I = inside, L = last, O = none, U = unit).
  • relations: Lists of relations between pair-wise entities. As with the named entities, each relation corresponds to the same index in all three lists (ne_id_gov, ne_id_dep, label)
    • ne_id_gov: List of NE entity IDs that act as head of the relation
    • ne_id_dep: List of NE entity IDs that are the tail of the relation
    • label: Relation label between both entities
  • docFileName: Name of the source document in the corpus
  • data_split: Indicates the split which a document belongs to (tune1/2/3/4/5, dev, test)
  • category: One of 7 materials science sub-domains in MuLMS (SOFC, graphene, electrolysis, PEMFC, )

Dataset Config NER_Dependencies

Each instance in this config refers to one token and carries a copy of the entire sentence, i.e., for n tokens in a sentence, the text of the sentence is given n times.

  • index: Unique instance ID for each token.
  • ID: Sentence ID. As opposed to the other config, the sentences here are not sorted by document and provided in their full form for every token they belong to.
  • Sentence: Sentence string
  • Token_ID: Unique ID for each token within each sentence. ID is resetted for each new sentence.
  • Token: Token string
  • NE_Dependencies: The named entity tag of form k:LABEL where k refers to the ID of the begin token and LABEL to the named entity. The entity ends at the token holding this
  • label.
  • data_split: Indicates the split which a document belongs to (tune1/2/3/4/5, dev, test)

Labels

For the different layers, the following labels are available:

  • Measurement Frames:
    • Measurement
    • Qual_Measurement
  • Named Entities:
    • MAT
    • NUM
    • VALUE
    • UNIT
    • PROPERTY
    • FORM
    • MEASUREMENT (measurement frame-evoking trigger)
    • CITE
    • SAMPLE
    • TECHNIQUE
    • DEV
    • RANGE
    • INSTRUMENT
  • Relations:
    • hasForm
    • measuresProperty
    • usedAs
    • propertyValue
    • conditionProperty
    • conditionSample
    • conditionPropertyValue
    • usesTechnique
    • measuresPropertyValue
    • usedTogether
    • conditionEnv
    • usedIn
    • conditionInstrument
    • takenFrom
    • dopedBy
  • Argumentative Zones:
    • Motivation
    • Background
      • PriorWork
    • Experiment
      • Preparation
      • Characterization
    • Explanation
    • Results
    • Conclusion
    • Heading
    • Caption
    • Metadata

Dataset Creation

Curation Rationale

Keeping track of all relevant recent publications and experimental results for a research area is a challenging task. MuLMS addresses this problem by providing a large set of annotated documents that allow for training models that can be used for automated information extraction and answering search queries in materials science documents.

Source Data

You can find all the details for every document in this corpus in MuLMS_Corpus_Metadata.csv.

Who are the source data producers?

You can find all the authors for every document in this corpus in MuLMS_Corpus_Metadata.csv.

Annotation process

The annotation process included guideline design in dedicated discussion sessions. Afterward, the text files were annotated using INCEpTION.

Who are the annotators?

The annotators worked collaboratively to annotate the dataset in the best possible way. All people in this project either have background in materials science or computer science. This synergy enables to incorporate both views, the materials scientist view that has a deep knowledge about the topics themselves as well as the CS view that always looks at processing text data automatically in a structured fashion.

Personal and Sensitive Information

This dataset does not contain any personal, sensitive or private data. MuLMS builds upon publicly available scientific publications and all authors are credited accordingly.

Citation

If you use our software or dataset in your scientific work, please cite both papers:

BibTeX:

@misc{schrader2023mulms,
      title={MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain},
      author={Timo Pierre Schrader and Matteo Finco and Stefan Grünewald and Felix Hildebrand and Annemarie Friedrich},
      year={2023},
      eprint={2310.15569},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{schrader-etal-2023-mulms,
    title = "{M}u{LMS}-{AZ}: An Argumentative Zoning Dataset for the Materials Science Domain",
    author = {Schrader, Timo  and
      B{\"u}rkle, Teresa  and
      Henning, Sophie  and
      Tan, Sherry  and
      Finco, Matteo  and
      Gr{\"u}newald, Stefan  and
      Indrikova, Maira  and
      Hildebrand, Felix  and
      Friedrich, Annemarie},
    booktitle = "Proceedings of the 4th Workshop on Computational Approaches to Discourse (CODI 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.codi-1.1",
    doi = "10.18653/v1/2023.codi-1.1",
    pages = "1--15",
}

Changes

Changes to the source code from the original repo are listed in the CHANGELOG file.

Copyright 

Experiment resources related to the MuLMS corpus.
Copyright (c) 2023 Robert Bosch GmbH

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.

License

This software is open-sourced under the AGPL-3.0 license. See the LICENSE_CODE file for details. The MuLMS corpus is released under the CC BY-SA 4.0 license. See the LICENSE_CORPUS file for details.

Dataset Card Authors

  • Timo Pierre Schrader (Bosch Center for AI, University of Augsburg)
  • Matteo Finco (Bosch Research)
  • Stefan Grünewald (Bosch Center for AI, University of Stuttgart)
  • Felix Hildebrand (Bosch Research)
  • Annemarie Friedrich (University of Augsburg)

Dataset Card Contact

For all questions, please contact Timo Schrader.

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