dataesr/scientific-paragraphs-categorization huggingface dataset:
DFT calculations Spin-polarized electronic structure calculations were achieved using density functional theory (DFT) with the projector augmented-wave (PAW) method as implemented in the VASP code  [22] [23] [24]. PAW datasets were used to describe valence electron-ionic core interactions. For the relaxation of cell parameters and atomic positions of CeTiGeH, the exchange and correlation energies were treated at the generalized gradient approximation (GGA) level, according to the Perdew-Burke-Ernzerhof formulation  [25] ; an on-site Hubbard correction U of 5 eV  [26] [27] [28] [29]  was added to Ce 4f orbitals, according to Dudarev's scheme  [30]. For the density of states and electron density distribution, we used the more accurate Heyd-Scuseria-Ernzerhof (HSE06) screened hybrid exchange functional, where 25% of GGA exchange is replaced by exact Hartree-Fock (HF) exchange  [31, 32].
This striking difference between the two compounds is consistent with our studies (to be published) on intermetallic systems with the same crystal structure and either Sc or Ti as transition element: RScX compounds show clear electride-type features, but RTiX do not. This is in agreement with DFT high-throughput screening of materials databases, which identified Sc-derived intermetallic compounds in this structural family as electride-type materials, but no candidates were found in Ti-derived compounds  [37]. As suggested by Hosono et al. [14] , the electride-type charge density in the R 2 Sc 4 sites may arise from the overlap of 4 s/4p Sc orbitals; if this is the case, then the radial extension of Ti 4 s and 4p orbitals could not match across the octahedral site to generate a local maximum of electron density.
Acknowledgments The authors gratefully acknowledge the  Région Nouvelle Aquitaine (Néo-Ammonia  projet,  AAPR2020-2019-8018010 ), the  French National Agency for Research  ( Intermetalyst  project,  ANR-19-07CE-0023 ) and the  European Regional Development Fund (ERDF)  for financial support. The authors thank  S. Arrii ,  J. Rousseau  and  L. Eloy  for PXRD, TEM and ICP analyses respectively. This work pertains to the  French government program  " Investissements d'Avenir" (EUR INTREE , reference ANR-18-.
The relationship between the data transformation as a result and the physical properties of a chosen sensing hardware is not straightforward and largely depends on the association of a software classifier with a sensing hardware. For an eNose, conductometric sensing arrays are often used as a hardware  [12] [13] [14] [15] , in conjunction with principal component analysis (PCA)/k-means clustering (k-means) as a classifier  [5, [16] [17] [18]. In such systems, time-dependent current signals are inputted by environmentally sensitive materials in an array. An information feature of a high-dimensional vector is composed of a single current signal for classification. PCA is a linear classifier as it involves only one matrix transformation from a dataset of input vectors to a set of scores. The PCA projects data on a subspace defined by a basis of the covariance matrix eigenvectors which have the highest eigenvalues.
Homogenizing the physical properties of the population of sensing elements can even degrade the classifier's recognition performance, so when the dynamic of sensing devices carries information, a long-enough acquisition is crucial for classification  [20]. However, time is also a resource to enrich a database to train a system: the slower the hardware, the longer the training, which ultimately conditions any classifier's recognition performance. As a consequence, in case information lies in the dynamic of a hardware  [21] , a fair compromise must be found for data acquisition time to optimize training with enough meaningful data but reasonable training duration for environmental pattern recognition. In the framework of recognizing volatile solvents with doped conducting polymers, we propose in this study to quantify how relevant it is to consider the acquisition time as a metrological FoM for effective molecular recognition with a conductometric nose using PCA/k-means.
The experimental details are fully described in a previous study  [22] , where the PCA was used exclusively with the feature α 1 calculated exclusively for the last ten seconds of each threeminute environment exposure. Although the steady-state response for these materials was relatively fast compared to that of other materials for comparable tests  [24] [25] [26] [27] , the dataset totals over 44 h of acquisition time assisted by manual operations to expose the different materials on different devices with different environments repeatedly to train the system. Applying the same approach to exposing n! times the n different vapor classes for three minutes would result in over a week, over a month and over seven months of total data acquisition, without interruption, to respectively recognize four, five and six different classes only.
As reducing the number of materials, devices or exposures would have consequences of degrading the recognition, it is straightforward to investigate decreasing the elementary period for one single environment exposure to minimize the total acquisition duration. To assess the classifier performance for a reduced acquisition time, the same dataset is considered, where data are pruned to ten seconds per acquisition sequences after the exposure starts in order to make sure that the recognition performances do not depend on the acquisition itself, keeping the same number of datapoints to analyze in each case. Also, PCA/k-means clustering was systematically used on all the different case scenarios with the same normalization and initialization settings to not bias the clustering by userdependent factors.
The recognition after clustering uses the same rules and thresholds in each case to assess a data sample as successfully, uncertainly or unsuccessfully recognized. All the individual PCA analyses are provided as Supplementary Information. total data acquisition, without interruption, to respectively recognize four, five and six different classes only. As reducing the number of materials, devices or exposures would have consequences of degrading the recognition, it is straightforward to investigate decreasing the elementary period for one single environment exposure to minimize the total acquisition duration. To assess the classifier performance for a reduced acquisition time, the same dataset is considered, where data are pruned to ten seconds per acquisition sequences after the exposure starts in order to make sure that the recognition performances do not depend on the acquisition itself, keeping the same number of datapoints to analyze in each case.
Steady Resistance Modulation as Thermodynamic Information Feature The first analysis was performed using α 1 as an information descriptor (see Figure  2 ), as in the former studies using the same dataset  [22, 28]. The choice of this specific feature was informed by the hypothesis that the presence of volatile molecules modulates the polymer electrical conductance by interacting with its dopant and shifts its thermodynamic charge-transfer equilibrium  [22]. As a thermodynamic property, the acquisition was set to three minutes per exposure to provide enough time for all devices to reach a quasi-steady state of conductance (steady output currents at a constant applied voltage). Here, from the whole dataset, 18 different sub-datasets were generated by segmenting the data to a concatenation of sequences of ten seconds after each exposure starts at t 0 (10 datapoints per exposures, 180 datapoints per sub-datasets-see Figure  2a ).
For each exposure, the feature α 1 takes into account two values: the resistance measured between the considered time interval and a reference resistance value R 0 measured while unexposed just before gas exposure. In each case, PCA was performed for the different sub-datasets, with the individual variance for each PC of each PCA for the different acquisition times shown in Figure  2b. From these values, one can observe that most of the variance (>66%) is explained within the first two PCs, except for data generated during the first ten seconds after t 0. It is also noticed that the variance for all 24 PCs does not change significantly for acquisitions occurring 30 s after exposure (Figure  2b , the color shade encodes the variance values in linear scale), suggesting that the feature α 1 is not enriched for the data separation in (PC1,PC2) if exposures last longer than 30 s.
Excluding the first ten seconds, deviations overtime are however noticed: for instance, the contribution of Ce(OTf) 3 -doped devices increases monotonically in PC1 α1 , such as the contribution of pristine P3HT in PC2 α1. This suggests that materials that better facilitate the (PC1,PC2) data separation but are expressed slower than others do not necessarily enhance the clustering. The previous study concluded on the exclusive basis of the PCA α1 170-179 analysis; two and three materials were preferred for recognizing ethanol from acetone from water using α 1 as and information descriptor  [22]. Figure  2f ,g show that such conclusion is highly acquisitiondependent (even for the same dataset considered) and that experiments performed with shorter time exposures might have concluded otherwise for the material selection.
As such dependency on the sample acquisition time greatly conditions the preferred material choice for classification, it appears legitimate to investigate a dynamical feature such as α 2 as a better information descriptor for gas recognition with the same raw current data. Eng 2023, 4, FOR PEER REVIEW 6 (even for the same dataset considered) and that experiments performed with shorter time exposures might have concluded otherwise for the material selection. As such dependency on the sample acquisition time greatly conditions the preferred material choice for classification, it appears legitimate to investigate a dynamical feature such as α2 as a be er information descriptor for gas recognition with the same raw current data.
Replicating the same methodology as previously for relative resistance modulation, PCA on α 2 as an information descriptor was performed for the 18 different sub-datasets. As for α 1 , the evolution of the variance for the individual PC depends on the acquisition time (Figure  3b ), mainly at the beginning of the transient response (typically the first 20 s). A singular difference with α 1 is the degradation of the data variance on the first two PCs with the increase in acquisition time (from >66% for PCA α1 170-179 down to <28% for PCA α2 170-179 ). This was expected as the information becomes more sensitive to the signal's noise on reaching the steady state; therefore, an increase in acquisition time decreases the contribution of the molecularly specific response compared in the overall information.
However, it should be noticed that the overlap in each case is only moderate: less than 20% of the total area of the ellipsoids. This suggests that recognition tests may be higher in the case where α 1 is used as an information descriptor than for α 2. To verify this, recognition tests were performed on the same sub-datasets using the 95% confidence ellipsoids as a threshold for classification (Figure  4g-i ). Detection is considered as "positive" if the actual class of the PCA score is exclusively contained within the corresponding confidence ellipsoid, "uncertain" if the actual class of the PCA score is not exclusively contained within the corresponding confidence ellipsoid, and "negative" if the actual class of the PCA score is excluded from the corresponding confidence ellipsoid. On the one hand for α 1 (Figure  4h ), one can observe that the positive detection rate 99.
Bioinformatics and Data Analysis Adapter sequences were removed by Illumina software following sequencing. The sequences of the PM10 samples were available from a previous study  (Tignat-Perrier et al. , 2020) , but were re-analyzed alongside the REPS sequences obtained here. The fastq files of both REPS and PM10 filter samples were imported into QIIME2 (v2021. 4) (Bolyen et al. , 2019)  and primers were removed with cutadapt  (Martin, 2011). The sequences were trimmed and denoised with the DADA2  (Callahan et al. , 2016)  plugin. Chimeras were detected by the consensus method and the sequences were truncated to 220 bp. The reads were merged to yield amplicon sequence variants (ASVs). The sequences of the transportation and field blanks for the PM10 filters were removed from all samples during the denoising step with DADA2.
After denoising all samples, ASVs present in REPS blanks were manually removed from all samples. The filtered ASVs from all the samples were grouped into 97% operational taxonomic units (OTUs) with the vsearch  (Rognes et al. , 2016)  plugin in QIIME2. A naïve Bayes scikit learn classifier  (Pedregosa et al. , 2011; Bokulich et al. , 2018) , trained on 341f and 805r primers using extracted reference full-length operon sequences of the SILVA v138 database  (Quast et al. , 2013) , was used for classification. Sequences classified as chloroplasts or mitochondria were filtered out with QIIME2. The final filtered sequences were imported into R with qiime2R  (Bisanz, 2018). The observed OTUs, Shannon's diversity index, Simpson's diversity index, and the weighted UniFrac distances were assessed with the phyloseq package (McMurdie and Holmes, 2013) (version 1.
38. 0). A principal coordinates analysis of weighted UniFrac was done with phyloseq as well. The OTUs were collapsed at the genus level with phyloseq and the genera shared between the co-located samplers each week was assessed with VennDiagram (Chen and Boutros, 2011) (version 1. 7. 1). A paired one-tailed t-test was performed (α = 0. 05) using stats (version 4. 1. 2) package (R Core Team, 2021) in R comparing the diversity metrics of samples collected with REPS and the filter from the same sampling period. The phyloseq objects were converted into DESeq2 objects and used for determining the differential abundance of OTUs based upon samplers. Using DESeq2, the counts for OTUs were modeled based on a negative binomial generalized distribution. The estimated fold change of the OTUs in REPS vs.
This would suggest that there were diverse bacteria associated with particles larger than 10 µm. Even though the liquid water content of clouds was not measured in Weeks 4 and 6, the relative humidity was 100% for 17. 8 and 29. 1% of the sampling period, respectively. During those periods, cloud droplets > 10 µm were likely present, as in the other Weeks. Furthermore, the cloud droplets would have reached REPS. Even if some biomass was removed from REPS as a result of wash-off, the impact was minimal because the diversity data obtained by REPS is very similar to the active sampler data. Also, REPS was previously found to determine increasing quantities of culturable bacteria as the sampling progressed across a 21-day sampling period, despite varying meteorological conditions  (Grogan and Mainelis, 2022).
ACKNOWLEDGEMENTS The research was funded by the  Agence Nationale de la Recherche  ( ANR-15-CE01-0002-INHALE ),  Région Auvergne-Rhône Alpes  and  CAMPUS France. KPD and SNCMG were supported by a  NIEHS  training grant in exposure science to  Rutgers University  ( 1T32ES019854 ; PI C. Weisel). We are thankful to  Laurent Deguillaume ,  Jean-Marc Pichon , and the  Observatoire de Physique du Globe de Clermont-Ferrand  staff for operating the sampling site, allowing access, and sharing meteorological data.
Data Availability The filter sample fastq files analyzed in this study were generated from a previous study, which were deposited in ftp://ftpadn. ec-lyon. fr/Tignat-Perrier_2020-Stoten_Puy-de-Dome_amplicon_ INHALE/. The REPS sequences generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under accession No. PRJNA929526.
[17]. The thermochemical data for most species is from the latest version of CRECK model  [23]  since it is the basis of the current model  [19]. For the species introduced by reactions added or updated in the present work, their thermochemical data originate from the same publication from which the reaction rate constant parameters were taken. Simulations presented in the discussion are performed with the homogeneous reactor model of the COSILAB software [27] using a constant p of 20 bar and a nominal reaction time of 4 ms. The constant pressure assumption is well justified  [ 20 , 28 ] , and well applies to our experimental configuration. Nevertheless, simulations with T 5 at the zero time point and measured pressure profiles up to 10 ms are sometimes necessary, as they take into account the reactions that occur in the quenching period, particularly those involving resonantly stabilized radicals such as cyclopentadienyl  [ 29 , 30 ].
Funding Sources This research was supported by  GIF (German-Israeli Foundation for research and development)  Grant  N I-1508-302. 5/2019  and by the  ISF (Israel Science Foundation ) Grant No. 370/20. DG thanks Esther K. and  M. Mark Watkins Chair  for Synthetic Organic Chemistry. SM thanks the  Neubauer Family Foundation  for generous financial support. MSR and DG thank  Deutscher Akademischer Austauschdienst (DAAD)  for generous financial support. Scheme 6. Computed reaction profile for the long-range ligand-assisted activation of H 2 with Ru(IV) species.
Acknowledgments This work was developed within the scope of the project  CICECO-Aveiro Institute of Materials ,  UIDB/50011/2020 ,  UIDP/50011/ 2020 & LA/P/0006/2020 , financed by national funds through the  FCT/MEC (PIDDAC). Inês S. Cardoso  acknowledges the  PhD FCT  grant  SFRH/BD/139801/2018.
Dynamic light scattering (DLS, Malvern Zetasizer Ultra, Malvern Instruments Ltd), TEM, and FESEM with EDX (FESEM-Hitachi S5500 Field Emission SEM) were used for measurements including NP size distributions, morphology, Zeta potentials, concentration (particles/ml), colloidal conductivity, and chemical composition. Image processing in MATLAB software (2022 version) was also used for NP size distribution analysis on FESEM images. UV-Vis (10 mm pathlength, Helma) and FTIR (Perkin Elmer Spectrum Two FTIR) spectroscopy techniques were used for optical properties measurements. Viscosity measurements were performed on a Brookfield DVNext Cone/Plate Viscometer. Glycerol (99. 5% purity, Sigma Aldrich) was used as a colloidal viscosity modifier. The Bruker® D8 Advance X-ray Diffraction platform was employed for phase identification.
For XPS characterisation, the colloid was dropcast onto a silicon substrate and allowed to dry in the air. Characterisation was carried out in a UHV Omicron system with a DAR 400 twin-anode x-ray source with Mg and Al filaments, and EA 125 energy analyzer. The sample was Ar + ion sputtered using a Prevac IS40C1 ion gun at 5 × 10 -6 mbar of argon, 0. 65 kV, and 5 mA emission current for 15 min prior to characterisation, to clean the surface layer which may inhibit signal from the deposited material, particularly at low kinetic energy. The binding energies in the data were calibrated to the C 1 s peak (283. 8 eV). An image processing algorithm was developed in MATLAB to attain quantitative data from the FESEM images. This method of NP size and size distribution analysis provides more data than visualization (qualitative data from FESEM images).
It gives quick results and considers all the colloidal particles using the Brownian motion mechanism whereby the speed of the NPs is proportional to their mass and by extension, their diameter. The challenge comes when the NPs are forming nanoclusters such as those in Fig. 2a  and d ; nanoclusters are interpreted as one large NP, which is far from the truth. Thus, DLS can give higher NP size values and is often limited when analysing multi-modal samples whereby larger particles tend to musk the small ones. A MATLAB image processing algorithm was utilized to analyze the size distribution and statistics of pyramidal BNPs in two different samples, S3 and S5. The algorithm was first applied to a 3. 6 mm 2 region of a FESEM image from S5, which contained a high aggregation of BNPs with both large and small NPs.
Future work In the future, the research will focus on examining the Mg-Cu BNPs' biochemical sensing abilities for trypsin and ammonia in the presence of more complex interferences such as hank solution or ionic fluids including potassium and calcium ions, to replicate realistic measurement conditions, such as urine samples. Real samples will be utilized after determining the BNPs' interactions with individual molecules. The aim of this research is to develop the capability to detect trypsin and ammonia in actual urine, sweat, breath, or saliva samples. Machine learning algorithms will be employed to analyse the vast amount of data generated by the different input combinations. Additionally, the use of Cu-Mg BNPs in inkjet printing of electronics is an area of interest, where sensors and conductive tracks could be printed using cost-effective materials instead of traditional materials like Au, Ag, Pt, and Pd.
Acknowledgements This publication has emanated from research [conducted with the financial support of/supported in part by a grant from]  Science Foundation Ireland  under Grant number [ 16/1571 RC/3872  and  19/US-C2C/3579 ]. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. This work is supported by  I-Form. The support provided for the research visit with sanction no. SIR/ 2022/000005  under SERB International Research Experience scheme by  Science and Engineering Research Board, Ministry of Science   and Technology, India  is acknowledged. Spectroscopy and microscopy measurements were carried out at the  Nano Research Facility (NRF) in Dublin City University  which was funded under the  Programme for Research in Third Level Institutions (PRTLI) Cycle 5.
The solubility of the neodymium oxide raw material in molten uorides and a thorough comprehension of the related phase equilibria and dissolution dynamics are both of theoretical and practical signi cance for the design of the proper electrolyte compositions and optimal process control and operation. In contrast to comprehensive works done in the eld of the cryolite -alumina electrolyte systems  [3, 12] , a small has been done in the eld of rare earth oxide -uoride electrolytes. Moreover, a discrepancy is there in the results from various studies in this eld. Due to this, the availability of reliable data is rather problematic. The present paper deals with phase equilibria of the molten Na 3 AlF 6 -NdF 3 -Nd 2 O 3 system. The phase diagrams of the systems Na 3 AlF 6 -NdF 3 and (Na 3 AlF 6 -NdF 3 ) eut -Nd 2 O 3 were experimentally determined by thermal analysis and the solidi ed samples after that analysis were then analysed by Xray diffraction.
99. 0 %, Slovalco, Slovakia), NdF 3 (99. 9 % Chempur, Germany), Nd 2 O 3 (99. 9 % Chempur, Germany). The furnace was rstly heated at the heating rate of 7 °C. min -1 to a temperature approximately 50 °C above the melting point of the melt. The samples were then kept at this temperature for approximately 50 min. The temperature of primary crystallization and the other heat effects were recorded at the cooling rate of 1. 5 °C. min -1. A computerized measuring device, developed at the Institute of Inorganic Chemistry SAS, Slovakia, was used for the temperature control of the furnace and data acquisition. The temperature of the sample was controlled by a Pt10Rh/Pt thermocouple calibrated using the melting points of the following pure chemicals; NaCl, NaF, and Li 2 CO 3. The accuracy of the temperature measurement of the measured thermocouples was found to be ±2 °C.
Acknowledgement This study was nancially supported by the  Slovak  grant agencies  APVV -19-0270 ,  APVV -19-0461 ,  VEGA 2/0024/20 , and  VEGA 2/0046/22. The work was performed within the  ITMS  project (with code  313021T081 ) supported by  Research & Innovation Operational Programme  funded by the  ERDF.