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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Search for Higgs bosons produced in association with a high-energy photon via vector-boson fusion and decaying to a pair of b -quarks in the ATLAS detector

A search for Standard Model Higgs bosons produced in association with a high-energy photon and decaying to $b\bar{b}$ is performed using 133 fb −1 of $\sqrt{s}$ = 13 TeV pp collision data collected with the ATLAS detector at the Large Hadron Collider at CERN. The photon requirement reduces the multijet background, and the H → $b\bar{b}$ decay is the dominant decay mode. Event selection requirements target events produced by vector-boson fusion, the dominant production mode in this channel. Several improvements enhance the search sensitivity compared to previous measurements. These improvements include better background modelling and characterization, the use of a neural-network classifier, and an updated signal extraction strategy adopting a direct binned-likelihood fit to the classifier output. With these improvements, the Higgs boson signal strength is measured to be 0.2 ± 0.7 relative to the Standard Model prediction. This corresponds to an observed significance of 0.3 standard deviations, compared to an expectation of 1.5 standard deviations assuming the Standard Model.

ATLAS experiment↗
DOE OSTI2021

Data trustworthiness signatures for nuclear reactor dynamics simulation

With the increased reliance on digitization in industrial control systems, the need for effective monitoring techniques has risen dramatically. Specifically, there is now a growing concern about the so-called false data injection (FDI) attacks. These attacks aim to alter the raw sensors’ data to cause malicious outcomes. Any serious FDI algorithm is based on an intimate knowledge of the system and its associated physics models, which renders conventional outlier/anomaly detection techniques almost obsolete in the face of such attacks. Thus, a critical need has emerged to develop a new class of defense methods that are capable of detecting FDI attacks under the assumption that the attacker has a strong familiarity with the system and its physics modeling. This class of defense methods are denoted by model-based defenses which are premised on the assumption that the attacker, while having a good understanding of the system, does not have full privileged access to all proprietary data and historical records of operation. However, (s)he is assumed to be capable of learning system behavior using self-learning techniques during an initial lie-in-wait period. To defend against this scenario, we propose a new model-based randomized window algorithm that searches time-series data for signatures that can serve as classifiers between normal and FDI scenarios. The classifiers are based on the correlations between the dominant degrees of freedom (DOFs) and the less-dominant DOFs (expected to be very sensitive to the system details that are unknown to the attacker). For demonstration, RELAP5 models are employed to calculate representative nuclear reactor behavior during a number of transient scenarios. Finally, falsified data are injected into the RELAP5-simulated behavior, and the proposed signature-identification algorithm is employed to detect the injected data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Spatiotemporal dynamics of nitrogen dioxide pollution and urban development: Satellite observations over China, 2005–2016

China experienced rapid urbanization and industrialization in recent years, which resulted in large increases of nitrogen dioxide (NO2), particularly in urban areas. We analyze the spatiotemporal variations of the vertical column densities (VCDs) of tropospheric NO2 with the level of urban development across China during the period 2005-2016. Nighttime light (NTL) data are applied as a proxy for urban development to investigate the relationship between NO, pollution and urban development. Both NO2 column data and NTL data are observed by satellite measurements. The annual mean of NO2 VCDs is presented on a grid basis. The average annual variations of NO2 VCDs and NTL value are detected for all 341 prefectural cities. Two-thirds of the cities reached their peak NO2 pollution level around 2010-2012, and decreased afterward. About 10% of the western cities kept on increasing their NO2 levels until 2016. All cities are classified into four categories with respect to the average annual variations of NO2/NTL in 2005-2010, and 2011-2016. Approximately 62% of the cities turned the average annual variations of NO2/NTL from positive to negative. 19% of the cities are in the positive-positive category and 11% of the cities are in the negative-negative category. The remaining 8% of the cities changed their ratios from negative to positive. We summarize the national air pollution regulations, and our results show the effectiveness of the regulations for most eastern China cities. This work can help to design more efficient regional emission reduction policies with sustainable development.

nighttime light data↗
DOE OSTI2013

Structurally distinct polycyclic aromatic hydrocarbons induce differential transcriptional responses in developing zebrafish

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous in the environment as components of fossil fuels and by-products of combustion. These multi-ring chemicals differentially activate the aryl hydrocarbon receptor (AHR) in a structurally dependent manner, and induce toxicity via both AHR-dependent and -independent mechanisms. PAH exposure is known to induce developmental malformations in zebrafish embryos, and recent studies have shown cardiac toxicity induced by compounds with low AHR affinity. Unraveling the potentially diverse molecular mechanisms of PAH toxicity is essential for understanding the hazard posed by complex PAH mixtures present in the environment. We analyzed transcriptional responses to PAH exposure in zebrafish embryos exposed to benz(a)anthracene (BAA), dibenzothiophene (DBT) and pyrene (PYR) at concentrations that induced developmental malformations by 120 h post-fertilization (hpf). Whole genome microarray analysis of mRNA expression at 24 and 48 hpf identified genes that were differentially regulated over time and in response to the three PAH structures. PAH body burdens were analyzed at both time points using GC–MS, and demonstrated differences in PAH uptake into the embryos. This was important for discerning dose-related differences from those that represented unique molecular mechanisms. While BAA misregulated the least number of transcripts, it caused strong induction of cyp1a and other genes known to be downstream of the AHR, which were not induced by the other two PAHs. Analysis of functional roles of misregulated genes and their predicted regulatory transcription factors also distinguished the BAA response from regulatory networks disrupted by DBT and PYR exposure. These results indicate that systems approaches can be used to classify the toxicity of PAHs based on the networks perturbed following exposure, and may provide a path for unraveling the toxicity of complex PAH mixtures. - Highlights: • Defined global mRNA expression changes induced by developmental exposure to PAHs • Determined PAH body burdens following developmental exposure • Genes uniquely induced by benz(a)anthracene included targets of the AHR and RELA • Dibenzothiophene and pyrene perturbed a distinct RELA network • Transcriptional networks reveal differential mechanisms of PAH toxicity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Blood lead level association with lower body weight in NHANES 1999–2006

Background: Lead exposure is associated with low birth-weight. The objective of this study is to determine whether lead exposure is associated with lower body weight in children, adolescents and adults. Methods: We analyzed data from NHANES 1999–2006 for participants aged ≥ 3 using multiple logistic and multivariate linear regression. Using age- and sex-standardized BMI Z-scores, overweight and obese children (ages 3–19) were classified by BMI ≥ 85th and ≥ 95th percentiles, respectively. The adult population (age ≥ 20) was classified as overweight and obese with BMI measures of 25–29.9 and ≥ 30, respectively. Blood lead level (BLL) was categorized by weighted quartiles. Results: Multivariate linear regressions revealed a lower BMI Z-score in children and adolescents when the highest lead quartile was compared to the lowest lead quartile (β (SE) = − 0.33 (0.07), p < 0.001), and a decreased BMI in adults (β (SE) = − 2.58 (0.25), p < 0.001). Multiple logistic analyses in children and adolescents found a negative association between BLL and the percentage of obese and overweight with BLL in the highest quartile compared to the lowest quartile (OR = 0.42, 95% CI: 0.30–0.59; and OR = 0.67, 95% CI: 0.52–0.88, respectively). Adults in the highest lead quartile were less likely to be obese (OR = 0.42, 95% CI: 0.35–0.50) compared to those in the lowest lead quartile. Further analyses with blood lead as restricted cubic splines, confirmed the dose-relationship between blood lead and body weight outcomes. Conclusions: BLLs are associated with lower body mass index and obesity in children, adolescents and adults. - Highlights: • NHANES analysis of BLL and body weight outcomes • Increased BLL associated with decreased body weight in children and adolescent • Increased BLL associated with decreased body weight in adults.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Diagnostic indicators for integrated assessment models of climate policy

Integrated assessments of how climate policy interacts with energy-economic systems can be performed by a variety of models with different functional structures. This article proposes a diagnostic scheme that can be applied to a wide range of integrated assessment models to classify differences among models based on their carbon price responses. Model diagnostics can uncover patterns and provide insights into why, under a given scenario, certain types of models behave in observed ways. Such insights are informative since model behavior can have a significant impact on projections of climate change mitigation costs and other policy-relevant information. The authors propose diagnostic indicators to characterize model responses to carbon price signals and test these in a diagnostic study with 11 global models. Indicators describe the magnitude of emission abatement and the associated costs relative to a harmonized baseline, the relative changes in carbon intensity and energy intensity and the extent of transformation in the energy system. This study shows a correlation among indicators suggesting that models can be classified into groups based on common patterns of behavior in response to carbon pricing. Such a classification can help to more easily explain variations among policy-relevant model results.

Climate change economics↗
DOE OSTI2022

What are mycorrhizal traits?

Traits are inherent properties of organisms, but how are they defined for organismal networks such as mycorrhizal symbioses? Mycorrhizal symbioses are complex and diverse belowground symbioses between plants and fungi that have proved challenging to fit into a unified and coherent trait framework. Here we propose an inclusive mycorrhizal trait framework that classifies traits as morphological, physiological, and phenological features that have functional implications for the symbiosis. We further classify mycorrhizal traits by location – plant, fungus, or the symbiosis – which highlights new questions in trait-based mycorrhizal ecology designed to charge and challenge the scientific community. This new framework is an opportunity for researchers to interrogate their data to identify novel insights and gaps in our understanding of mycorrhizal symbioses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Morphological classification of bioaerosols from composting using scanning electron microscopy

Highlights: • Bioaerosols were captured using the filter method. • Bioaerosols were analysed using scanning electron microscope. • Bioaerosols were classified on the basis of morphology. • Single small cells were found more frequently than aggregates and larger cells. • Smaller cells may disperse further than heavier aggregate structures. - Abstract: This research classifies the physical morphology (form and structure) of bioaerosols emitted from open windrow composting. Aggregation state, shape and size of the particles captured are reported alongside the implications for bioaerosol dispersal after release. Bioaerosol sampling took place at a composting facility using personal air filter samplers. Samples were analysed using scanning electron microscopy. Particles were released mainly as small (<1 μm) single, spherical cells, followed by larger (>1 μm) single cells, with aggregates occurring in smaller proportions. Most aggregates consisted of clusters of 2–3 particles as opposed to chains, and were <10 μm in size. No cells were attached to soil debris or wood particles. These small single cells or small aggregates are more likely to disperse further downwind from source, and cell viability may be reduced due to increased exposure to environmental factors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Importance of patient education on home medical care waste disposal in Japan

Highlights: • Attached office nurses more recovered medical waste from patients’ homes. • Most nurses educated their patients on how to store home medical care waste in their homes and on how to separate them. • Around half of nurses educated their patients on where to dispose of their home medical care waste. - Abstract: To determine current practices in the disposal and handling of home medical care (HMC) waste, a questionnaire was mailed to 1965 offices nationwide. Of the office that responded, 1283 offices were analyzed. Offices were classified by management configuration: those attached to hospitals were classified as ”attached offices” and others as “independent offices”. More nurses from attached offices recovered medical waste from patients’ homes than those from independent offices. Most nurses educated their patients on how to store HMC waste in their homes (79.3% of total) and on how to separate HMC waste (76.5% of total). On the other hand, only around half of nurses (47.3% from attached offices and 53.2% from independent offices) educated their patients on where to dispose of their HMC waste. 66.0% of offices replied that patients had separated their waste appropriately. The need for patient education has emerged in recent years, with education for nurses under the diverse conditions of HMC being a key factor in patient education.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

High-content image informatics of the structural nuclear protein NuMA parses trajectories for stem/progenitor cell lineages and oncogenic transformation

Stem and progenitor cells that exhibit significant regenerative potential and critical roles in cancer initiation and progression remain difficult to characterize. Cell fates are determined by reciprocal signaling between the cell microenvironment and the nucleus; hence parameters derived from nuclear remodeling are ideal candidates for stem/progenitor cell characterization. Here we applied high-content, single cell analysis of nuclear shape and organization to examine stem and progenitor cells destined to distinct differentiation endpoints, yet undistinguishable by conventional methods. Nuclear descriptors defined through image informatics classified mesenchymal stem cells poised to either adipogenic or osteogenic differentiation, and oligodendrocyte precursors isolated from different regions of the brain and destined to distinct astrocyte subtypes. Nuclear descriptors also revealed early changes in stem cells after chemical oncogenesis, allowing the identification of a class of cancer-mitigating biomaterials. To capture the metrology of nuclear changes, we developed a simple and quantitative “imaging-derived” parsing index, which reflects the dynamic evolution of the high-dimensional space of nuclear organizational features. A comparative analysis of parsing outcomes via either nuclear shape or textural metrics of the nuclear structural protein NuMA indicates the nuclear shape alone is a weak phenotypic predictor. In contrast, variations in the NuMA organization parsed emergent cell phenotypes and discerned emergent stages of stem cell transformation, supporting a prognosticating role for this protein in the outcomes of nuclear functions. - Highlights: • High-content analysis of nuclear shape and organization classify stem and progenitor cells poised for distinct lineages. • Early oncogenic changes in mesenchymal stem cells (MSCs) are also detected with nuclear descriptors. • A new class of cancer-mitigating biomaterials was identified based on image informatics. • Textural metrics of the nuclear structural protein NuMA are sufficient to parse emergent cell phenotypes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Developing a heterogeneous ensemble learning framework to evaluate Alkali-silica reaction damage in concrete using acoustic emission signals

The monitoring and evaluation of Alkali-silica reaction (ASR) damage in concrete structures are required to ensure the serviceability and integrity of concrete infrastructures such as bridges and dams. The innovation of this paper lies in the development of an automatic ASR monitoring and evaluation approach by leveraging acoustic emission (AE) and a heterogeneous ensemble learning framework. Here, in this paper, ASR was monitored by AE sensors attached to a concrete specimen, which was placed in a chamber with high humidity and temperature. The recorded AE signals were filtered and divided by four ASR phases according to signal strength, crack width and expansion strain. A heterogeneous ensemble network including convolutional neural networks (CNN) and random forest models was employed to learn different features from AE signals and classify the AE signals into their corresponding phases. The results suggest that the proposed model has a high performance and classifies the signals into the assigned phases with high accuracy.

42 ENGINEERING↗
DOE OSTI2024

Evanescent and inertial-like waves in rigidly rotating odd viscous liquids

Three-dimensional non-rotating odd viscous liquids give rise to Taylor columns and support axisymmetric inertial-like waves ( J. Fluid Mech ., vol. 973, 2023, A30). When an odd viscous liquid is subjected to rigid-body rotation however, there arise in addition a plethora of other phenomena that need to be clarified. In this paper, we show that three-dimensional incompressible or two-dimensional compressible odd viscous liquids, rotating rigidly with angular velocity 𝛺, give rise to both oscillatory and evanescent inertial-like waves or a combination thereof (which we call of mixed type) that can be non-axisymmetric. By evanescent, we mean that along the radial direction, typically when moving away from a solid boundary, the velocity field decreases exponentially. These waves precess in a prograde or retrograde manner with respect to the rotating frame. The oscillatory and evanescent waves resemble respectively the body and wall-modes observed in (non-odd) rotating Rayleigh–Bénard convection ( J. Fluid Mech ., vol. 248, 1993, pp. 583–604). We show that the three types of waves (wall, body or mixed) can be classified with respect to pairs of planar wavenumbers 𝜅 which are complex, real or a combination, respectively. Experimentally, by observing the precession rate of the patterns, it would be possible to determine the largely unknown values of the odd viscosity coefficients. This formulation recovers as special cases recent studies of equatorial or topological waves in two-dimensional odd viscous liquids which provided examples of the bulk–interface correspondence at frequencies 𝜔 < 2⁢𝛺. We finally point out that the two- and three-dimensional problems are formally equivalent. Their difference then lies in the way data propagate along characteristic rays in three dimensions, which we demonstrate by classifying the resulting Poincaré–Cartan equations.

Mechanics↗
DOE OSTI2016

Chemical Attribution of Fentanyl Using Multivariate Statistical Analysis of Orthogonal Mass Spectral Data

Attribution of the origin of an illicit drug relies on identification of compounds indicative of its clandestine production and is a key component of many modern forensic investigations. Here, the results of these studies can yield detailed information on method of manufacture, starting material source, and final product, all critical forensic evidence. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic fentanyl, N-(1-phenylethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods, all previously published fentanyl synthetic routes or hybrid versions thereof, were studied in an effort to identify and classify route-specific signatures. A total of 160 distinct compounds and inorganic species were identified using gas and liquid chromatographies combined with mass spectrometric methods (gas chromatography/mass spectrometry (GC/MS) and liquid chromatography–tandem mass spectrometry-time of-flight (LC–MS/MS-TOF)) in conjunction with inductively coupled plasma mass spectrometry (ICPMS). The complexity of the resultant data matrix urged the use of multivariate statistical analysis. Using partial least-squares-discriminant analysis (PLS-DA), 87 route-specific CAS were classified and a statistical model capable of predicting the method of fentanyl synthesis was validated and tested against CAS profiles from crude fentanyl products deposited and later extracted from two operationally relevant surfaces: stainless steel and vinyl tile. Finally, this work provides the most detailed fentanyl CAS investigation to date by using orthogonal mass spectral data to identify CAS of forensic significance for illicit drug detection, profiling, and attribution.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Human Microdosing with Carcinogenic Polycyclic Aromatic Hydrocarbons: In Vivo Pharmacokinetics of Dibenzo[ def,p ]chrysene and Metabolites by UPLC Accelerator Mass Spectrometry

Metabolism is a key health risk factor following exposures to pro-carcinogenic polycyclic aromatic hydrocarbons (PAHs) such as dibenzo[def,p]chrysene (DBC), an IARC classified 2A probable human carcinogen. Human exposure to PAHs occurs primarily from the diet in non-smokers. However, little data is available on the metabolism and pharmacokinetics in humans of high molecular weight PAHs (≥4 aromatic rings), including DBC. We previously determined the pharmacokinetics of DBC in human volunteers orally administered a micro-dose (29 ng; 5 nCi) of [14C]-DBC by accelerator mass spectrometry (AMS) analysis of total [14C] in plasma and urine. In the current study, we utilized a novel “moving wire” interface between ultra-performance liquid chromatography (UPLC) and AMS to detect and quantify parent DBC and its major metabolites. The major [14C] product identified in plasma was unmetabolized [14C]-DBC itself, (Cmax= 18.5 ± 15.9 fg/mL, Tmax= 2.1 ± 1.0 h), whereas the major metabolite was identified as [14C]-(+/-)-DBC-11,12-diol (Cmax= 2.5 ± 1.3 fg/mL, Tmax= 1.8 h). Several minor species of [14C]-DBC metabolites were also detected for which no reference standards were available. Free and conjugated metabolites were detected in urine with [14C]-(+/-)-DBC-11,12,13,14-tetraol isomers identified as the major metabolites, 56.3% of which were conjugated (Cmax= 35.8 ± 23.0 pg/pool, Tmax= 6-12 h pool). [14C]-DBC-11,12-diol, of which 97.5% was conjugated, was also identified in urine (Cmax= 29.4 ± 11.6 pg/pool, Tmax= 6-12 h pool). Parent [14C]-DBC was not detected in urine. This is the first dataset to assess metabolite profiles and associated pharmacokinetics of a carcinogenic PAH in human volunteers at an environmentally relevant dose, providing the data necessary for translation of high dose animal models to humans for translation of environmental health risk assessment.

Madeen, Erin P.↗
DOE OSTI2024

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the “black-box” nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having “no change” (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Pose Classification Using Three-Dimensional Atomic Structure-Based Neural Networks Applied to Ion Channel–Ligand Docking

The identification of promising lead compounds showing pharmacological activities toward a biological target is essential in early stage drug discovery. With the recent increase in available small-molecule databases, virtual high-throughput screening using physics-based molecular docking has emerged as an essential tool in assisting fast and cost-efficient lead discovery and optimization. However, the best scored docking poses are often suboptimal, resulting in incorrect screening and chemical property calculation. We address the pose classification problem by leveraging data-driven machine learning approaches to identify correct docking poses from AutoDock Vina and Glide screens. To enable effective classification of docking poses, we present two convolutional neural network approaches: a three-dimensional convolutional neural network (3D-CNN) and an attention-based point cloud network (PCN) trained on the PDBbind refined set. We demonstrate the effectiveness of our proposed classifiers on multiple evaluation data sets including the standard PDBbind CASF-2016 benchmark data set and various compound libraries with structurally different protein targets including an ion channel data set extracted from Protein Data Bank (PDB) and an in-house KCa3.1 inhibitor data set. Our experiments show that excluding false positive docking poses using the proposed classifiers improves virtual high-throughput screening to identify novel molecules against each target protein compared to the initial screen based on the docking scores.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Accurate, Uncertainty-Aware Classification of Molecular Chemical Motifs from Multimodal X-ray Absorption Spectroscopy

Accurate classification of molecular chemical motifs from experimental measurement is an important problem in molecular physics, chemistry, and biology. In this work, we present neural network ensemble classifiers for predicting the presence (or lack thereof) of 41 different chemical motifs on small molecules from simulated C, N, and O K-edge X-ray absorption near-edge structure (XANES) spectra. Our classifiers not only achieve class-balanced accuracies of more than 0.95 but also accurately quantify uncertainty. Here, we also show that including multiple XANES modalities improves predictions notably on average, demonstrating a “multimodal advantage” over any single modality. In addition to structure refinement, our approach can be generalized to broad applications with molecular design pipelines.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

A van der Waals Inclusive Density Functional Theory Study of the Nature of Bonding for Thiophene Adsorption on Ni(100) and Cu(100) Surfaces

We report the results of the first-principles density functional theory calculations for the adsorption of thiophene (C 4 H 4 S) on Cu(100) and Ni(100) surfaces. The adsorption properties on these surfaces are evaluated for several adsorption configurations, and the calculations are performed with the inclusion of van der Waals (vdW) interactions invoking the optimized vdW (optB86b-vdW, optB88-vdW, and optPBE-vdW) as well as revPBE-vdW and rPW86-vdW2 functionals. In agreement with the earlier reports, on both surfaces, the most stable adsorption configurations correspond to the hollow-45 at which the thiophene adsorbs parallel to the surface. For the adsorption on Ni(100), in agreement with the experimental observations, we confirm that thiophene spontaneously ruptures via breaking of a C–S bond when the center of the molecule is placed over a bridge site. On the basis of both the bond distribution and charge transfer analysis, we conclude that the rupturing of thiophene observed at the bridge-45 configuration on Ni(100) may result from large charge transfer from the surface. The analyses of the electronic and geometric structures and charge transfer reveal that the interaction of the thiophene molecule with Ni(100) is much stronger than that of the Cu(100) surface. Upon adsorption of the molecule, for the former surface, we observed much larger charge transfer, non-negligible changes in the positions of the surface atoms, and appreciable electronic structure changes (i.e., in the center and width of the d-band). The adsorption of thiophene on Ni(100) also leads to the decrease in the magnetic moment of the surface Ni atoms particularly those in the vicinity of the adsorbed molecule. All these observations considered, we conclude that the nature of bonding on Ni(100) can be classified as strong chemisorption, while on Cu(100) it can be classified between weak and strong physisorption depending on the vdW functional used.

36 MATERIALS SCIENCE↗