An integrated mass spectrometry imaging and digital pathology workflow for objective detection of colorectal tumours by unique atomic signatures

Tumours are abnormal growths of cells that reproduce by redirecting essential nutrients and resources from surrounding tissue. Changes to cell metabolism that trigger the growth of tumours are reflected in subtle differences between the chemical composition of healthy and malignant cells. We used LA-ICP-MS imaging to investigate whether these chemical differences can be used to spatially identify tumours and support detection of primary colorectal tumours in anatomical pathology. First, we generated quantitative LA-ICP-MS images of three colorectal surgical resections with case-matched normal intestinal wall tissue and used this data in a Monte Carlo optimisation experiment to develop an algorithm that can classify pixels as tumour positive or negative. Blinded testing and interrogation of LA-ICP-MS images with micrographs of haematoxylin and eosin stained and Ki67 immunolabelled sections revealed Monte Carlo optimisation accurately identified primary tumour cells, as well as returning false positive pixels in areas of high cell proliferation. We analysed an additional 11 surgical resections of primary colorectal tumours and re-developed our image processing method to include a random forest regression machine learning model to correctly identify heterogenous tumours and exclude false positive pixels in images of non-malignant tissue. Our final model used over 1.6 billion calculations to correctly discern healthy cells from various types and stages of invasive colorectal tumours. The imaging mass spectrometry and data analysis methods described, developed in partnership with clinical cancer researchers, have the potential to further support cancer detection as part of a comprehensive digital pathology approach to cancer care through validation of a new chemical biomarker of tumour cells.

Digital pathology and laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) imaging reveals a unique elemental signature of colorectal cancer.     

MolE8: finding DFT potential energy surface minima values from force-field optimised organic molecules with new machine learning representations

The use of machine learning techniques in computational chemistry has gained significant momentum since large molecular databases are now readily available. Predictions of molecular properties using machine learning have advantages over the traditional quantum mechanics calculations because they can be cheaper computationally without losing the accuracy. We present a new extrapolatable and explainable molecular representation based on bonds, angles and dihedrals that can be used to train machine learning models. The trained models can accurately predict the electronic energy and the free energy of small organic molecules with atom types C, H N and O, with a mean absolute error of 1.2 kcal mol⁻¹. The models can be extrapolated to larger organic molecules with an average error of less than 3.7 kcal mol⁻¹ for 10 or fewer heavy atoms, which represent a chemical space two orders of magnitude larger. The rapid energy predictions of multiple molecules, up to 7 times faster than previous ML models of similar accuracy, has been achieved by sampling geometries around the potential energy surface minima. Therefore, the input geometries do not have to be located precisely on the minima and we show that accurate density functional theory energy predictions can be made from force-field optimised geometries with a mean absolute error 2.5 kcal mol⁻¹.

New representations and machine learning calculate DFT minima from force field geometries.     

Control of the Cu morphology on Ru-passivated and Ru-doped TaN surfaces – promoting growth of 2D conducting copper for CMOS interconnects

Prolonging the lifetime of Cu as a level 1 and level 2 interconnect metal in future nanoelectronic devices is a significant challenge as device dimensions continue to shrink and device structures become more complex. At nanoscale dimensions Cu exhibits high resistivity which prevents its functioning as a conducting wire and prefers to form non-conducting 3D islands. Given that changing from Cu to an alternative metal is challenging, we are investigating new materials that combine properties of diffusion barriers and seed liners to reduce the thickness of this layer and to promote successful electroplating of Cu to facilitate the coating of high-aspect ratio interconnect vias and to allow for optimal electrical conductance. In this study we propose new combined barrier/liner materials based on modifying the surface layer of the TaN barrier through Ru incorporation. Simulating a model Cu₂₉ structure at 0 K and through finite temperature ab initio molecular dynamics on these surfaces allows us to demonstrate how the Ru content can control copper wetting, adhesion and thermal stability properties. Activation energies for atom migrations onto a nucleating copper island allow insight into the growth mechanism of a Cu thin-film. Using this understanding allows us to tailor the Ru content on TaN to control the final morphology of the Cu film. These Ru-modified TaN films can be deposited by atomic layer deposition, allowing for fine control over the film thickness and composition.

Modifying the surface layer of the barrier material TaN with Ru controls the morphology of deposited copper.     

Electrostatic regulation of blue copper sites

In the last 50 years, the blue copper proteins became central targets of investigation. Extensive experiments focused on the Cu coordination to probe the effect of local perturbations on its properties. We found that local electric fields, generated by charged residues evolutionarily placed throughout the protein edifice, mainly second sphere, but also more remotely, constitute an additional significant factor regulating blue copper proteins. These fields are not random, but exhibit a highly specific directionality, negative with respect to the and vectors in the Cu first shell. The field magnitude contributes to fine-tuning of the geometric and electronic properties of Cu sites in individual blue copper proteins. Specifically, the local electric fields evidently control the Cu–S_Met bond distance, Cu(ii)–S_Cys bond covalency, and the energies of the frontier molecular orbitals, which, in turn, govern the Cu(ii/i) reduction potential and the relative absorption intensities at 450 nm and 600 nm.

Intramolecular electric fields in blue copper proteins are oriented in a fixed way to modulate properties of their copper sites: they control the first-shell copper interactions to influence geometric, spectroscopic, and redox behavior.     

NanoSIMS combined with fluorescence microscopy as a tool for subcellular imaging of isotopically labeled platinum-based anticancer drugs

Multi-elemental, isotope selective nano-scale secondary ion mass spectrometry (NanoSIMS) combined with confocal laser-scanning microscopy was used to characterize the subcellular distribution of ¹⁵N-labeled cisplatin in human colon cancer cells. These analyses indicated predominant cisplatin colocalisation with sulfur-rich structures in both the nucleus and cytoplasm. Furthermore, colocalisation of platinum with phosphorus-rich chromatin regions was observed, which is consistent with its binding affinity to DNA as the generally accepted crucial target of the drug. Application of ¹⁵N-labeled cisplatin and subsequent measurement of the nitrogen isotopic composition and determination of the relative intensities of platinum and nitrogen associated secondary ion signals in different cellular compartments with NanoSIMS suggested partial dissociation of Pt–N bonds during the accumulation process, in particular within nucleoli at elevated cisplatin concentrations. This finding raises the question as to whether the observed intracellular dissociation of the drug has implications for the mechanism of action of cisplatin. Within the cytoplasm, platinum mainly accumulated in acidic organelles, as demonstrated by a direct combination of specific fluorescent staining, confocal laser scanning microscopy and NanoSIMS. Different processing of platinum drugs in acidic organelles might be relevant for their detoxification, as well as for their mode of action.

NanoSIMS combined with fluorescence microscopy reveals subcellular structures in cancer cells where ¹⁵N-labeled cisplatin is accumulated, with implications for Pt–N bond integrity.     

Making thermodynamic models of mixtures predictive by machine learning: matrix completion of pair interactions

Predictive models of thermodynamic properties of mixtures are paramount in chemical engineering and chemistry. Classical thermodynamic models are successful in generalizing over (continuous) conditions like temperature and concentration. On the other hand, matrix completion methods (MCMs) from machine learning successfully generalize over (discrete) binary systems; these MCMs can make predictions without any data for a given binary system by implicitly learning commonalities across systems. In the present work, we combine the strengths from both worlds in a hybrid approach. The underlying idea is to predict the pair-interaction energies, as they are used in basically all physical models of liquid mixtures, by an MCM. As an example, we embed an MCM into UNIQUAC, a widely-used physical model for the Gibbs excess energy. We train the resulting hybrid model in a Bayesian machine-learning framework on experimental data for activity coefficients in binary systems of 1146 components from the Dortmund Data Bank. We thereby obtain, for the first time, a complete set of UNIQUAC parameters for all binary systems of these components, which allows us to predict, in principle, activity coefficients at arbitrary temperature and composition for any combination of these components, not only for binary but also for multicomponent systems. The hybrid model even outperforms the best available physical model for predicting activity coefficients, the modified UNIFAC (Dortmund) model.

Embedding matrix completion methods from machine learning in classical thermodynamic models creates powerful hybrid models for predicting properties of mixtures.     

On-surface synthesis of organocopper metallacycles through activation of inner diacetylene moieties

The design of organometallic complexes is at the heart of modern organic chemistry and catalysis. Recently, on-surface synthesis has emerged as a disruptive paradigm to design previously precluded compounds and nanomaterials. Despite these advances, the field of organometallic chemistry on surfaces is still at its infancy. Here, we introduce a protocol to activate the inner diacetylene moieties of a molecular precursor by copper surface adatoms affording the formation of unprecedented organocopper metallacycles on Cu(111). The chemical structure of the resulting complexes is characterized by scanning probe microscopy and X-ray photoelectron spectroscopy, being complemented by density functional theory calculations and scanning probe microscopy simulations. Our results pave avenues to the engineering of organometallic compounds and steer the development of polyyne chemistry on surfaces.

The diacetylene skeletons of DNBD precursors are attacked on Cu(111) by copper adatoms resulting in the synthesis of organocopper metallacycles.     

Choosing the right molecular machine learning potential

Quantum-chemistry simulations based on potential energy surfaces of molecules provide invaluable insight into the physicochemical processes at the atomistic level and yield such important observables as reaction rates and spectra. Machine learning potentials promise to significantly reduce the computational cost and hence enable otherwise unfeasible simulations. However, the surging number of such potentials begs the question of which one to choose or whether we still need to develop yet another one. Here, we address this question by evaluating the performance of popular machine learning potentials in terms of accuracy and computational cost. In addition, we deliver structured information for non-specialists in machine learning to guide them through the maze of acronyms, recognize each potential''s main features, and judge what they could expect from each one.

This article provides a lifeline for those lost in the sea of the molecular machine learning potentials by providing a balanced overview and evaluation of popular potentials.     

Direct growth of crystalline triazine-based graphdiyne using surface-assisted deprotection–polymerisation

Graphdiyne polymers have interesting electronic properties due to their π-conjugated structure and modular composition. Most of the known synthetic pathways for graphdiyne polymers yield amorphous solids because the irreversible formation of carbon–carbon bonds proceeds under kinetic control and because of defects introduced by the inherent chemical lability of terminal alkyne bonds in the monomers. Here, we present a one-pot surface-assisted deprotection/polymerisation protocol for the synthesis of crystalline graphdiynes over a copper surface starting with stable trimethylsilylated alkyne monomers. In comparison to conventional polymerisation protocols, our method yields large-area crystalline thin graphdiyne films and, at the same time, minimises detrimental effects on the monomers like oxidation or cyclotrimerisation side reactions typically associated with terminal alkynes. A detailed study of the reaction mechanism reveals that the deprotection and polymerisation of the monomer is promoted by Cu(ii) oxide/hydroxide species on the as-received copper surface. These findings pave the way for the scalable synthesis of crystalline graphdiyne-based materials as cohesive thin films.

We present a one-pot deprotection/polymerisation protocol for the synthesis of crystalline graphdiynes on top of a copper surface starting with stable trimethylsilylated alkyne monomers.      

Manipulating electron transfer – the influence of substituents on novel copper guanidine quinolinyl complexes

Copper guanidine quinolinyl complexes act as good entatic state models due to their distorted structures leading to a high similarity between Cu(i) and Cu(ii) complexes. For a better understanding of the entatic state principle regarding electron transfer a series of guanidine quinolinyl ligands with different substituents in the 2- and 4-position were synthesized to examine the influence on the electron transfer properties of the corresponding copper complexes. Substituents with different steric or electronic influences were chosen. The effects on the properties of the copper complexes were studied applying different experimental and theoretical methods. The molecular structures of the bis(chelate) copper complexes were examined in the solid state by single-crystal X-ray diffraction and in solution by X-ray absorption spectroscopy and density functional theory (DFT) calculations revealing a significant impact of the substituents on the complex structures. For a better insight natural bond orbital (NBO) calculations of the ligands and copper complexes were performed. The electron transfer was analysed by the determination of the electron self-exchange rates following Marcus theory. The obtained results were correlated with the results of the structural analysis of the complexes and of the NBO calculations. Nelsen''s four-point method calculations give a deeper understanding of the thermodynamic properties of the electron transfer. These studies reveal a significant impact of the substituents on the properties of the copper complexes.

Copper guanidine quinolinyl complexes act as good entatic state models for the electron transfer due to a high similarity between the corresponding Cu(i) and Cu(ii) complexes. The introduction of substituents leads to a further enhancement.     

Cu(i) catalysis for selective condensation/bicycloaromatization of two different arylalkynes: direct and general construction of functionalized C–N axial biaryl compounds

Selective condensation/bicycloaromatization of two different arylalkynes is firstly developed under ligand-free copper(i)-catalysis, which allows the direct synthesis of C–N axial biaryl compounds in high yields with excellent selectivity and functional group tolerance. Due to the critical effects of Cu(i) catalyst and HFIP, many easily occurring undesired reactions are suppressed, and the coupled five–six aromatic rings are constructed via the selective formation of two C(sp²)–N(sp²) bonds and four C(sp²)–C(sp²) bonds. The achievement of moderate enantioselectivity verifies its potential for the simplest asymmetric synthesis of atropoisomeric biaryls. Western blotting demonstrated that the newly developed compounds are promising targets in biology and pharmaceuticals. This unique reaction can construct structurally diverse C–N axial biaryl compounds that have never been reported by other methods, and might be extended to various applications in materials, chemistry, biology, and pharmaceuticals.

Selective condensation/bicycloaromatization of two different arylalkynes is firstly developed under ligand-free copper(i)-catalysis, which allows the direct synthesis of C–N axial biaryl compounds in high yields with excellent selectivity and functional group tolerance.     

A novel approach to measure isotope ratios via multi-collector—inductively coupled plasma—mass spectrometry based on sample mixing with a non-enriched standard

In this work, a novel approach to measure isotope ratios via multi-collector—inductively coupled plasma—mass spectrometry (MC-ICP-MS) for low amounts of target element is proposed. The methodology is based on mixing of the sample (target element isolate) with a non-enriched in-house standard, previously characterized for its isotopic composition. This methodology has been applied to isotopic analysis of Cu and of Fe in whole blood samples. For this purpose, different mixtures of sample + in-house standard were prepared and adjusted to a final concentration of 500 μg/L of the target elements for isotopic analysis. δ⁶⁵Cu, δ⁵⁶Fe, and δ⁵⁷Fe varied linearly as a function of the amount of in-house standard (or of sample) present in the mixture. The isotopic composition of the sample was calculated considering the isotope ratios measured for (i) the mixture and (ii) the in-house standard and (iii) the relative concentrations of target element contributed by the sample and the standard to the mixture, respectively. For validation purposes, the isotopic analysis of whole blood Cu was carried out using both the conventional (using 2 mL of whole blood) and the newly developed approach (using 500 μL of whole blood). The δ⁶⁵Cu values obtained using mixtures containing 40 % (200 μg/L) of Cu from the blood samples and 60 % (300 μg/L) of Cu from the in-house standard were in good agreement with the δ⁶⁵Cu value obtained using the conventional approach (bias ≤0.15?‰).     

A peptide-derived strategy for specifically targeting the mitochondria and ER of cancer cells: a new approach in fighting cancer

An effective anti-cancer therapy should exclusively target cancer cells and trigger in them a broad spectrum of cell death pathways that will prevent avoidance. Here, we present a new approach in cancer therapy that specifically targets the mitochondria and ER of cancer cells. We developed a peptide derived from the flexible and transmembrane domains of the human protein NAF-1/CISD2. This peptide (NAF-1^44-67) specifically permeates through the plasma membranes of human epithelial breast cancer cells, abolishes their mitochondria and ER, and triggers cell death with characteristics of apoptosis, ferroptosis and necroptosis. In vivo analysis revealed that the peptide significantly decreases tumor growth in mice carrying xenograft human tumors. Computational simulations of cancer vs. normal cell membranes reveal that the specificity of the peptide to cancer cells is due to its selective recognition of their membrane composition. NAF-1^44-67 represents a promising anti-cancer lead compound that acts via a unique mechanism.

An effective anti-cancer therapy should exclusively target cancer cells and trigger in them a broad spectrum of cell death pathways that will prevent avoidance.     

Mass spectrometric investigations of the kinetic stability of chromium and copper complexes with humic substances by isotope-labelling experiments

Isotope-labelling exchange experiments were carried out to investigate the kinetic stability of Cr(III) complexes with humic substances (HS). To compare the results with those of an ion, not expected to form kinetically stable HS complexes with respect to its electron configuration, Cu(II) was investigated under the same conditions. HS solutions of different origin were therefore spiked with ⁵³Cr(III) or ⁶⁵Cu(II) after saturation of HS with chromium and copper of natural isotopic composition. In fractions of metal/HS complexes with different molecular weight, obtained by ultrafiltration and HPLC/ICP-MS using size exclusion chromatography (SEC), respectively, the isotope ratios of chromium and copper were determined by ICP and thermal ionisation mass spectrometry. Distinct differences in the isotopic composition of chromium were found in the permeate of the ultrafiltration compared with the corresponding unseparated solution, which indicates kinetically stable Cr(III)/HS complexes. On the other hand, the copper isotopic composition was identical in the permeate and the unseparated solution, which shows that a total exchange of Cu²⁺ ions took place between free and HS complexed copper ions. The SEC/ ICP-MS experiments also resulted in a different isotopic distribution of chromium in the chromatographically separated complexes whereas the copper complexes, separated by SEC, showed identical isotopic composition. The kinetic stability of Cr(III)/HS complexes could be explained by the d³ electron configuration of Cr³⁺ ions, a fact which is well known from classical Cr(III) complexes, and influences substantially the mobility of this heavy metal in the environment.     

A framework for automated structure elucidation from routine NMR spectra

Methods to automate structure elucidation that can be applied broadly across chemical structure space have the potential to greatly accelerate chemical discovery. NMR spectroscopy is the most widely used and arguably the most powerful method for elucidating structures of organic molecules. Here we introduce a machine learning (ML) framework that provides a quantitative probabilistic ranking of the most likely structural connectivity of an unknown compound when given routine, experimental one dimensional ¹H and/or ¹³C NMR spectra. In particular, our ML-based algorithm takes input NMR spectra and (i) predicts the presence of specific substructures out of hundreds of substructures it has learned to identify; (ii) annotates the spectrum to label peaks with predicted substructures; and (iii) uses the substructures to construct candidate constitutional isomers and assign to them a probabilistic ranking. Using experimental spectra and molecular formulae for molecules containing up to 10 non-hydrogen atoms, the correct constitutional isomer was the highest-ranking prediction made by our model in 67.4% of the cases and one of the top-ten predictions in 95.8% of the cases. This advance will aid in solving the structure of unknown compounds, and thus further the development of automated structure elucidation tools that could enable the creation of fully autonomous reaction discovery platforms.

A machine learning model and graph generator were able to accurately predict for the presence of nearly 1000 substructures and the connectivity of small organic molecules from experimental 1D NMR data.     

Machine learning and semi-empirical calculations: a synergistic approach to rapid,accurate, and mechanism-based reaction barrier prediction

Modern QM modelling methods, such as DFT, have provided detailed mechanistic insights into countless reactions. However, their computational cost inhibits their ability to rapidly screen large numbers of substrates and catalysts in reaction discovery. For a C–C bond forming nitro-Michael addition, we introduce a synergistic semi-empirical quantum mechanical (SQM) and machine learning (ML) approach that allows the prediction of DFT-quality reaction barriers in minutes, even on a standard laptop using widely available modelling software. Mean absolute errors (MAEs) are obtained that are below the accepted chemical accuracy threshold of 1 kcal mol⁻¹ and substantially better than SQM methods without ML correction (5.71 kcal mol⁻¹). Predictive power is shown to hold when the ML models are applied to an unseen set of compounds from the toxicology literature. Mechanistic insight is also achieved via the generation of full SQM transition state (TS) structures which are found to be very good approximations for the DFT-level geometries, revealing important steric interactions in some TSs. This combination of speed, accuracy, and mechanistic insight is unprecedented; current ML barrier models compromise on at least one of these important criteria.

A synergistic approach that combines machine learning with semi-empirical methods enables the fast and accurate prediction of DFT-quality reaction barriers, with mechanistic insights available from semi-empirical transition state geometries.     

Mass spectrometric investigations of the kinetic stability of chromium and copper complexes with humic substances by isotope-labelling experiments

Isotope-labelling exchange experiments were carried out to investigate the kinetic stability of Cr(III) complexes with humic substances (HS). To compare the results with those of an ion, not expected to form kinetically stable HS complexes with respect to its electron configuration, Cu(II) was investigated under the same conditions. HS solutions of different origin were therefore spiked with ⁵³Cr(III) or ⁶⁵Cu(II) after saturation of HS with chromium and copper of natural isotopic composition. In fractions of metal/HS complexes with different molecular weight, obtained by ultrafiltration and HPLC/ICP-MS using size exclusion chromatography (SEC), respectively, the isotope ratios of chromium and copper were determined by ICP and thermal ionisation mass spectrometry. Distinct differences in the isotopic composition of chromium were found in the permeate of the ultrafiltration compared with the corresponding unseparated solution, which indicates kinetically stable Cr(III)/HS complexes. On the other hand, the copper isotopic composition was identical in the permeate and the unseparated solution, which shows that a total exchange of Cu²⁺ ions took place between free and HS complexed copper ions. The SEC/ ICP-MS experiments also resulted in a different isotopic distribution of chromium in the chromatographically separated complexes whereas the copper complexes, separated by SEC, showed identical isotopic composition. The kinetic stability of Cr(III)/HS complexes could be explained by the d³ electron configuration of Cr³⁺ ions, a fact which is well known from classical Cr(III) complexes, and influences substantially the mobility of this heavy metal in the environment. Received: 7 December 1998 / Revised: 25 March 1999 / Accepted: 27 March 1999     

Machine learning designs non-hemolytic antimicrobial peptides

Machine learning (ML) consists of the recognition of patterns from training data and offers the opportunity to exploit large structure–activity databases for drug design. In the area of peptide drugs, ML is mostly being tested to design antimicrobial peptides (AMPs), a class of biomolecules potentially useful to fight multidrug-resistant bacteria. ML models have successfully identified membrane disruptive amphiphilic AMPs, however mostly without addressing the associated toxicity to human red blood cells. Here we trained recurrent neural networks (RNN) with data from DBAASP (Database of Antimicrobial Activity and Structure of Peptides) to design short non-hemolytic AMPs. Synthesis and testing of 28 generated peptides, each at least 5 mutations away from training data, allowed us to identify eight new non-hemolytic AMPs against Pseudomonas aeruginosa, Acinetobacter baumannii, and methicillin-resistant Staphylococcus aureus (MRSA). These results show that machine learning (ML) can be used to design new non-hemolytic AMPs.

Machine learning models trained with experimental data for antimicrobial activity and hemolysis are shown to produce new non-hemolytic antimicrobial peptides active against multidrug-resistant bacteria.     

Complexation of copper(I) by thiourea in acidic aqueous solution

The interaction of copper(II) and copper(I) with thiourea(Tu) has been investigated by UV and visible spectrophotometry. Over the range of concentrations of copper(I) and Tu(0.1–20)×10^–3 mol-dm^–3 in acid aqueous solutions there are two complexes, CuTu₂ ⁺ (log ₂=11.1) and the other has the ratio Cu/Tu=1/1 with the likely composition Cu₂Tu₂ ²⁺ with log ₂₂=18.5. By the determination of copper(0) solubility in acid thiourea solution and potentiometric measurements it was shown that the potential of the copper electrode is that of a non-equilibrium (corrosive) electrode.     

Plasma-assisted oxidation of Cu(100) and Cu(111)

Oxidized copper surfaces have attracted significant attention in recent years due to their unique catalytic properties, including their enhanced hydrocarbon selectivity during the electrochemical reduction of CO₂. Although oxygen plasma has been used to create highly active copper oxide electrodes for CO₂RR, how such treatment alters the copper surface is still poorly understood. Here, we study the oxidation of Cu(100) and Cu(111) surfaces by sequential exposure to a low-pressure oxygen plasma at room temperature. We used scanning tunnelling microscopy (STM), low energy electron microscopy (LEEM), X-ray photoelectron spectroscopy (XPS), near edge X-ray absorption fine structure spectroscopy (NEXAFS) and low energy electron diffraction (LEED) for the comprehensive characterization of the resulting oxide films. O₂-plasma exposure initially induces the growth of 3-dimensional oxide islands surrounded by an O-covered Cu surface. With ongoing plasma exposure, the islands coalesce and form a closed oxide film. Utilizing spectroscopy, we traced the evolution of metallic Cu, Cu₂O and CuO species upon oxygen plasma exposure and found a dependence of the surface structure and chemical state on the substrate''s orientation. On Cu(100) the oxide islands grow with a lower rate than on the (111) surface. Furthermore, while on Cu(100) only Cu₂O is formed during the initial growth phase, both Cu₂O and CuO species are simultaneously generated on Cu(111). Finally, prolonged oxygen plasma exposure results in a sandwiched film structure with CuO at the surface and Cu₂O at the interface to the metallic support. A stable CuO(111) surface orientation is identified in both cases, aligned to the Cu(111) support, but with two coexisting rotational domains on Cu(100). These findings illustrate the possibility of tailoring the oxidation state, structure and morphology of metallic surfaces for a wide range of applications through oxygen plasma treatments.

A low-pressure oxygen plasma oxidized Cu(100) and Cu(111) surfaces at room temperature. The time-dependent evolution of surface structure and chemical composition is reported in detail for a range of exposure times up to 30 min.