Structure and superconductivity of tin-containing hftizrsnm (M = cu, fe, nb, ni) medium-entropy and high-entropy alloys

In an attempt to incorporate tin (Sn) into high-entropy alloys composed of refractory metals Hf, Nb, Ti and Zr with the addition of 3d transition metals Cu, Fe, and Ni, we synthesized a series of alloys in the system HfTiZrSnM (M = Cu, Fe, Nb, Ni). The alloys were characterized crystallographically, microstructurally, and compositionally, and their physical properties were determined, with the emphasis on superconductivity. All Sn-containing alloys are multi-phase mixtures of intermetallic compounds (in most cases four). A common feature of the alloys is a microstructure of large crystalline grains of a hexagonal (Hf, Ti, Zr)5Sn3 partially ordered phase embedded in a matrix that also contains many small inclusions. In the HfTiZrSnCu alloy, some Cu is also incorporated into the grains. Based on the electrical resistivity, specific heat, and magnetization measurements, a superconducting (SC) state was observed in the HfTiZr, HfTiZrSn, HfTiZrSnNi, and HfTiZrSnNb alloys. The HfTiZrSnFe alloy shows a partial SC transition, whereas the HfTiZrSnCu alloy is non-superconducting. All SC alloys are type II superconductors and belong to the Anderson class of “dirty” superconductors

Vrstična tunelska spektroskopija koreliranih elektronskih sistemov in molekulskih struktur

In this thesis, Scanning tunneling microscopy (STM) and spectroscopy were used to study the topography, electronic properties, and band structure of the surfaces of complex correlated electron materials and self-assembled molecular structures. First, the setpoint artifacts that can occur when acquiring band structure data using quasi-particle interference (QPI) are studied, applied to the surface states of (111) metal surfaces. Next, QPI is applied to study the topological semi-metal Sb(111), enabling the reconstruction of its band structure. The second part of the thesis concerned materials exhibiting charge density waves (CDW). Combining STM imaging with Fourier transform analysis along the columns of the quasi-one-dimensional material NbSe$_3$, the real-space distribution of the two modulation wave-vectors is studied. Our measurements support a new model of CDW sliding, in which the two modulations alternate along two of the three types of columns comprising the unit cell. Measurements of the topography and electronic properties of the layered material 1T-TaSeS, exhibiting both CDWs and superconductivity, are presented. In contrast to expectations, high energy resolution measurements below its superconducting transition temperature reveal no spatial correlation between the observed superconducting gap and the domain wall structure. In the end, the self-assembly of 2-mercaptobenzimidazole, an organic corrosion inhibitor, on the surface of copper Cu(111) is investigated. Evaporation in UHV enabled controlled preparation of high-quality samples with (sub-)monolayer coverages. By tuning the substrate temperature and surface coverage, different self-assembled structures could be obtained and characterized using STM and DFT, elucidating the bonding of 2-mercaptobenzimidazole on Cu(111). Atomic real-space and high energy resolution in combination with a detailed understanding of the measuring modes and possible artifacts, make STM a powerful experimental technique, providing several possibilities for investigating the surfaces of complex materials.V disertaciji so predstavljeni rezultati uporabe vrstične tunelske mikroskopije (VTM) in spektroskopije za preučevanje topografije ter elektronskih lastnosti koreliranih elektronskih materialov in samourejenih molekulskih struktur na površinah. Najprej smo se osredotočili na karakterizacijo merilnih napak, ki se lahko pojavijo pri merjenju pasovne strukture površin s pomočjo interference kvazi delcev na primeru površinskega stanja enostavnih kovin. Nato smo s pomočjo te metode rekonstruirali pasovno strukturo topološke polkovine Sb(111). V drugem delu doktorata smo se osredotočili na preučevanje materialov, ki kažejo valove gostote naboja (VGN). Z uporabo VTM in analize s pomočjo Fourierjeve transformacije vzdolž kolon kvazi-enodimenzionalnih kristalov NbSe$_3$ smo preučevali urejanje VGN v tem kristalu, ki kaže zanimive transportne lastnosti imenovane drsenje VGN. Naši rezultati so v skladu z novim modelom transporta, ki predvideva izmenjavo obeh modulacij vzdolž dveh od treh kolon. Na primeru plastnih kristalov 1T-TaSeS smo preučevali soobstoj superprevodnosti in VGN. Prek visoko ločljivih meritev lokalne gostote stanj pod temperaturo prehoda v superprevodno stanje, smo pokazali, da v nasprotju s pričakovanji superprevodne lastnosti in domenska struktura tega materiala nista povezani. V zadnjem delu doktorata so predstavljeni rezultati meritev samourejanja organskega inhibitorja, 2-merkaptobenzimidazola, na površini bakra. S pomočjo naparevanja v ultra visokem vakuumu smo nadzorovano pripravili visoko kvalitetne vzorce s (pod-)enoplastno pokritostjo. Z nadzorom nad temperaturo substrata in površinsko pokritostjo smo pridobili različne samourejene strukture in jih analizirali z VTM ter simulacijami s pomočjo teorije gostotnih funkcionalov in tako izboljšali razumevanje mehanizmov vezave 2-merkaptobenzimidazola na površino bakra. Zaradi atomske in visoke energijske ločljivosti je VTM skupaj s podrobnim poznavanjem merilnih načinov in možnih napak močna eksperimentalna tehnika, ki omogoča raznolike raziskave površin kompleksnih materialov

Chemical selectivity and sensitivity of a 16-channel electronic nose for trace vapour detection

Good chemical selectivity of sensors for detecting vapour traces of targeted molecules is vital to reliable detection systems for explosives and other harmful materials. We present the design, construction and measurements of the electronic response of a 16 channel electronic nose based on 16 differential microcapacitors, which were surface-functionalized by different silanes. The e-nose detects less than 1 molecule of TNT out of 10$^{+12}$ N$_2$ molecules in a carrier gas in 1 s. Differently silanized sensors give different responses to different molecules. Electronic responses are presented for TNT, RDX, DNT, H$_2$S, HCN, FeS, NH$_3$, propane, methanol, acetone, ethanol, methane, toluene and water. We consider the number density of these molecules and find that silane surfaces show extreme affinity for attracting molecules of TNT, DNT and RDX. The probability to bind these molecules and form a surface-adsorbate is typically 10$^{+7}$ times larger than the probability to bind water molecules, for example. We present a matrix of responses of differently functionalized microcapacitors and we propose that chemical selectivity of multichannel e-nose could be enhanced by using artificial intelligence deep learning methods

Improving the chemical selectivity of an electronic nose to TNT, DNT and RDX using machine learning

We used a 16-channel e-nose demonstrator based on micro-capacitive sensors with functionalized surfaces to measure the response of 30 different sensors to the vapours from 11 different substances, including the explosives 1,3,5-trinitro-1,3,5-triazinane (RDX), 1-methyl-2,4-dinitrobenzene (DNT) and 2-methyl-1,3,5-trinitrobenzene (TNT). A classification model was developed using the Random Forest machine-learning algorithm and trained the models on a set of signals, where the concentration and flow of a selected single vapour were varied independently. It is demonstrated that our classification models are successful in recognizing the signal pattern of different sets of substances. An excellent accuracy of 96% was achieved for identifying the explosives from among the other substances. These experiments clearly demonstrate that the silane monolayers used in our sensors as receptor layers are particularly well suited to selecting and recognizing TNT and similar types of explosives from among other substances