Polyfluorothiolate derivatives of rhodium(I). Crystal and molecular structure of [Rh(μ-SC6HF4)(C8H12)]2

Summary The thiolato-bridged dinuclear compounds [Rh(-SR)-(COD)]₂, where R=p-C₆HF₄ (1),p-C₆H₄F (2) and CF₃ (3), are obtained from the chloro-bridged analogue by ligand exchange.Compound (1) crystallizes in the space group P₁ with a=9.740(3)Å, b=11.642(4)Å, c=13.997(6)Å, =103.87(3)°, =106.98(3)° and =105.10(2)°; z=2. In this dinuclear molecule both Rh atoms have a square planar coordination sharing one edge, namely the two sulphur bridging atoms. The Rh—Rh separation of 2.96 Å is consistent with at most a very weak metal-metal interaction. Upon addition of CO the dimeric [Rh(-SR)(CO)₂]₂ (4), (5) and (6) are obtained, but addition of PPh₃ affords the monomeric species [Rh(SR)(PPh₃)-(COD)] (7), (8) and (9). Reactions of the dimeric tetracarbonyl derivatives with PPh₃ vary with the nature of R; [Rh(-SR)(PPh₃)(CO)]₂ is obtained when R=p-C₆H₄F (10) and CF₃ (11) but monomeric [Rh(SR)-(PPh₃)(CO)₂] (12) is produced when R=p-C₆HF₄. The latter mononuclear compounds, with R=p-C₆H₄F (13) and CF₃ (14), are also formed by reaction of [Rh(SR)-(PPh₃)(COD)] with CO.     

De Rham systems and the solution of a class of functional equations

The main objective of this paper is to solve the functional equation

Computing the Kekulé structure count for alternant hydrocarbons

A fast computer algorithm brings computation of the permanents of sparse matrices, specifically, molecular adjacency matrices. Examples and results are presented, along with a discussion of the relationship of the permanent to the Kekulé structure count. A simple method is presented for determining the Kekulé structure count of alternant hydrocarbons. For these hydrocarbons, the square of the Kekulé structure count is equal to the permanent of the adjacency matrix. In addition, for alternant structures the adjacency matrix for N atoms can be written in such a way that only an N/2 × N/2 matrix need be evaluated. The Kekulé structure count correlates with topological indices. The inclusion of the number of cycles improves the fit. When comparing with previous results, the variance decreases 74%. The calculated standard heat of formation correlates with the logarithm of the Kekulé structure count. This heat increments 349 kJ/mol each time the Kekulé structure count increases by one order of magnitude. © 2002 Wiley Periodicals, Inc. Int J Quantum Chem, 2002     

Insertion reactions of [M(SR)3(PMe2Ph)2] with CS2 (M = Ru, Os; R = C6F4H-4, C6F5). X-ray structures of [Ru(S2CSC6F4H-4)2(PMe2Ph)2], trans-thiolates [M(SR)2(S2CSR)(PMe2Ph)2] (M = Ru; R = C6F5 and M = Os; R = C6F4H-4), and trans-thiolate-phosphine [Os(SC6F5)2(S2CSC6F5)(PMe2Ph)2

Reactions of [M(SR)(3)(PMe(2)Ph)(2)] (M = Ru, Os; R = C(6)F(4)H-4, C(6)F(5)) with CS(2) in acetone afford [Ru(S(2)CSR)(2)(PMe(2)Ph)(2)] (R = C(6)F(4)H-4, 1; C(6)F(5), 3) and trans-thiolates [Ru(SR)(2)(S(2)CSR)(PMe(2)Ph)(2)] (R = C(6)F(4)H-4, 2; C(6)F(5), 4) or the isomers trans-thiolates [Os(SR)(2)(S(2)CSR)(PMe(2)Ph)(2)] (R = C(6)F(4)H-4, 5; C(6)F(5), 7) and trans-thiolate-phosphine [Os(SR)(2)(S(2)CSR)(PMe(2)Ph)(2)] (R = C(6)F(4)H-4, 6; C(6)F(5), 8) through processes involving CS(2) insertion into M-SR bonds. The ruthenium(III) complexes [Ru(SR)(3)(PMe(2)Ph)(2)] react with CS(2) to give the diamagnetic thiolate-thioxanthato ruthenium(II) and the paramagnetic ruthenium(III) complexes while osmium(III) complexes [Os(SR)(3)(PMe(2)Ph)(2)] react to give the paramagnetic thiolate-thioxanthato osmium(III) isomers. The single-crystal X-ray diffraction studies of 1, 4, 5, and 8 show distorted octahedral structures. (31)P [(1)H] and (19)F NMR studies show that the solution structures of 1 and 3 are consistent with the solid-state structure of 1.     

A Banking Platform to Leverage Data Driven Marketing with Machine Learning

Payment data is one of the most valuable assets that retail banks can leverage as the major competitive advantage with respect to new entrants such as Fintech companies or giant internet companies. In marketing, the value behind data relates to the power of encoding customer preferences: the better you know your customer, the better your marketing strategy. In this paper, we present a B2B2C lead generation application based on payment transaction data within the online banking system. In this approach, the bank is an intermediary between its private customers and merchants. The bank uses its competence in Machine Learning driven marketing to build a lead generation application that helps merchants run data driven campaigns through the banking channels to reach retail customers. The bank’s retail customers trade the utility hidden in its payment transaction data for special offers and discounts offered by merchants. During the entire process banks protects the privacy of the retail customer.