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| 1 | Effect of position and force tool control in friction stir welding of dissimilar aluminum-steel lap joints for automotive applications显示文摘Widespread use of aluminum alloys for the fabrication of car body parts is conditional to the use of appropriate welding methods,especially if dissimilar welding must be performed with automotive steel grades.Friction stir welding(FSW)is considered to be a reasonable solution to obtain sound aluminum-steel joints.In this context,this work studies the effects of tool position and force control in dissimilar friction stir welding of AA6061 aluminum alloy on DC05 low carbon steel in lap joint configuration,also assessing proper welding parameter settings.Naked eye and scanning electron microscopy(SEM)have been used to detect macroscopic and microscopic defects in joints,as well as to determine the type of intermixture between aluminum and steel.The joint strength of sound joints has been assessed by shear tension test.Results point out that tool force control allows for obtaining joints with better quality and strength in a wider range of process parameters.A process window has been determined for tool force conditions to have joints with adequate strength for automotive purposes. | M.Wasif Safeen P.Russo Spena G.Buffa D.Campanella A.Masnata L.Fratini | 2020 | Advances in Manufacturing2020，8，1： | 2 |
| 2 | Machine learning property prediction for organic photovoltaic devices显示文摘Organic photovoltaic(OPV)materials are promising candidates for cheap,printable solar cells.However,there are a very large number of potential donors and acceptors,making selection of the best materials difficult.Here,we show that machine-learning approaches can leverage computationally expensive DFT calculations to estimate important OPV materials properties quickly and accurately.We generate quantitative relationships between simple and interpretable chemical signature and one-hot descriptors and OPV power conversion efficiency(PCE),open circuit potential(Voc),short circuit density(Jsc),highest occupied molecular orbital(HOMO)energy,lowest unoccupied molecular orbital(LUMO)energy,and the HOMO–LUMO gap.The most robust and predictive models could predict PCE(computed by DFT)with a standard error of±0.5 for percentage PCE for both the training and test set.This model is useful for pre-screening potential donor and acceptor materials for OPV applications,accelerating design of these devices for green energy applications. | Nastaran Meftahi Mykhailo Klymenko Andrew J.Christofferson Udo Bach David A.Winkler Salvy P.Russo | 2020 | npj Computational Materials2020，，1： | 2 |
| 3 | Hepatitis C virus: from oxygen free radicals to hepatocellular carcinoma显示文摘 | F.Farinati R.Cardin M.Bortolami P.Burra F. P.Russo M.Rugge M.Guido A.Sergio R.Naccarato | 2007 | Journal of Viral Hepatitis2007，，12： | 1 |
| 4 | TOPICAL CARTEOLOL WITH AND WITH-OUT BENZALKONIUM CHLORIDE: EF-FECT ON THE OCULAR SURFACE AND INTRAOCULAR PRESSURE显示文摘Purpose: Long term use of antiglaucoma medication may induce changes in both tear film and ocular surface. The purpose of this study was to evaluate the effect on ocular surface of carteolol with and without 0.004% benzalkonium chloride (BAC). Methods: Forty-three patients (age 61 ±11, m±SD) with primary open angle glaucoma (POAG) or ocular hypertension (OHT) | V.Papa A.Scuderi P.Russo G.Milazzo | 2003 | 国际眼科杂志2003，3，1： | 0 |
| 5 | OCULAR SURFACE CHANGES INDUCED BY CARTEOLOL AND TEMOLOL显示文摘Purpose: Antiglaucoma drugs may induce ocular surface changes due to the presence of benzalkonium chloride (BAC). The purpose of this study was to compare the effect on ocular surface of carteolol and timolol preserved with a different concentration of BAC (0.004% and 0.01%, respectively). Methods: | G.Milazzo B.Carstocea O.Gafencu M.Armengioiu P.Russo V.Pa-pa | 2003 | 国际眼科杂志2003，3，1： | 0 |
| 6 | Machine learning-based discovery of vibrationally stable materials显示文摘The identification of the ground state phases of a chemical space in the convex hull analysis is a key determinant of the synthesizability of materials.Online material databases have been instrumental in exploring one aspect of the synthesizability of many materials,namely thermodynamic stability.However,the vibrational stability,which is another aspect of synthesizability,of new materials is not known.Applying first principles approaches to calculate the vibrational spectra of materials in online material databases is computationally intractable.Here,a dataset of vibrational stability for~3100 materials is used to train a machine learning classifier that can accurately distinguish between vibrationally stable and unstable materials.This classifier has the potential to be further developed as an essential filtering tool for online material databases that can inform the material science community of the vibrational stability or instability of the materials queried in convex hulls. | Sherif Abdulkader Tawfik Mahad Rashid Sunil Gupta Salvy P.Russo Tiffany R.Walsh Svetha Venkatesh | 2023 | npj Computational Materials2023，，1： | 0 |
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