We present a simple model of charge transfer current through sensitizer N3 molecule contact to TiO2 and ZnO semiconductors to calculate the charge transfer current. The model underlying depends on the fundamental parameters of the charge transfer reaction and it is based on the quantum transition theory approach. A transition energy, driving energy and potential barrier have been taken into account charge transfer current at N3 / TiO2 and N3 / ZnO devices with wide polarity solvents Acetic acid, 2-Methoxyethanol, 1-Butanol, Methyl alcohol, chloroform, N,N-Dimethylacetamide and Ethyl alcohol via the quantum donor-acceptor system.The effects of the transition energy and potential barrier are computed and discussion on charge transfer current.

The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of

The Schiff base (E)-2-(((2-(1H-benzo[d]imidazol-2-yl) phenyl) imino) methyl)-4-methylphenol (Lb) ligand with some metals(II) ion such as; Co, Cu, Cd, and Hg, were synthesis and characterized by the mass and 1 HNMR spectrometry for ligand Schiff base, the fourier-transform infrared spectroscop (FTIR), UV- visible and the flame atomic absorption (AA) spectrum, the CHN analysis, and the chlorine content, in addition to measuring the magnetic sensitivity of the complexes. All the complexes had octahedral geometry. The bioactivity activity for compounds against; Rhizopodium, Staphylococcus aureus, and Escherichia coli showed different efficacy towards these microorganisms

(3) (PDF) Theoretical investigation of charge transfer at N3 sensitized molecule dye contact with TiO2 and ZnO semiconductor. Available from: https://www.researchgate.net/publication/362773606_Theoretical_investigation_of_charge_transfer_at_N3_sensitized_molecule_dye_contact_with_TiO2_and_ZnO_semiconductor [accessed May 01 2023].

The current study included the separation of three alkaloid compounds from Anastatica Hierochuntica and studied the effect of the these compounds on cancerous cells , specifically liver cancer it was found that compound number one is the most influential or inhibiting at 50 percent followed by compound number three when using concentration of 400 μg/mL.

New bidentate dithiocarbamate ligand (NaL) namely [Sodium-2-(((3-methyl -4- “(2,2,2-tri fluoro ethoxy) pyridin-2”-yl) methyl) sulfinyl)-1H-benzoimidazole -1-carbodithioate] was prepared. This free ligand was synthesized from the reaction of a (RS)-2-([3-methyl -4-(2,2,2-tri fluoroethoxy) pyridin-2-yl] methyl sulfinyl)-1H benzoimidazole, CS2 and NaOH in methanol as solvent. From reaction of dithiocarbamate salt (NaL) with metal ions (M); Co(II), Ni(II), Cu(II), Zn(II), Cd(II) and Pd(II)”, have obtained the DTC complexes at general molecular formula [M(L)2(H2O)2] and [Pd(L)2]. To characterize the ligand and its complexes, used different analyses methods such FTIR, UV-Vis, elemental microanalysis, atomic absoreption, magnetic susceptibil

Upper limb amputation is a condition that severely limits the amputee’s movement. Patients who have lost the use of one or more of their upper extremities have difficulty performing activities of daily living. To help improve the control of upper limb prosthesis with pattern recognition, non-invasive approaches (EEG and EMG signals) is proposed in this paper and are integrated with machine learning techniques to recognize the upper-limb motions of subjects. EMG and EEG signals are combined, and five features are utilized to classify seven hand movements such as (wrist flexion (WF), outward part of the wrist (WE), hand open (HO), hand close (HC), pronation (PRO), supination (SUP), and rest (RST)). Experiments demonstrate that usin

The turning process has various factors, which affecting machinability and should be investigated. These are surface roughness, tool life, power consumption, cutting temperature, machining force components, tool wear, and chip thickness ratio. These factors made the process nonlinear and complicated. This work aims to build neural network models to correlate the cutting parameters, namely cutting speed, depth of cut and feed rate, to the machining force and chip thickness ratio. The turning process was performed on high strength aluminum alloy 7075-T6. Three radial basis neural networks are constructed for cutting force, passive force, and feed force. In addition, a radial basis network is constructed to model the chip thickness ratio. T