| 1 |
FibeR-CNN: Expanding Mask R-CNN to improve image-based fiber analysis
Frei M, Kruis FE
Powder Technology, 377, 974, 2021 |
| 2 |
Original Paper Stochastic modeling of classifying aerodynamic lenses for separation of airborne particles by material and size
Furat O, Masuhr M, Kruis FE, Schmidt V
Advanced Powder Technology, 31(6), 2215, 2020 |
| 3 |
Aerosol synthesis of titanium nitride nanoparticles by direct current arc discharge method
Fu QQ, Kokalj D, Stangier D, Kruis FE, Tillmann W
Advanced Powder Technology, 31(9), 4119, 2020 |
| 4 |
Image-based size analysis of agglomerated and partially sintered particles via convolutional neural networks
Frei M, Kruis FE
Powder Technology, 360, 324, 2020 |
| 5 |
Scaling-up metal nanoparticle production by transferred arc discharge
Stein M, Kruis FE
Advanced Powder Technology, 29(12), 3138, 2018 |
| 6 |
Fractional Monte Carlo time steps for the simulation of coagulation for parallelized flowsheet simulations
Kotalczyk G, Kruis FE
Chemical Engineering Research & Design, 136, 71, 2018 |
| 7 |
Ejector-based sampling from low-pressure aerosol reactors
Rosenberger T, Munzer A, Kiesler D, Wiggers H, Kruis FE
Journal of Aerosol Science, 123, 105, 2018 |
| 8 |
Fully automated primary particle size analysis of agglomerates on transmission electron microscopy images via artificial neural networks
Frei M, Kruis FE
Powder Technology, 332, 120, 2018 |
| 9 |
A time-driven constant-number Monte Carlo method for the GPU-simulation of particle breakage based on weighted simulation particles
Kotalczyk G, Devi J, Kruis FE
Powder Technology, 317, 417, 2017 |
| 10 |
Dependence of Steady-State Compositional Mixing Degree on Feeding Conditions in Two-Component Aggregation
Zhao HB, Kruis FE
Industrial & Engineering Chemistry Research, 53(14), 6047, 2014 |
