@article{SchoeningSimonisRugeetal.2002,
  author    = {Sch{\"o}ning, Michael Josef and Simonis, Anette and Ruge, Christian and Ecken, Holger and M{\"u}ller-Veggian, Mattea and L{\"u}th, Hans},
  title     = {A (bio-)chemical Field-effect Sensor with Macroporous Si as Substrate Material and a SiO₂ / LPCVD-Si₃N₄ Double Layer as pH Transducer},
  series = {Sensors. 2 (2002), H. 1},
  journal   = {Sensors. 2 (2002), H. 1},
  isbn      = {1424-8220},
  doi       = {10.3390/s20100011},
  pages     = {11 -- 22},
  year      = {2002},
  abstract  = {Macroporous silicon has been etched from n-type Si, using a vertical etching cell where no rear side contact on the silicon wafer is necessary. The resulting macropores have been characterised by means of Scanning Electron Microscopy (SEM). After etching, SiO₂ was thermally grown on the top of the porous silicon as an insulating layer and Si₃N₄ was deposited by means of Low Pressure Chemical Vapour Deposition (LPCVD) as transducer material to fabricate a capacitive pH sensor. In order to prepare porous biosensors, the enzyme penicillinase has been additionally immobilised inside the porous structure. Electrochemical measurements of the pH sensor and the biosensor with an Electrolyte/Insulator/Semiconductor (EIS) structure have been performed in the Capacitance/Voltage (C/V) and Constant capacitance (ConCap) mode.},
  language  = {en}
}
@incollection{WendorffEggertPohletal.2007,
  author    = {Wendorff, Marion and Eggert, Thorsten and Pohl, Martina and Dresen, Carola and M{\"u}ller, Michael and Jaeger, Karl-Erich and Sprenger, Georg A. and Sch{\"u}rmann, Melanie and Sch{\"u}rmann, Martin and Johnen, Sandra and Sprenger, Gerda and Sahm, Hermann and Inoue, Tomoyuki and Sch{\"o}rken, Ulrich and Breittaupt, Holger and Fr{\"o}lich, Bettina and Heim, Petra and Iding, Hans and Juchem, Bettina and Siegert, Petra and Kula, Maria-Regina and Weckbecker, Andrea and Hummel, Werner and Fessner, Wolf-Dieter and Elling, Lothar and Wolberg, Michael and Bode, Silke and Feldmann, Ralf and Geilenkirchen, Petra and Schubert, Thomas and Walter, Lydia and D{\"u}nnwald, Thomas and Demir, Ayhan S. and Kolter-Jung, Doris and Nitsche, Adam and D{\"u}nkelmann, Pascal and Cosp, Annabel and Lingen, Bettina},
  title     = {Catalytic asymmetric synthesis : section 2.2},
  series = {Asymmetric synthesis with chemical and biological methods / ed. by Dieter Enders ...},
  booktitle = {Asymmetric synthesis with chemical and biological methods / ed. by Dieter Enders ...},
  publisher = {Wiley-VCH},
  address   = {Weinheim},
  isbn      = {978-3-527-31473-7},
  pages     = {298 -- 413},
  year      = {2007},
  language  = {en}
}
@article{JablonowskiKollmannNabeletal.2016,
  author    = {Jablonowski, Nicolai David and Kollmann, Tobias and Nabel, Moritz and Damm, Tatjana and Klose, Holger and M{\"u}ller, Michael and Bl{\"a}sing, Marc and Seebold, S{\"o}ren and Krafft, Simone and Kuperjans, Isabel and Dahmen, Markus and Schurr, Ulrich},
  title     = {Valorization of Sida (Sida hermaphrodita) biomass for multiple energy purposes},
  series = {GCB [Global Change Biology] Bioenergy},
  volume    = {9},
  journal   = {GCB [Global Change Biology] Bioenergy},
  number    = {1},
  publisher = {Wiley-VCH},
  address   = {Weinheim},
  issn      = {1757-1707 (online)},
  doi       = {10.1111/gcbb.12346},
  pages     = {202 -- 214},
  year      = {2016},
  abstract  = {The performance and biomass yield of the perennial energy plant Sida hermaphrodita (hereafter referred to as Sida) as a feedstock for biogas and solid fuel was evaluated throughout one entire growing period at agricultural field conditions. A Sida plant development code was established to allow comparison of the plant growth stages and biomass composition. Four scenarios were evaluated to determine the use of Sida biomass with regard to plant development and harvest time: (i) one harvest for solid fuel only; (ii) one harvest for biogas production only; (iii) one harvest for biogas production, followed by a harvest of the regrown biomass for solid fuel; and (iv) two consecutive harvests for biogas production. To determine Sida's value as a feedstock for combustion, we assessed the caloric value, the ash quality, and melting point with regard to DIN EN ISO norms. The results showed highest total dry biomass yields of max. 25 t ha⁻¹, whereas the highest dry matter of 70\% to 80\% was obtained at the end of the growing period. Scenario (i) clearly indicated the highest energy recovery, accounting for 439 288 MJ ha⁻¹; the energy recovery of the four scenarios from highest to lowest followed this order: (i) ≫ (iii) ≫ (iv) > (ii). Analysis of the Sida ashes showed a high melting point of >1500 °C, associated with a net calorific value of 16.5-17.2 MJ kg⁻¹. All prerequisites for DIN EN ISO norms were achieved, indicating Sida's advantage as a solid energy carrier without any post-treatment after harvesting. Cell wall analysis of the stems showed a constant lignin content after sampling week 16 (July), whereas cellulose had already reached a plateau in sampling week 4 (April). The results highlight Sida as a promising woody, perennial plant, providing biomass for flexible and multipurpose energy applications.},
  language  = {en}
}
@inproceedings{WalterElsenMuelleretal.1999,
  author    = {Walter, Peter and Elsen, Ingo and M{\"u}ller, Holger and Kraiss, Karl-Friedrich},
  title     = {3D object recognition with a specialized mixtures of experts architecture},
  series = {IJCNN'99. International Joint Conference on Neural Networks. Proceedings},
  booktitle = {IJCNN'99. International Joint Conference on Neural Networks. Proceedings},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {0-7803-5529-6},
  issn      = {1098-7576},
  doi       = {10.1109/IJCNN.1999.836243},
  pages     = {3563 -- 3568},
  year      = {1999},
  abstract  = {Aim of the AXON2 project (Adaptive Expert System for Object Recogniton using Neuml Networks) is the development of an object recognition system (ORS) capable of recognizing isolated 3d objects from arbitrary views. Commonly, classification is based on a single feature extracted from the original image. Here we present an architecture adapted from the Mixtures of Eaqerts algorithm which uses multiple neuml networks to integmte different features. During tmining each neural network specializes in a subset of objects or object views appropriate to the properties of the corresponding feature space. In recognition mode the system dynamically chooses the most relevant features and combines them with maximum eficiency. The remaining less relevant features arz not computed and do therefore not decelerate the-recognition process. Thus, the algorithm is well suited for ml-time applications.},
  language  = {en}
}