Inorganic M aterials from Nano to Macro Scales

Understanding structure-proper ty relationships\, as well as the development of materials for target appl ications\, is limited by our ability to control the defects at multi-lengt h scales. One potential solution investigated by the Ibáñez group is thr ough the use of nanoparticle (NP)-based precursors. These undergo predicta ble transformations into polycrystalline solids dictated by the specific\, tunable features of the nanoparticle’s inorganic core\, the surrounding surface species\, and the NP array configuration.
In particular\, the Ibáñez group works on developing new synthetic methods for complex nanoc rystals\, unravels the role of native or post-synthetically added surface species\, and investigates means for their organization. With the idea to carefully engineer the NP-based precursor to produce macroscopic solids wi th targeted features\, the group also explores the chemical transformation s involved in the process.
Broadening the use of NP-based precursors\, the group also aims to provide high-efficiency\, cost-effective thermoelec tric materials. To accelerate the discovery and optimization of thermoelec tric materials\, we built the Werner Siemens Thermoelectric Laboratory\, w here we employ high-throughput methods.

The Werner Siemens Thermoe lectric Laboratory is dedicated to developing high-performance thermoelect ric materials that can efficiently convert heat into electricity and vice versa. Existing materials are often limited by high costs\, toxicity\, and low efficiency. To overcome these challenges\, we employ innovative solut ion-processed methods using nanoparticles\, guided by high-throughput (HTP ) experimentation\, machine learning\, and atomistic simulations. Our prim ary objective is to create cost-effective thermoelectric materials for a w ide range of applications\, including the Internet of Things (IoT)\, wirel ess sensors\, and waste heat recovery.

Our approach is unique in t hat it explores a high-dimensional search space of material possibilities\ , accelerating the discovery process far beyond conventional methods. By i ntegrating computational predictions and data-driven techniques\, we aim t o uncover materials with unprecedented performance.