The scanning tunneling microscope is proposed as a method to measure forces as small as 10-18 N. As one application for this concept, we introduce a new type of microscope capable of investigating surfaces of insulators on an atomic scale. The atomic force microscope is a combination of the principles of the scanning tunneling microscope and the stylus profilometer. It incorporates a probe that does not damage the surface. Our preliminary results in air demonstrate a lateral resolution of 30 A and a vertical resolution less than 1 A.

https://link.aps.org/pdf/10.1103/PhysRevLett.56.930

Atomic force microscope

/pdf/self-assembled-monolayers-of-thiolates-on-metals-as-a-form-1neb0hj3k4.pdf

Self-assembled monolayers of thiolates on metals as a form of nanotechnology.

In this work we briefly describe the most relevant features of WSXM, a freeware scanning probe microscopy software based on MS-Windows. The article is structured in three different sections: The introduction is a perspective on the importance of software on scanning probe microscopy. The second section is devoted to describe the general structure of the application; in this section the capabilities of WSXM to read third party files are stressed. Finally, a detailed discussion of some relevant procedures of the software is carried out.

https://nanoed.tul.cz/pluginfile.php/1608/mod_resource/content/1/view.pdf

WSXM: a software for scanning probe microscopy and a tool for nanotechnology.

Surface microscopy using vacuum tunneling is demonstrated for the first time. Topographic pictures of surfaces on an atomic scale have been obtained. Examples of resolved monoatomic steps and surface reconstructions are shown for (110) surfaces of CaIrSn 4 and Au.

https://link.aps.org/pdf/10.1103/PhysRevLett.49.57

Surface studies by scanning tunneling microscopy

1. Introduction 2. Theoretical foundations 3. Propagation and focusing of optical fields 4. Spatial resolution and position accuracy 5. Nanoscale optical microscopy 6. Near-field optical probes 7. Probe-sample distance control 8. Light emission and optical interaction in nanoscale environments 9. Quantum emitters 10. Dipole emission near planar interfaces 11. Photonic crystals and resonators 12. Surface plasmons 13. Forces in confined fields 14. Fluctuation-induced phenomena 15. Theoretical methods in nano-optics Appendices Index.

/pdf/principles-of-nano-optics-1hz998yqo8.pdf

Principles of nano-optics

This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has beenissued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, itsdistribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publica-tion, requests should be filled only by reprints or legally obtained copies (e.g., payment of royalties.

Handling Complexity with Self-Organizing Fractial Semantic Networks

Background: Manual evaluation of somatostatin receptor (SSTR) immunohistochemistry (IHC) is a time-consuming and cost-intensive procedure. Aim of the study was to compare manual evaluation of SSTR subtype IHC to an automated software-based analysis, and to in-vivo imaging by SSTR-based PET/CT. Methods: We examined 25 gastroenteropancreatic neuroendocrine tumor (GEP-NET) patients and correlated their in-vivo SSTR-PET/CT data (determined by the standardized uptake values SUVmax,-mean) with the corresponding ex-vivo IHC data of SSTR subtype (1, 2A, 4, 5) expression. Exactly the same lesions were imaged by PET/CT, resected and analyzed by IHC in each patient. After manual evaluation, the IHC slides were digitized and automatically evaluated for SSTR expression by Definiens XD software. A virtual IHC score “BB1” was created for comparing the manual and automated analysis of SSTR expression. Results: BB1 showed a significant correlation with the corresponding conventionally determined Her2/neu score of the SSTR-subtypes 2A (rs: 0.57), 4 (rs: 0.44) and 5 (rs: 0.43). BB1 of SSTR2A also significantly correlated with the SUVmax (rs: 0.41) and the SUVmean (rs: 0.50). Likewise, a significant correlation was seen between the conventionally evaluated SSTR2A status and the SUVmax (rs: 0.42) and SUVmean (rs: 0.62).Conclusion: Our data demonstrate that the evaluation of the SSTR status by automated analysis (BB1 score), using digitized histopathology slides (“virtual microscopy”), corresponds well with the SSTR2A, 4 and 5 expression as determined by conventional manual histopathology. The BB1 score also exhibited a significant association to the SSTR-PET/CT data in accordance with the high affinity profile of the SSTR analogues used for imaging.

Somatostatin receptor immunohistochemistry in neuroendocrine tumors: comparison between manual and automated evaluation.

A method for writing data to and/or reading data from locations on a surface (90) via a tip (40) comprises moving the tip between the locations on the surface. At each location, energy (250) is selectively applied to the surface via the tip and the tip and the surface are selectively forced together in synchronization with the application of energy.

Data read/write systems

PROBLEM TO BE SOLVED: To easily learn and understand knowledge through a knowledge database by processing text information which is transmitted with an input character string together with information included in the knowledge database. SOLUTION: The input character string 12 of a text, a voice, etc., is converted by a semantic preprocessor 17 to a regular input network 18 of meaning units. The semantic preprocessor 17 converts the input character string 12 to the regular input network 18. A semantic processor 19 finds, in the knowledge database, the subset which matches all the semantic units of the input network 18 most. Further, expansion from the knowledge database 11 is performed in relative semantic units. The input network is called a resulting semantic network 13 there. The semantic processor 19 generates a new fractal hierarchic network of semantic units and pointers from the input network 18 and the knowledge data base 11. The resulting semantic network 13 reflects both general meanings of the input character string 12 and individual sides and is represented as the fractal hierarchical network of the semantic units and pointers like the knowledge database 11. COPYRIGHT: (C)2000,JPO

Method and device for processing text information

We report on a new atomic force microscope (AFM)-based data storage concept called the "Millipede" that has a potentially ultrahigh density, terabit capacity, small form factor, and high data rate. Its potential for ultra-high storage density was demonstrated by a new thermo-mechanical local-probe technique to store, read back, and erase data in very thin polymer films.

Gerd Binnig

Papers

Handling Complexity with Self-Organizing Fractial Semantic Networks

Somatostatin receptor immunohistochemistry in neuroendocrine tumors: comparison between manual and automated evaluation.

Data read/write systems

Method and device for processing text information

The "Millipede"-more than one thousand tips for parallel and dense AFM data storage