Leonardo Gabriel

Vega Macotela

Tetyana Torchynska

Larysa Khomenkova

Fabrice Gourbilleau

Light emission and structure of Nd-doped Si-rich-HfO 2 films prepared by magnetron sputtering in different atmospheres

A B S T R A C T

Radio-frequency magnetron sputtering was used to produce HfO 2 films doped with Nd, Si and N. The deposition was carried out in two different atmospheres: i) in pure argon plasma to grow Si-HfO 2 :Nd films, and ii) in argonnitrogen mixed plasma to produce Si-N-HfO 2 :Nd films. The effect of annealing temperature on optical and structural properties of the films was investigated. Annealing was performed at T A = 800-1100 °C for t A = 15 min in nitrogen atmosphere. The evolution of film's properties was studied by means of the scanning electronic microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD) and photoluminescence (PL) methods. It was observed that the film morphology depends significantly on the deposition atmosphere. For the Si-HfO 2 :Nd films, the presence of the grains with the mean size about 100 nm was detected by the SEM method. The tetragonal HfO 2 and SiO 2 phases have been detected by the XRD method after film annealing at 950 °C. PL spectra of these films are complex and demonstrate several PL bands in the visible (400-750 nm) and infrared (800-1430 nm) spectral ranges. Their contribution depends on the annealing temperature and governs the shape of total PL spectrum. In contrary, the Si-N-HfO 2 :Nd films showed unstructured smooth surface as well as featureless PL spectra. Whatever the annealing temperature, they demonstrate broad unstructured PL band with the peak within 440-480 nm. Peculiarities of PL spectra of both types of the films and the mechanism of phase separation are analyzed and discussed.

Introduction

HfO 2 -based materials during the last decades are mainly considered as the alternative gate dielectrics to silicon oxide in complementary metal-oxide semiconductor technology. At the same time, their high refractive index (n = 1.9-2.2, depending on preparation method), high optical transparency in the ultraviolet-infrared spectral range (band gap is about 5.8-6.0 eV), compactness and hardness offer optical applications as well. The phonon cut-off energy (∼about 780 cm -1 ) reduces the probability of non-radiative phonon assisted relaxation that is attractive for doping these materials in particular with the rare-earth elements [1][2][3]. However, in spite of mentioned advantages, the HfO 2based compounds up to now are not often addressed as a host for the rare-earth ions. Just a few groups reported on the light emission obtained from Eu 3+ -doped HfO 2 -based nanotubes [4], Er 3+ -doped sol-gel SiO 2 -HfO 2 waveguides [5][6][7], as well as Er 3+ or Pr 3+ doped HfO 2 -based thin films [8,9].

Among different rare-earth ions, the trivalent neodymium (Nd 3+ ) was used in the inorganic laser materials attracting the great attention of scientific researches and industrial applications. Nd 3+ ions exhibit broad and strong absorption band around 800 nm and a very intense emission in the near-infrared luminescence range from 800 to 1430 nm associated with the 4 F 3/2 → 4 I J (J = 9/2, 11/2, 13/2) optical transitions in the 4f inner electronic shell of Nd 3+ ions, following a known "fourlevel" scheme [10,11]. The 4 F 3/2 emitting state can be populated conveniently by the emission of low-cost commercially available laser diodes. In addition, it has many intense pumping levels and efficient upconverted emissions [11]. Among different host matrix materials, the garnets [10], glasses [12], ceramics [13], II-VI compounds [14], as well as Si-rich SiO 2 or Si 3 N 4 are the most studied [15,16].

In this work we present the results of the structural and spectroscopic study of HfO 2 -based thin films co-doped with Si, N and Nd grown by radio frequency (RF) magnetron sputtering. The effect of annealing temperature on the phase separation process and luminescence of such composite films is considered as a fruitful way for monitoring the properties of these materials.

Experimental details

The films were grown by RF magnetron sputtering on 2-inch, Bdoped, (100) oriented Si wafers with a resistivity of about 15 Ω cm. Prior to the deposition, the substrates were submitted to standard RCA cleaning: dipped in a diluted hydrofluoric solution (10%), dried in N 2 , and immediately transferred to the load-lock vacuum chamber of the deposition setup. A pure HfO 2 target (99.9%) topped with the calibrated Si and Nd 2 O 3 pellets was used. The Si and Nd 2 O 3 pellets covered about 16% of target surface each.

To produce the Si-HfO 2 :Nd films, the deposition was performed in pure Ar plasma with argon flow f Ar = 3sccm (so-called "standard" approach). The deposition time was 200 min allowed to grow homogeneous film with the thickness of about 410 nm.

The Si-N-HfO 2 :Nd films were produced with Ar-N 2 mixed plasma (f Ar = 3 sccm and f N2 = 2 sccm; so-called "reactive" approach). The deposition time was 240 min resulting in the film thickness of about 260 nm.

All other deposition conditions were similar for both approaches, i.e. the RF power density, substrate temperature (T S ), total plasma pressure and substrate-cathode distance were 0.74 W/cm 2 , 400 °C, 0.03 mbar and 57 mm, respectively. After the film deposition, each substrate was cut onto the smaller pieces (usually 1 × 1 cm 2 ) to study the effect of annealing treatment. It was performed in a conventional horizontal furnace at different temperatures T A = 800-1100 °C during t A = 15 min in a continuous nitrogen flow (f N2 = 48 sccm).

Several experimental techniques were used to analyze the properties of the films. The PL emission was excited with a 325 nm line of He-Cd laser or by different lines of Ar + -ion laser. PL spectra were recorded using several setups: i) a Jobin-Yvon TRIAX 180 monochromator linked with a fast Hamamatsu photomultiplier and a SRS lock-in amplifier (SP830 DPS) (for 400-1000 nm spectral range); ii) with a Jobin Yvon 1 m single grating monochromator coupled to a Northcoast Germanium detector cooled with liquid nitrogen and a SRS lock-in amplifier (SP830 DPS) (for 800-1700 nm spectral range). Referenced chopping frequency was 20 Hz.

To study the film surface morphology as well as to get the information on their chemical composition, a scanning electronic microscope (SEM) Quanta 3D FEG-FEI with an additional detector Apollo X10 mark EDAX for the energy dispersive X-ray spectroscopy (EDS) was used.

X-ray diffraction (XRD) results were obtained using the equipment of Model X " PERT MRD with the Pixel detector, three-axis goniometry and parallel collimator with the angular resolution of 0.0001°. X-ray beam was achieved from the Cu source (K α1 line, λ = 1.5406 Å). All experiments were carried out at the room temperature.

Results and discussion

Chemical composition of the films

The chemical composition of the films was analyzed by energydispersive X-ray spectroscopy (EDS). The representative spectra for asdeposited films are shown in Fig. 1. As one can see, for all films, the presence of Hf, Si, O and Nd elements was registered. In addition, for the films deposited in argon-nitrogen plasma, the significant signal from the nitrogen was also detected (Fig. 1a, curve 2). Note that the carbon signal can be due to the carbon adsorption on the film surface from an ambient atmosphere.

The annealing treatment results in the variation of chemical composition of the samples that is more prominent for the Si-N-HfO 2 :Nd films. It turned out that the nitrogen-related signal is the most affected. Its intensity changes slightly for T A < 950 °C (not shown) and decreases significantly for higher temperatures (T A = 1000-1100 °C) (Fig. 1b). This effect can be explained taking into account the results of Ref. [17]. The authors demonstrated that rapid (60 s) thermal annealing at 1000 °C in nitrogen atmosphere of HfSiON thin films causes the O and N atomic transport and exchange of the chemical species resulting either in the Si-N or Si-O-N bond formation, as well as in the NO formation and its out-diffusion from the samples. Considering the annealing conditions used for our Si-N-HfO 2 :Nd films, one can assume that the similar processes can occur in our films. Since the N-related EDS signal decreases in intensity, one can conclude that NO formation is the main process for the N desorption from the films. It should be noted that EDS spectra of annealed Si-HfO 2 :Nd films were found to be similar to those of as-deposited ones.

Surface morphology

Fig. 2 represents the surface SEM images of the Si-HfO 2 :Nd (Fig. 2, upper panel) and Si-N-HfO 2 :Nd (Fig. 2, bottom panel) samples. As one can see, the morphology of as-deposited films depends very much on the sputtering approach. The Si-N-HfO 2 :Nd films, grown with reactive approach, show smooth surface contrary to their Si-HfO 2 :Nd counterparts deposited in pure argon plasma (Fig. 2). This difference in the film morphology can be explained taking into account the structure zone model (SZM) proposed in 1969 for the films grown by electron-beam plasma vapor deposition [18] and developed later for the sputtering approach [19].

The real structure of the film is determined by four processes: i) shadowing, i.e. a geometric interaction between arriving adatoms and the roughness of the growing surface; ii) surface diffusion, i.e. mobility of adatoms at the surface and interfaces such as grain boundaries; iii) bulk diffusion, i.e. mobility of adatoms in the volume of grains, and iv) recrystallization, i.e. phase transition as a complete change of crystal orientation [18,19]. The processes i) and ii) dominate at low and medium temperature of the substrate, T S , respectively. Processes iii) and iv) are important at high T S . Besides, process iv) dominates at the percolation thickness and large film thickness.

It was shown that for the most metals and dielectrics, activation energies for diffusion are related to the melting temperature of material, T M [18]. Thus, the basic processes depend on the T S /T M ratio and determine the final structure of the film. In the regard of the value of T S /T M ratio, three different zones were considered: i) when T S / T M < 0.3, the formation of fine-grained porous structure due to the low mobility of adatoms and their stick at the place, where they were land on the substrate (zone I); ii) when 0.3 < T S /T M < 0.5, a columnar structure grows owing to surface diffusion of adatoms with the activation energy of 0.1-0.3 eV (zone II); iii) for T S /T M > 0.5, a rough equiaxed grain structure appears due to the high bulk diffusion with the activation energy of above 0.3 eV (zone III) [18].

Later, in this model the effect of the total plasma pressure on the film structure was introduced [19]. It was shown that for T S /T M < 0.1 and a higher argon pressure the formation of porous structure is less probable due to the high deposition rate (10 nm/min) and limited adatom surface mobility. At the same time for T S /T M = 0.1-0.3 (socalled after zone T), the self-diffusion is appreciable and the formation of films with the dense array of grains separated by grain boundaries occurs owing to sintering the grains. However, for T S /T M > 0.3, the effect of argon pressure decreases significantly with T S /T M rise and the real film structure can be found to be similar to that proposed in Ref. [18].

Taking into account the substrate temperature (T S = 400 °C) used for the fabrication of the films investigated and the melting temperature of sputtered targets, the T S /T M ratio can be determined. However, the melting temperature of the target can be roughly estimated. This target was constructed as the HfO 2 target (T M = 2810 °C) topped with Si (T M = 1410 °C) and Nd 2 O 3 (T M = 2233 °C) pellets. However, the main contribution in sputtering was given by the HfO 2 target. Considering corresponding T M values, one can obtain the T S /T M ∼0.14 that is corresponding to the zone T in the SZM model. One can expect the formation of the grain dense arrays separated with the grain boundaries. The surface of the films should show the presence of grains or clusters of different heights. This structure was found for the as-deposited Si-HfO 2 :Nd films (Fig. 2, upper panel). They contain the nanocrystal grains with the mean size of about 20-60 nm. These films show developed structure, i.e. the nanocrystals form the micro-clusters separated by the valleys.

For the films grown with reactive approach, the effect of nitrogen addition has to be taken into account. In this case the total plasma pressure was kept to be the same as for a standard approach. However, the nitrogen incorporation in the plasma implies on the change in the plasma dynamics because N 2 number increasing leads to enlarging the collision probability of atoms with the substrate surface and promotes a larger number of chemical reactions. The collisions enhances the atom surface mobility and increasingly smooth the film by the surface diffusion [20].

Recently, the effect of the oxygen content on the structure of SnO-SnO 2 -Sb 2 O 3 films was considered in Ref. [21]. It was reported decreasing the grain size and film roughness due to the high particle collision, enhanced atom surface diffusion and surface atom mobility in the growing films. This effect becomes more prominent for the higher oxygen content. For the film grown with O 2 /Ar = 0.47 the formation of very smooth films with small grains was found. Considering the mentioned above findings, one can explain the difference in the morphology of Si-HfO 2 :Nd and Si-N-HfO 2 :Nd films. For the latter the higher probability of particle collisions with the surface of growing film and higher diffusion of adatoms allows to form a surface covering layer and to block the grain's growth. This leads to the repeated nucleation and the formation of smooth films.

The annealing treatments at 950 and 1100 °C results in increasing the grain sizes in the Si-HfO 2 :Nd films up to 40-100 nm due to the change of grain boundaries and the coalescence of small grains (Fig. 2). Annealing the Si-N-HfO 2 :Nd samples does not affect their surface structure very much. However, the films annealed at T A = 1100 °C demonstrate some tendency in their densification (Fig. 2). It is worth to point that the more prominent effect on the film structure is appeared from the deposition conditions. The use of reactive approach allows the fabrication of smoother films.

XRD study

The samples described above were characterized by the XRD method. XRD patterns recorded with the symmetric geometry are shown in Fig. 3a,b. The both types of studied films in as-deposited states demonstrate a series of XRD peaks that can be attributed to the different phases. The very intensive two XRD peaks at 32.9726°( Figs. 3b) and 69.0523°(Fig. 3a andb) were identified as the XRD signal of the Si substrates, i.e. from ( 112) and (400) planes in cubic Si crystal lattice (Table 1). Additionally, a set of XRD peaks with the low intensity (Table 1) was assigned to the X-ray diffraction in the tetragonal Nd 2 Si 2 O 7 and tetragonal HfSiO 4 phases (Table 1). These phases were supposed to be appeared in the film due to the high deposition temperature of 400 °C.

The thermal treatment of the Si-HfO 2 :Nd film at T A = 950 °C leads to the significant changes of XRD patterns. The signal from the tetragonal Nd 2 Si 2 O 7 and tetragonal HfSiO 4 phases disappeared, while the XRD peaks related to the tetragonal HfO 2 phase become significant (Fig. 3a, Table 2). The intensities of five XRD peaks related to this phase (Fig. 3a, the peaks denoted as 1, 2, 3, 4, 6) increased significantly (Fig. 3a). Additionally, the XRD signal of the cubic Si phase (peak 5) and new XRD peaks, identified as the X-ray diffraction from the crystal planes in the hexagonal Nd 2 O 3 (peak 1c) and tetragonal SiO 2 (peak 1b) lattices, were detected (Fig. 3a, Table 2). Simultaneously, the improved crystallinity of the Si-HfO 2 :Nd film annealed at T A = 950 °C hampers the detection of XRD peaks from underlying Si substrate (Fig. 3a).

Thermal annealing the Si-N-HfO 2 :Nd films is less effective. The crystallization process in these films at T A = 950 °C is completely different (Fig. 3b). All XRD peaks, which were revealed in the as-deposited Si-N-HfO 2 :Nd film, have been detected also after their annealing at 950 °C (Fig. 3b). Additionally, the new XRD peaks (Fig. 3b, peaks 1-6) have appeared and were attributed to the tetragonal HfSiO 4 phase (Table 3). Note that owing to the less effective crystallization process in the Si-N-HfO 2 :Nd film at 950 °C, the peaks related to the XRD in the Si substrate can be still detected in the XRD pattern (Fig. 3b).

Light-emitting properties

The luminescent spectroscopy is a powerful tool for the characterization of rare-earth doped materials. Hereafter the most interesting results for the films described above will be presented. Note that the majority researchers were focused early on the investigation of the near-infrared PL spectrum in the range 800-1400 nm [10][11][12][13][14][15][16]. Typical radiative emission bands of Nd 3+ ions in the mentioned range can be detected at 880, 1060, and 1350 nm [10,[START_REF] Cui | Raman and fluorescence spectroscopy of CeO 2 , Er 2 O 3 , Nd 2 O 3 , Tm 2 O 3 , Yb 2 O 3 , La 2 O3, and Tb 4 O 7[END_REF][23][24]. These emission bands originate from the electronic transitions between 4f N levels: 4 F 3∕2 → 4 I 9∕2 , 4 F 3∕2 → 4 I 11∕2 and 4 F 3∕2 → 4 I 13∕2 in Nd 3+ ions (Table 4).

It is essential that the high PL intensity in the studied films was detected for the annealed films. The spectra registered in the visible and IR spectral ranges with the excitation by the 325 nm line of a He-Cd laser and 488 nm line of an Ar + -ion lasers are shown in Fig. 4 and Fig. 5a, respectively. The total PL intensity has non-monotonic behavior with annealing temperature. The highest PL intensity was detected for the films annealed at T A = 900-950 °C whatever the deposition approaches.

Fig. 4a shows the visible PL spectra of the Si-HfO 2 :Nd films in the asdeposited state and after annealing at T A = 950 °C. As one can see, the PL spectrum consists the several PL components related to the optical transitions in the 4f inner electronic shell of Nd 3+ ions with a set of peaks in the spectral range of 350-900 nm (Table 4). Their contribution depends on the annealing temperature and governs the shape of total PL spectrum. The crystallization of the Si-HfO 2 :Nd film upon annealing at 950 °C and the appearance of the tetragonal HfO 2 crystal phase (Table 2) lead to significant increasing the PL intensity of visible and IR PL bands (Table 4, Fig. 6).

In contrary, the PL spectra of the Si-N-HfO 2 :Nd film are still featureless after annealing at T A = 950 °C (Fig. 4b). Actually, the PL intensity increases after annealing, but whatever the annealing temperatures, PL spectra demonstrate broad unstructured PL bands with the main peak within 440-480 nm and other small PL peaks in the range of 750-900 nm (Fig. 4b). Thus the less effective crystallization of the Si-N-HfO 2 :Nd film upon annealing (Fig. 3b) no permit re-arranging the Nd 3+ ions in host matrix that is necessary to obtain the efficient radiative defects.

Fig. 5a represents the PL spectra of both films in the range of 800-1400 nm. One can see that the typical radiative emission bands of Nd 3+ ions are detected at 880, 1060, and 1350 nm. The spectra have similar shape. The difference in the intensity can be explain by the less effective crystallization of the Si-N-HfO2:Nd film upon annealing and lower amount of the sensitizers in Si-N-HfO 2 :Nd that can excite the Nd 3+ ions. Another reason can be the competition between different radiative channels contributed in the visible broad PL band.

The effect of the excitation wavelengths on the intensity of PL bands centered at 550 nm and 880 nm is shown in Fig. 5b for the Si-HfO 2 :Nd film. As one can see, the PL bands at 550 nm and 880 nm have the similar PL intensities under the excitation with the 500 nm and 514 nm light wavelengths. However, at shorter excitation wavelengths, the 880nm Nd 3+ PL band demonstrates some kind of saturation, whereas the 550-nm PL bands increases in the intensity. It is worth to point that 476 and 455 nm excitation is the non-resonant and resonant excitations for the Nd 3+ ions, respectively (Table 4). The similar intensities of the 880nm Nd 3+ PL band related to the 4 F 3/2 → 4 I J (J = 9/2) radiative transition detected at the both excitations (resonant and non-resonant) give the evidence that the rare-earth ions are excited via energy transfer from the Si nanoclusters or host defects as well. Recently we have demonstrated such effect for the Er-doped and Pr-doped HfSiO films [8,9].

The increase of annealing temperatures results in decreasing the PL intensity of Nd-related PL bands in the both types of investigated films. This can be due to the formation of Nd-oxide or Si-oxide phases, as well as due to decreasing the number of sensitizers, for instance, the Si nanoclusters, caused by their oxidation, or recovering the host defects.

Conclusions

In the present work, the effects of deposition and annealing conditions on the emission, chemical composition and structural properties of the Si-HfO 2 :Nd and Si-N-HfO 2 :Nd films have been studied. The Ndrelated emission was achieved via the direct excitation of the ions as well as by the excitation via Si-nanoclusters. It is also demonstrated that in the film grown in pure argon plasma, the formation of the nanocrystal tetragonal HfO 2 phase upon high-temperature annealing favors the bright emission related to the optical transitions via the 4f inner electronic levels in Nd 3+ ions. Using the argon-nitrogen plasma allows the fabrication of films with the same composition, but the smooth surface that may be interesting for optic waveguide applications. The annealing of these films is less efficient in terms of the film crystallization. It was shown that the highest Nd-related PL emission can be obtained from the films heat treated at 900-950 °C.

Fig. 1 .

 1 Fig. 1. EDS spectra for Si-HfO 2 :Nd (curves 1) and Si-N-HfO 2 :Nd (curves 2) films, as-deposited (a) and annealed at 1100 °C (b).

Fig. 2 .

 2 Fig. 2. Surface SEM images of the Si-HfO 2 :Nd (upper panel) and Si-N-HfO 2 :Nd (bottom panel) films as-deposited and annealed at 950 and 1100 °C.

Fig. 3 .

 3 Fig. 3. XRD spectra for Si-HfO 2 : Nd (a) and Si-N-HfO 2 : Nd (b) films, as-deposited and annealed at 950 °C.

Fig. 4 .

 4 Fig. 4. PL spectra for Si-HfO 2 : Nd (a) and Si-N-HfO 2 : Nd (b) films, as-deposited and annealed at 950 °C, detected by a photo multiplayer tube.

Fig. 5 .

 5 Fig. 5. a) PL spectrum of the Si-HfO 2 : Nd (1) and Si-N-HfO 2 :Nd (2) films annealed at 950 °C in the range 850-1500 nm. The spectra are normalized for the film thickness. The excitation wavelength is 488 nm (non-resonant for Nd 3+ ions). b) Variation of PL intensity for the PL peaks at 550 nm and 880 nm versus excitation wavelength detected in the Si-HfO 2 : Nd film annealed at 950 °C.

Fig. 6 .

 6 Fig.6. Excitation and recombination optical transitions via the 4f inner electronic levels detected for the Nd 3+ ions embedded in Si-HfO 2 :Nd films. The Nd 3+ energy level diagram was taken from Ref.[11].

Table 1

 1 XRD peaks in as-deposited Si-HfO 2 :Nd and Si-N-HfO 2 :Nd films.

	Peak	Degree	(h k l)	Phase	Crystal type	ICSD
	a	32.9726	(1 1 2)	Si	Cubic	00-005-0490
	a *	34.0723	(2 1 1)	HfSiO 4	Tetragonal	01-077-1759
	b	55.8723	(4 0 0)	HfSiO 4	Tetragonal	01-077-1759
	c	61.6237	(2 4 1)	Nd 2 Si 2 O 7	Tetragonal	01-089-5347
	d	66.2150	(3 3 8)	Nd 2 Si 2 O 7	Tetragonal	01-089-5347
	e	69.0523	(4 0 0)	Si	Cubic	00-005-0490
	e*	69.8360	(3 2 3)	HfSiO 4	Tetragonal	01-077-1759
	e "	73.6440	(4 3 1)	HfSiO 4	Tetragonal	01-077-1759

Table 2

 2 XRD peaks of Si-HfO 2 :Nd film annealed at 950 °C (Fig.3a).

	Peak	Degree	(h k l)	Phase	Crystal type	ICSD
	1a	30.0643	(1 0 1)	HfO 2	Tetragonal	00-008-0342
	1b	30.1870	(1 1 0)	SiO 2	Tetragonal	00-045-1374
	1c	30.7750	(1 0 1)	Nd 2 O 3	Hexagonal	00-041-1089
	2a	34.1623	(0 0 2)	HfO 2	Tetragonal	00-008-0342
	2b	35.1123	(2 0 0)	HfO 2	Tetragonal	00-008-0342
	3a	49.9123	(2 0 2)	HfO 2	Tetragonal	00-008-0342
	3b	50.2723	(2 2 0)	HfO 2	Tetragonal	00-008-0342
	4a	59.7523	(3 1 1)	HfO 2	Tetragonal	00-008-0342
	4b	59.9723	(2 2 2)	HfO 2	Tetragona	00-008-0342
	4c	60.4242	(1 2 1)	SiO 2	Tetragonal	00-045-1374
	5	69.0523	(4 0 0)	Si	Cubic	00-005-0490
	6	82.1823	(2 0 4)	HfO 2	Tetragonal	00-008-0342

Table 3

 3 XRD peaks of Si-N-HfO 2 :Nd film annealed at 950 °C (Fig.3b).

	Peak	Degree	(h k l)	Phase	Crystal type	ICSD
	a	33.0126	(1 1 2)	Si	Cubic	00-005-0490
	a *	34.0723	(2 1 1)	HfSiO 4	Tetragonal	01-077-1759
	1	38.6826	(2 2 0)	HfSiO 4	Tetragonal	01-077-1759
	2	40.7726	(2 0 2)	HfSiO 4	Tetragonal	01-077-1759
	3	44.0526	(3 0 1)	HfSiO 4	Tetragonal	01-077-1759
	c	61.6237	(2 4 1)	Nd 2 Si 2 O 7	Tetragonal	01-089-5347
	4	64.6307	(4 0 2)	HfSiO 4	Tetragonal	01-077-1759
	d	66.2150	(3 3 8)	Nd 2 Si 2 O 7	Tetragonal	01-089-5347
	e	69.0523	(4 0 0)	Si	Cubic	00-005-0490
	e*	69.8360	(3 2 3)	HfSiO 4	Tetragonal	01-077-1759
	e *	73.6440	(4 3 1)	HfSiO 4	Tetragonal	01-077-1759
	5	76.4726	(4 1 3)	HfSiO 4	Tetragonal	01-077-1759
	__6__	81.1056_	(5 1 2]	HfSiO 4	Tetragonal	01-077-1759

Table 4

 4 Optical transitions detected in Si-HfO 2 :Nd films (Fig.4a).

	Number	Wavelength, nm	Optical transition	Reference
	1	400	4 P 1/2 → 4 I 9∕2	[14]
	2	455	4 G 11/2 → 4 I 9∕2	[=23,24]
	3	540-550	4 G 7/2 → 4 I 9∕2	[12,24]
	4	560	4 G 5/2 → 4 I 9∕2	[12,22]
	5	660	4 F 9∕2 → 4 I 9∕2	[10]
	5	650	2 G 7/2 → 4 I 9∕2	[24]
	6	710	4 F 7∕2 → 4 I 9∕2	[24]
	7	790	2 H 9/2 , 4 F 5∕2 → 4 I 9∕2	[22]
	8	880	4 F 3∕2 → 4 I 9∕2	[10]
	9	1074	4 F 3∕2 → 4 I 11∕2	[10]
	10	1330	4 F 3∕2 → 4 I 13∕2	[10]

Acknowledgements

This work was partly supported by National Academy of Sciences of Ukraine (project III-4-16), Ministry of Education and Science (project ID: 89452), the French National Agency of Research (ANR), as well as by the CONACYT Mexico (project 258224) and SIP, IPN Mexico (project 20180495).


Serge Abiteboul
email: serge.abiteboul@inria.fr

Julia Stoyanovich
email: stoyanovich@nyu.edu.

Transparency, Fairness, Data Protection, Neutrality: Data Management Challenges in the Face of New Regulation

Keywords: CCS Concepts:, Information systems → Data management systems, • Social and professional topics → Computing / technology policy, Technology audits, • Applied computing → Law, transparency, fairness, data protection, neutrality, responsible data science

The data revolution continues to transform every sector of science, industry and government. Due to the incredible impact of datadriven technology on society, we are becoming increasingly aware of the imperative to use data and algorithms responsibly -in accordance with laws and ethical norms. In this article we discuss three recent regulatory frameworks: the European Union's General Data Protection Regulation (GDPR), the New York City Automated Decisions Systems (ADS) Law, and the Net Neutrality principle, that aim to protect the rights of individuals who are impacted by data collection and analysis. These frameworks are prominent examples of a global trend: Governments are starting to recognize the need to regulate data-driven algorithmic technology.

Our goal in this paper is to bring these regulatory frameworks to the attention of the data management community, and to underscore the technical challenges they raise and which we, as a community, are well-equipped to address. The main take-away of this article is that legal and ethical norms cannot be incorporated into data-driven systems as an afterthought. Rather, we must think in terms of responsibility by design, viewing it as a systems requirement.

INTRODUCTION

The data revolution continues to transform every sector of science, industry and government. Due to the incredible impact of data-driven technology on society, we are becoming increasingly aware of the imperative to use data and algorithms responsibly -in accordance with laws and ethical norms. The goal of this article is to underscore the technical challenges raised by recent legal and regulatory frameworks, which the data management community is well-equipped to address.

We discuss three recent frameworks: the European Union's General Data Protection Regulation (GDPR) [The European Union 2016], the New York City Automated Decisions Systems (ADS) Law [The New York City Council 2017], and the Net Neutrality principle. These frameworks are prominent examples of a global trend: Governments are starting to recognize the need to regulate data-driven algorithmic technology. The GDPR and the NYC ADS Law aim to protect the rights of individuals who are impacted by data collection and analysis, while the Net Neutrality principle ensures that services are being treated equitably. Yet, despite the focus on organizations, rights of individuals also figure prominently in the neutrality debate: One of the imperatives is that individuals should be able to enjoy freedom of choice and expression on-line. We will give some legal context on neutrality by discussing the EU Regulation 2015/2120 [The European Parliament AND Council 2015], the Indian Net Neutrality Regulatory Framework [Government of India, Ministry 2018], and the ongoing regulatory debate on Net Neutrality in the US.

Our goal in this paper is to bring these regulatory frameworks to the attention of the data management community. The main take-away of this article is that legal norms cannot be incorporated into data-driven systems as an afterthought. Rather, we must think in terms of responsibility by design, viewing it as a systems requirement.

The General Data Protection Regulation

The European Union recently enacted a sweeping regulatory framework known as the General Data Protection Regulation, or the GDPR [The European Union 2016]. The regulation was adopted in April 2016, and became enforceable about two years later, on May 25, 2018. The GDPR aims to protect the rights and freedoms of natural persons with regard to how their personal data is processed, moved and exchanged (Article 1). The GDPR is broad in scope, and applies to "the processing of personal data wholly or partly by automated means" (Article 2), both in the private sector and in the public sector. Personal data is broadly construed, and refers to any information relating to an identified or identifiable natural person, called the data subject (Article 4). In this article we focus on the following salient points of the regulation:

• lawful processing of data is predicated on the data subject's informed consent, stating whether their personal data can be used, and for what purpose (Articles 6, 7);

• the data subject has a right to correct any errors in their data ("right to rectification", Article 16), to withdraw their data from the system ("right to erasure", Article 17), and to move data from one data processor to another ("right to portability", Article 20);

• the data subject has the right to be informed about the collection and use of their data. 1

The primary focus of the GDPR is on protecting the rights of data subjects, by giving them insight into, and control over, the collection and processing of their personal data. Providing insight, in response to the "right to be informed", requires technical methods for algorithmic and data transparency, which we will discuss in Section 2. We will also discuss the challenges inherent in giving individuals an ability to erase or move their data in Section 4.

The New York City Algorithmic Decision Systems Law

New York City recently passed a law [The New York City Council 2017] requiring that a task force be put in place to survey the current use of "automated decision systems" (ADS), defined as "computerized implementations of algorithms, including those derived from machine learning or other data processing or artificial intelligence techniques, which are used to make or assist in making decisions, " in City agencies. The task force is working to develop a set of recommendations for enacting algorithmic transparency by the agencies, and will propose procedures for:

• requesting and receiving an explanation of an algorithmic decision affecting an individual (Section 3 (b));

1 https://gdpr-info.eu/issues/right-to-be-informed/

• interrogating automated decision systems for bias and discrimination against members of legally protected groups, and addressing instances in which a person is harmed based on membership in such groups (Sections 3 (c) and (d));

• assessing how automated decision systems function and are used, and archiving the systems together with the data they use (Sections 3 (e) and (f)).

In contrast to the GDPR, which is very broad in scope, the NYC ADS Law only regulates City agencies in their use of algorithms and data, and does not directly apply to private companies. However, because government agencies often procure systems and components from industry partners, the Law will likely impact industry practices. Further, while New York is the first US city to pass a law of this kind, we expect other US municipalities to follow with similar legal frameworks or recommendations in the near future.

The primary focus of the NYC ADS Law is on algorithmic transparency, which, in turn, cannot be achieved without data transparency [START_REF] Stoyanovich | Follow the data! Algorithmic transparency starts with data transparency[END_REF]]. As we discussed in Section 1.1, transparency is also an implicit requirement of the GDPR, stemming from the "right to be informed". We will discuss the role that the data management community can play in enabling data transparency in Section 2.

The NYC ADS Law further requires fair and equitable treatment of individuals, mandating that ADS safeguard against bias and discrimination, and provide transparency in this regard. We will discuss fairness in Section 3, and will propose some research directions for the data management community that are complementary to the rich and rapidly expanding body of work on fairness in machine learning.

The Net Neutrality Principle

Net Neutrality is the principle that Internet Service Providers (ISPs) should not discriminate or charge differently based on the message source (the content provider), its destination (the user), or its content. The concept was articulated by Tim Wu in 2003 [START_REF] Wu | Network Neutrality, Broadband Discrimination[END_REF]].

According to Net Neutrality, an ISP cannot block or throttle video streams from YouTube (negative discrimination), or enable free access to Facebook out of package (a kind of positive discrimination). A September 2018 report from Northeastern University and the University of Massachusetts, Amherst, found that US telecommunications companies are indeed slowing internet traffic to and from those two sites in particular, along with other popular apps [START_REF] Kharif | YouTube, Netflix Videos Found to Be Slowed by Wireless Carriers[END_REF][START_REF] Molavi Kakhki | Identifying Traffic Differentiation in Mobile Networks[END_REF]]. Of course, there are limits to the non-discrimination, such as blocking pornographic material for young Internet users, filtering hate speech in some countries, or guaranteeing quality for emergency services.

In ProPublica's story on "machine bias" in an algorithm used for sentencing defendants [START_REF] Angwin | Machine Bias: Risk Assessments in Criminal Sentencing[END_REF]] amplified calls to make algorithms more transparent and accountable [START_REF] Kroll | Accountable Algorithms[END_REF]. Transparency and accountability are intrinsically linked with trust, and are of particular importance when algorithmic systems are integrated into government processes, assisting humans in their decision-making tasks, and sometimes even replacing humans. Transparency of government is a core democratic value, which compels us to develop technological solutions that both increase government efficiency and can be made transparent to the public.

A narrow interpretation of algorithmic transparency requires that the source code of a system be made publicly available. This is a significant step towards transparency (as long as the posted code is readable, well-documented and complete), but it is rarely sufficient. One of the reasons for this, of particular relevance to the data management community, is that meaningful transparency of algorithmic processes cannot be achieved without transparency of data [START_REF] Stoyanovich | Follow the data! Algorithmic transparency starts with data transparency[END_REF].

What is data transparency, and how can we achieve it? One immediate interpretation of this term in the context of predictive analytics includes "making the training and validation datasets publicly available. " However, while data should be made open whenever possible, much of it is sensitive and cannot be shared directly. That is, data transparency is in tension with the privacy of individuals who are included in the dataset. In light of this, we may adopt the following alternative interpretation of data transparency: In addition to releasing training and validation datasets whenever possible, vendors should make publicly available summaries of relevant statistical properties of the datasets that can aid in interpreting the decisions made using this data, while applying state-of-the-art methods to preserve the privacy of individuals (such as differential privacy [START_REF] Dwork | The Algorithmic Foundations of Differential Privacy[END_REF]). When appropriate, privacy-preserving synthetic datasets can be released in lieu of real datasets to expose certain features of the data [START_REF] Ping | DataSynthesizer: Privacy-Preserving Synthetic Datasets[END_REF]].

An important aspect of data transparency is interpretability -surfacing the statistical properties of a dataset, the methodology that was used to produce it, and, ultimately, substantiating its "fitness for use" in the context of a specific automated decision system or task. This consideration of a specific use is particularly important because datasets are increasingly used outside the original context for which they were intended. The data management community can begin addressing these challenges by building on the significant body of work on data profiling (see [START_REF] Ziawasch Abedjan | Data Profiling: A Tutorial[END_REF]] for a recent tutorial), with an eye on the new legal requirements.

Interpretability rests on making explicit the interactions between the program and the data on which it acts. This property is important both when an automated decision system is interrogated for systematic bias and discrimination, and when it is asked to explain an algorithmic decision that affects an individual. For example, suppose that a system scores and ranks individuals for access to a service. If an individual enters her data and receives the result -say, a score of 42 -this number alone provides no information about why she was scored in this way, how she compares to others, and what she can do to potentially improve her outcome. A prominent example of a system of this kind, which is both opaque and extremely impactful, is the FICO credit scoring system in the US [START_REF] Citron | The Scored Society: Due Process for Automated Predictions[END_REF].

The data management research community is well-positioned to contribute to developing new methods for interpretability. These new contributions can naturally build on a rich body of work on data provenance (see [START_REF] Herschel | A survey on provenance: What for? What form? What from[END_REF]] for a recent survey), on recent work on explaining classifiers [START_REF] Túlio Ribeiro | Explaining the Predictions of Any Classifier[END_REF]] and auditing black box models using causal framework [START_REF] Datta | Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems[END_REF]], and on automatically generating "nutritional labels" for data and models [START_REF] Yang | A Nutritional Label for Rankings[END_REF]].

FAIRNESS

We can all agree that algorithmic decision-making should be fair, even if we do not agree on the definition of fairness.

But isn't this about algorithm design? Why is this a data problem? Indeed, the machine learning and data mining research communities are actively working on methods for enabling fairness of specific algorithms and their outputs, with a particular focus on classification problems (see, for example, [START_REF] Dwork | Fairness through awareness[END_REF][START_REF] Feldman | Certifying and Removing Disparate Impact[END_REF][START_REF] Sorelle | On the (im)possibility of fairness[END_REF][START_REF] Hajian | A Methodology for Direct and Indirect Discrimination Prevention in Data Mining[END_REF][START_REF] Kamiran | Quantifying explainable discrimination and removing illegal discrimination in automated decision making[END_REF][START_REF] Kleinberg | Inherent Trade-Offs in the Fair Determination of Risk Scores[END_REF][START_REF] Romei | A multidisciplinary survey on discrimination analysis[END_REF] and proceedings of the recently established ACM Conference on Fairness, Accountability, and Transparency (ACM FAT*)2 ). While important, these approaches focus solely on the final step in the data science lifecycle, and are thus limited by the assumption that input datasets are clean and reliable.

Data-driven algorithmic decision making usually requires multiple pre-processing stages to address messy input and render it ready for analysis [START_REF] Jagadish | Big data and its technical challenges[END_REF]]. This pre-processing, which includes data cleaning, integration, querying and ranking, is often the source of algorithmic bias [START_REF] Kirkpatrick | It's Not the Algorithm, It's the Data[END_REF][START_REF] Stoyanovich | Fides: Towards a Platform for Responsible Data Science[END_REF], and so reasoning about sources of bias, and mitigating unfairness upstream from the final step of data analysis, is potentially more impactful.

For example, much research goes into ensuring statistical parity -a requirement that the demographics of those receiving a particular outcome, (e.g., a positive or negative classification), are identical to the demographics of the population as a whole. Suppose that the input to a binary classifier contains 900 men and 100 women, but that it is known that women represent 50% of the over-all population, and so achieving statistical parity amounts to enforcing a 50-50 gender balance among the positively classified individuals. That is, all else being equal, a woman in the input to the classifier is far more likely to receive a positive classification than a man. An alternative is to observe the following:

If the input to the classifier was produced by a SQL query, and if relaxing the query would make the input more balanced (e.g., 1000 men and 500 women), then a more effective way to mitigate the lack of statistical parity in the output of the classifier is to relax the query upstream.

It is easy to construct additional examples that show how bias may be introduced during data cleaning, data integration, querying, and ranking -upstream from the final stage of data analysis. Therefore, it is meaningful to detect and mitigate these effects in the data lifecycle stages in which they occur. (See [START_REF] Mitchell | Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions[END_REF]] for a discussion of the definitions of "bias", and of the corresponding assumptions made when defining fairness measures.)

Members of the data management community who are interested in this topic may consider a growing body of work on impossibility results, which show that different notions of fairness cannot be enforced simultaneously, and so require explicit trade-offs [START_REF] Chouldechova | Fair prediction with disparate impact: A study of bias in recidivism prediction instruments[END_REF][START_REF] Sorelle | On the (im)possibility of fairness[END_REF][START_REF] Kleinberg | Inherent Trade-Offs in the Fair Determination of Risk Scores[END_REF]]. These are not negative results per se, nor are they surprising. Fairness is a subjective, context-dependent and highly politicized concept; a global consensus on what is fair is unlikely to emerge, in the context of algorithmic decision making or otherwise. Think, for example, of the decade-long debate about the interplay between "disparate treatment" and "disparate impact", for which recent examples include by Ricci v. De Stefano3 and the ongoing lawsuit regarding the use of race in Harvard University admissions4 . That being said, a productive way to move forward in the data science context is to develop methods that can be instrumented with different alternative fairness notions, and that can support principled and transparent trade-offs between these notions.

MOVING AND REMOVING PERSONAL DATA

The Right to Be Forgo en

The right to be forgotten is originally motivated by the desire of individuals to not be perpetually stigmatized by something they did in the past. Under pressure from despicable social phenomena such as revenge porn, it was turned recently into laws in 2006 in Argentina, and since then in the European Union, as part of the GDPR. In particular, Article 17 of the GDPR states that data subjects have the right to request erasure of their personal data, and that they can do so for a large number of reasons.

The passing of this law primarily resulted in a high number of requests to search engines to dereference web pages.

This turned out to be controversial for a number of reasons, including also that the dereferencing by Google is very opaque, and that this company in effect acquired, against its own will, a questionable power to adjudicate. Furthermore, as is advocated by Wikimedia among others, the right to be forgotten sometimes conflicts with other rights such as the public's right to information.

In addition to search engines, the right to be forgotten affects companies that keep personal data. A prominent example is Facebook, where for many years it was impossible to delete data that pertains to a user's account. A user may close an account, then reopen it some time later and find all her data as it was originally. It is now possible to request the deletion of all data pertaining to an account from Facebook, however, the user has no proof that the deletion indeed occurred.

An important technical issue, of clear relevance to the data management community, is that of deletion of information in systems that are typically meant to accumulate data. This deletion must be both permanent and deep, in the sense that its effects must propagate through data dependencies. To start, it is difficult to guarantee that all copies of every piece of deleted data have actually been deleted. Further, when some data is deleted, the remaining database may become inconsistent, and may, for example, include dangling pointers. Additionally, production systems typically do not include a strong provenance mechanism, and so they have no means of tracking the use of an arbitrary data item (one to be deleted), and reasoning about the dependencies on that data item in derived data products.

Although much attention of the data management community has over the years been devoted to tracking and reasoning about provenance, primarily in relational contexts and in workflows (see [START_REF] Herschel | A survey on provenance: What for? What form? What from[END_REF]] for a recent survey), there is still important work to be done on making these methods both practically feasible, and sufficiently general to accommodate the current legal requirements. An important direction that is, to the best of our knowledge, still unexplored, concerns ascertaining the effects of a deletion on downstream processes that are not purely relational, but include other kinds of data analysis tasks, like data mining or predictive analytics.

Requests for deletion may also conflict with other laws such as requirements to keep certain transaction data for some period of time, or with requirements for fault tolerance and recoverability. Should the deleted pieces of data also be erased from caches and backups? Requesting this functionality gives immediate nightmares to systems engineers in charge of a production data management system, with millions of lines of code and terabytes of legacy data. The likely answer is: "this cannot be done; the only solution I see is redeveloping the system from scratch with right-tobe-forgotten-by-design. " Understanding the impact of deletion requests on our ability to offer guarantees on system resilience and performance, and developing appropriate primitives and protocols for practical use, is another call to action for the data management community.

Interoperability and Portability

Article 20 of the GDPR, "Right to data portability", stipulates a data subject's right to receive her personal data from a vendor, and to transfer her data to another vendor. The main goals of this provision are both to keep the data subject informed about what data a vendor has about her, and to prevent vendor lock-in. This enables a user who is unhappy with a service to leave for a competing service that best serves her needs, without having to reconstruct her entire data history. This also allows a user to select applications of her choice and have them cooperate, to her best advantage, even if they come from different vendors.

In response to data portability regulation, and to users' concerns, Google, Twitter, Microsoft, and Facebook teamed up in the Data Transfer Project that aims to facilitate content transfer between applications. Of course, it is not an easy task for a company to provide a service that facilitates the departure of its customers. This is why, in spite of commendable behavior of companies that engage in the Data Transfer Project, it is the role of regulators to impose data portability and interoperability requirements.

Interoperability of database applications is an old topic. But one can imagine an unlimited number of possibilities, such as having a Whatsapp call talk to a Skype one. And it certainly acquires a different flavor when we consider interoperating applications with billions of users and millions of transactions per second.

For data portability, it should be noted that the devil is in the detail. The export format should be stable and structured to facilitate reuse. Also, which data can be exported is an issue. Obviously, it includes all data that the user volunteered to the service. But should it also include data the vendor gathered from the behavior of the user (e.g., the time the user is waking up in the morning)? Should it include data the service inferred (e.g., what is the home address of the user, her job address)?

Another issue with portability is the target system. A user may want to port her photos from Service A to Service B. The issue is then for Service B to be able to incorporate as much data as possible from Service A. Now, the user may want to integrate her photos in a personal information system [START_REF] Abiteboul | Managing Your Digital Life[END_REF]]. Such a system must be able to integrate information from a large panel of domains. This brings us to the fields of data integration [START_REF] Lenzerini | Data Integration: A Theoretical Perspective[END_REF] and knowledge representation.

NEUTRALITY

As already mentioned, Net Neutrality is now legally required in some countries. Yet, detecting Net Neutrality violations to enforce the law is not an easy task. Indeed, simply measuring the performance of Internet communications is not easy: measurement results may depend on the location of the source, of the target, of the context (other applications competing for the same bandwidth), and on other factors. Indeed, different measures provided for network traffic typically diverge. The evaluation of Net Neutrality relying on such hard-to-obtain measures is a challenging research topic [START_REF] Molavi Kakhki | Identifying Traffic Differentiation in Mobile Networks[END_REF], which is primarily of interest to the networks and Internet measurement communities, and less so to data management.

But beyond Net Neutrality, new forms of neutrality are emerging such as device neutrality (is my smart-phone blocking certain apps and favoring others?), and platform neutrality (is this particular web service providing neutral recommendation?). For instance, app stores like Google Play and the Apple App Store, tend to refuse to reference certain services, perhaps because they are competing with the company's own services. Research is needed to be able to verify these new facets of neutrality. In particular, it is not easy to check whether a recommendation engine like Google search or Booking is enforcing only transparent editorial policies, and whether, other than that, their results are comprehensive, impartial and based solely on relevance. For example, it has been observed that search engines tend to favor some "friendly" services over competitors5 .

TAKE-AWAYS

In this article, we discussed several recent regulatory frameworks that aim to protect the rights of individuals, to ensure equitable treatment of services, and to bring transparency to data-driven algorithmic processes in industry and in government. Our goal was to bring these regulatory frameworks to the attention of the data management community, and to underscore the technical challenges they raise and which we, as a community, are well-equipped to address.

An important take-away of this article is that legal norms cannot be incorporated into data-driven systems as an afterthought. Rather, we must think in terms of responsibility by design, viewing it as a systems requirement.

We also stress that enacting algorithmic and data transparency, fairness, data protection, and neutrality will require a significant cultural shift. In making this shift, we must accept that the objectives of "efficiency", "accuracy" and "utility" cannot be the primary goal, but that they must be balanced with equitable treatment of members of historically disadvantaged groups, and with accountability and transparency to individuals affected by algorithmic decisions and to the general public.

In this article we focused on explicit regulation of industry stakeholders by government entities (in the case of the GDPR and the Net Neutrality laws), and on government oversight (in the case of the NYC ADS law). Another implicit regulatory mechanism can be achieved by empowering users and user associations, by providing them with data literacy education and with precise information on how different products and services work. Better educated users can choose better solutions, including more effective ways to protect their private data. Such users can also more easily understand explanations provided to them by an algorithmic system. User associations can help individuals make informed choices, and support them via class actions lawsuits in the case of disputes.

  the European Union, Net Neutrality is guaranteed by EU Regulation 2015/2120 [The European Parliament AND Council 2015], although different countries may interpret the regulation differently. For example, some forms of zero-rating, the practice of providing Internet access without financial cost as a means of positive discrimination, are legal in some EU countries but not in others. Since 2018, India has perhaps the world's strongest Net Neutrality rules [Government of India, Ministry of Communication 2018]. In general, more and more countries are adopting Net Neutrality regulations, with a notable exception. In the United States, the Federal Communications Commission (FCC) issued its Open Internet Order in 2015, reclassifying Internet access -previously classified as an information service -as a common carrier telecommunications service, thereby enforcing some form of Net Neutrality. However, in 2017, under the chairmanship of Ajit Pai, the FCC officially repealed Net Neutrality rules.

https://www.fatconference.org/

https://en.wikipedia.org/wiki/Ricci_v._DeStefano

https://www.nytimes.com/2018/10/13/us/harvard-affirmative-action-asian-students.html

https://en.m.wikipedia.org/wiki/European_Union_vs._Google

ACKNOWLEDGMENTS

This work was supported in part by National Science Foundation (NSF) Grant No. 1741047, and by Agence Nationale de la Recherche (ANR) Grant Headwork.


was recovered by filtration. The 1 H NMR spectrum, obtained on a Bruker DPX 300 MHz spectrometer, con firmed that the precipitate is pyrrothine hydrochloride: d H (DMSO, d 6 ): 7AE41 (1H, s, C=CH) 3AE9 5AE3 (1H, br s, NH 3 + ) 3AE27 (3H, s, CH 3 ).

Cell free extract preparation

Saccharothrix algeriensis NRRL B 24137 was grown in SSM or SSM supplemented with organic acids as described in the section 'Culture conditions'. The biomass was recovered by centrifugation of culture broth at 5000 g for 15 min (4K15; Sigma), then washed twice with physiological water (0AE9% NaCl) and once with lysis buffer (Tris HCl 50 mmol l 1 , pH 8). Wet cells were finally recovered by filtration on 0AE2 lm membrane filters (Advantec, Dublin, Ireland). Wet cells (0AE6 g) were re suspended in 1 ml of lysis buffer and transferred to a Fast Protein Blue tube (MP Biomedicals, Irvine, CA, USA). Two disruption cycles (30 s, 5 m s 1 ) were carried out in a Fast Prep disruptor (MP Biomedicals). The lys ing matrix was discarded, and then the sample was centri fuged at 10 000 g for 30 min (centrifuge 1 15K; Sigma) to remove the cell debris. The supernatant constituting the soluble cell free extract of Sa. algeriensis NRRL B 24137 was used immediately for the assay of pyrrothine N acyltransferase activity. A sample was frozen at )80°C for further protein assays. Samples were always kept in ice and centrifugations were carried out at 4°C.

Pyrrothine N acyltransferase assay

Pyrrothine N acyltransferase activity was assayed in a final volume of 100 ll. The reaction mixture contained 80 ll of cell free extract in Tris HCl buffer (50 mmol l 1 , pH 8), 10 ll of acetyl or benzoyl coenzyme A at 5 mmol l 1 in bidistilled water and 10 ll of pyrrothine hydrochoride at 2AE5 mmol l 1 in methanol. The reaction mixture was incubated at 30°C for 5 and 10 min. The reaction was stopped by adding fresh 2AE5% w ⁄ v trichloroacetic acid (Fisher). The cell free extract was properly diluted with Tris HCl buffer (50 mmol l 1 , pH 8) before assay to observe a product formation linear in time within 10 min. Enzymatic activity was identified as either acetyltrans ferase or benzoyltransferase activity according to the acyl group donor used during the assay, i.e. acetyl CoA and benzoyl CoA, respectively. A unit of enzyme is defined as the enzyme activity producing 1 lmol of thiolutin (acetyl pyrrothine) or BEP per minute. Specific enzymatic activity was expressed in lU mg 1 of protein.

Enzymatic activity was also expressed in mU g 1 of DCW. A value of 162 mg of proteins per gram of dry cells was used to achieve conversion from U mg 1 of pro teins to U g 1 of DCW. Protein content in dry cells was determined as follow. DCW and intracellular protein con centration were determined every 24 h, for 7 days, in a culture of Sa. algeriensis NRRL B 24137 on SSM. The plot of proteins in mg l 1 vs DCW in g l 1 indicated a linear correlation between the two parameters with a slope of 162 mg of proteins per gram of DCW (R 2 = 0AE91).

HPLC analysis for dithiolopyrrolone quantification

Dithiolopyrrolones were quantified by HPLC (Bio tek Instruments, Milan, Italy). The analytical column was ProntoSIL 120 5 C 18 SH, 150 • 4AE6 mm (Bishoff Chroma tography, Leonberg, Germany) fitted with a guard column of 10 • 4 mm, and detection was achieved with a diode array detector (UV vis 545 V; Bio tek instruments).

For detection and quantification of thiolutin and BEP produced in the culture medium, analyses were per formed as described by Bouras et al. (2006a) with an injection volume of 80 ll.

For detection and quantification of dithiolopyrrolones formed during enzymatic assay, analyses were performed under the following chromatographic conditions. Samples were analysed by linear gradient elution using a mixture of methanol ⁄ bidistilled water (solvent A ⁄ solvent B) as mobile phase and a flow rate of 0AE8 ml min 1 . Column temperature was maintained at 30°C and injection volume was 40 ll. UV detection of antibiotics was carried out at 390 nm. For the acetyltransferase assay, elution was carried out with a linear gradient from 30% A to 47% A in 17 min. Pyrrothine and thiolutin retention times (R t ) were 7 and 12AE3 min, respectively. For the benzoyltrans ferase assay, the mobile phase composition was 30% A, reached 45% A in 15 min, 100% A in 35 min and was finally kept at this value for 2 min. Pyrrothine and BEP retention times (R t ) were 8 and 29 min, respectively.

Both antibiotics were quantified using a thiolutin standard calibration curve. Indeed, the molar absorbtivity values at 390 nm of pyrrothine derivatives are close. e 390 is in the range of 8317 9333 mol 1 l cm 1 as described by Lamari et al. (2002b).

The BEP chemical structure was confirmed by compar ison of the UV spectrum obtained with those mentioned by Bouras et al. (2008). The UV spectrum of BEP yielded k max in nm (relative absorbance): 231 (1), 309 (0AE45), 401 (0AE68).

Replication of experiments and statistical analysis

Data of growth and dithiolopyrrolone production repre sent the average of triplicate flasks, and error bars denote standard errors. Values of acyltransferase specific activities